Two weeks later, she encounters AI again but this time while dealing with an insurance claim. When she asks to speak to a person, she’s redirected to another chatbot. What felt helpful while shopping now feels like a barrier.

Same customer, same technology, opposite experiences. In one moment, AI helps her make a decision. In the other, it stands between her and the help she wants.

Consumers are increasingly comfortable using AI on their own terms. But that comfort doesn’t automatically extend to brands using AI on their behalf. AI customer trust depends on whether customers understand how AI is being used, see value from it, and retain control over the experience.

Our research makes that tension clear. Forsta’s 2026 Retail consumer study found that among shoppers who used AI over the holiday season, 58% bought something because of its recommendation. Asked how far they trust retailers to use AI responsibly, only 22% said a lot or quite a bit.

Consumers are embracing AI faster than they’re granting brands permission to use it on their behalf.  The aforementioned customer’s disappointment has identifiable causes. Our research validates that earning AI customer trust requires giving people enough transparency, value, and control to feel confident about what your brand does with AI.

Consumers have embraced AI but on their own terms

AI has become part of everyday life, and Forsta’s internal research shows how routine: 43% of consumers use it for research and finding information, 34% for personal tasks, 25% for work, and 20% while shopping.

The behavior underneath those figures is consistent. One in three U.S. shoppers used AI over the recent holiday season, and among those who did, 60% came back to it repeatedly rather than trying it once, per Forsta’s Retail Consumer Study. Returning to a tool signals it earned a place in how someone shops.

What people use it for stays narrow. 87% turned to AI primarily for gift ideas rather than price or product comparisons, and fewer than one in ten completed a purchase through an AI platform in any category.

That openness shouldn’t be mistaken for unconditional enthusiasm. Younger consumers may be among AI’s most active adopters, but their skepticism is rising just as quickly. Bentley University-Gallup research found that 47% of adults aged 18 to 29 now believe AI does more harm than good, up 11 percentage points from 2025 and from just 30% in 2023.

That was the largest year-over-year increase of any age group. For brands, the takeaway is that familiarity with AI doesn’t automatically translate into trust. Younger consumers may be comfortable using the technology, but that experience can also make them more discerning about where, when, and how brands deploy it.

Consumer AI adoption is outpacing trust

Forsta’s 2025 State of CX report, drawn from 4,000 consumers across the U.S. and UK, found 48% of U.S. consumers and 45% of UK consumers open to AI-led customer experiences when it means faster service. Only one in five feel very comfortable dealing with AI on its own. 

Consumers don’t automatically trust brands to use AI responsibly, and the disparity between open to and comfortable with is where AI customer service earns a relationship or quietly costs one.

On personalization specifically, Forsta’s internal research finds consumers divided almost evenly in three ways: comfortable, uncomfortable, and undecided. The undecided third is the group worth designing for, because their position is still moving.

Comfort drops further as consequences rise. Close to half remain uncomfortable with AI-generated financial or insurance recommendations, where being wrong costs real money.

The published retail data shows the same caution among people already using the technology. Asked how far they trust retailers to use AI responsibly, 22% of AI users said a lot or quite a bit, and 39% said a little or not at all. The largest group, at 40%, landed on somewhat, which is a hedge rather than a verdict.

Underneath sits the data question. 69% of U.S. consumers will share personal information in exchange for a better experience, while 19% trust brands to handle that information responsibly. The UK figures track closely, at 64% and 17%.

Four questions shaping AI customer trust

Consumers are asking questions brands haven’t fully answered. Four come up repeatedly in the research, and each maps to a decision someone inside the business has already made without telling the customer.

How is my data being used?

Privacy ranked as the top concern among shoppers who used AI, though most of that concern sat in the moderate range rather than the extreme, which makes it addressable. What people want is visibility into how their information feeds the system. Forsta’s State of CX research found 43% of U.S. consumers trust a brand more when it discloses AI use openly, which is a low-cost benefit to give them.

How are decisions being made?

Accuracy and transparency each drew 18% of AI users describing themselves as very or extremely concerned. The issue underneath both is explainability. A recommendation that arrives without reasoning asks the customer to take it on faith, and faith is exactly what’s in short supply. Showing why a product surfaced often does more for confidence than improving the recommendation itself.

Who is accountable?

Customers hold the brand responsible, not the model. When an AI customer support interaction gives someone the wrong answer, nobody files it under vendor error. That exposure grows as AI speaks for the brand more often: nearly eight in ten AI users say they value AI recommendations as much as or more than advice from a retailer. Governance and oversight are what keep that influence from becoming liability.

Can I trust the outcome?

Bias drew the lowest concern scores of the four, which probably understates it, since people can only report bias they noticed. Some did. One respondent described AI that seemed “biased toward certain vendors and products.” Consumers need to believe a recommendation is accurate and serves them rather than the brand paying for placement.

Personalization requires a stronger value exchange

Consumers expect clear benefits before sharing their data, and the State of CX research shows they will trade when the return is legible. 69% of consumers say they will share personal information for a better experience. That willingness is conditional on the exchange being visible, which is where most programs fall down. The data goes in, the benefit stays vague, and the customer concludes the transaction ran one way.

What counts as a meaningful return is unglamorous but ultra-meaningful:

• Support that resolves faster because the agent already has the context
• Recommendations that reflect what someone actually bought instead of what the segment bought
• Loyalty rewards that arrive without being chased
• An experience that picks up where the last interaction left off
• Offers and communications that reflect what someone actually needs right now 

The research shows what happens when AI in customer experience misses that mark. Shoppers praised the technology for speed and convenience, then described missed context, irrelevant results, and generic suggestions as the main friction. Forsta’s own summary of the finding is that AI is winning on utility and lagging on relevance. Generic personalization is worse than none, because it proves the data was collected and reveals it wasn’t used.

Around 30% of consumers in both markets say they would consider switching brands for more personalized experiences, and nearly one in five already have. The value exchange isn’t a philosophical position. It’s a retention number.

AI customer trust and expectations vary by generation

One AI strategy won’t resonate with every customer, and the research splits cleanly enough to plan around. AI use in shopping skews younger, though the retail study notes adoption expanding across age groups rather than concentrating at one end.

The sharper divide is over data. The State of CX research found 49% of Gen Z willing to share personal information against 18% of Boomers, with the Gen Z willingness explicitly conditional on seeing value returned. The same research describes Boomers disengaging from digital-first retail altogether.

The obvious read is that younger customers are the easy audience. The data says otherwise. 71% of Gen Z and 68% of Millennials have walked away from a retail purchase over a poor experience. They adopt faster, share more, and leave sooner. Their openness is a rolling assessment rather than a settled preference, which means a brand earning it has to keep earning it.

Older customers hold the opposite position and are more consistent about it. Emphasis on security, preference for a person on complex or high-stakes issues, and more scepticism toward AI-driven recommendations. That’s a higher bar to clear once and a more durable relationship afterward.

Neither group wants the same AI, and neither is served well by the average of the two.

Human interaction remains essential

AI should enhance human experiences rather than replace them, and consumers are consistent about where the line falls. The State of CX research found that even in highly digitized sectors, many people actively seek human contact when resolving complex or high-stakes issues. Only one in five feel very comfortable dealing with AI on its own.

The pattern in the retail data points the same way. AI use concentrates in high-consideration categories where decisions are complex and confidence matters, and it stops short of the transaction. Among AI users, 5-8% completed an instant purchase through an AI platform in any category. People use AI to think through a decision and then take the decision themselves.

That instinct intensifies as stakes rise. A gift recommendation carries a low cost of being wrong. A mortgage application, a claims decision, a diagnosis, or a complaint that has already failed once carries a high one. Financial services, insurance, healthcare, and customer support are where the demand for a person is loudest, and where routing someone into an AI-only path does the most damage.

The practical version is unremarkable. Automate the volume. Make it easy to reach a human when the stakes are high. Make the handover to a person easy to find rather than buried behind three menu levels.

How brands can build trust in AI customer experiences

Building AI customer trustcomes down to three things: transparency, value, and control. Tell customers when AI is involved and how their data is being used. Make the benefit of sharing that data visible in the experience they receive. And give them meaningful control, including an easy route to a person when they want one.

Sixty-three percent of U.S. consumers will leave after one or two bad experiences, so responsible AI design is a standard applied to every interaction rather than a policy published once.

Consumers have already made AI part of how they make decisions. They use it, they trust its recommendations as much as a brand’s, and will keep doing so on their own terms. What they haven’t decided is whether to extend that confidence to the companies deploying it, and that question is still open in every sector represented in this research.

The organizations that earn customer trust in AI will be the ones that treat transparency, control, and access to a person as design requirements rather than as concessions. Start with disclosure, since it costs the least and moves trust the most.

Speak to an expert about building trust in your customer experience program today.

A services-only estimate tells one story. A broader view of the insights ecosystem tells another. Add software, analytics, reporting, automation and AI-enabled research into the picture, and the market starts to look very different. Growth is no longer measured by project volume or headcount alone: it’s measured by how much value research can create when insight is faster, more connected, and easier to use. 

Key takeaways

• Market research industry size depends on what’s being counted. Services-only agency revenue and broader software-inclusive estimates can tell very different stories, so every figure needs clear scope. 
• Across the main benchmarks, the industry is growing rather than declining. The growth rate varies by region and market definition, but the direction of travel is clear. 
• For agencies, AI creates an opportunity to scale output, improve responsiveness, and protect margins without turning research into a people-free function. 
• For in-house teams, market size data can help to reposition insights as a strategic growth capability, not just a cost center or project-by-project support function. 
• The bigger story is not just industry size, but industry need. Data quality pressures, faster decision cycles and rising expectations for accessible insight are increasing demand for platform-grade research infrastructure.

Why the market research industry size number is harder to pin down than you’d think

Pinning down industry size sounds simple. It isn’t because different analysts count different parts of the insights ecosystem.

Some estimates focus on market research services (the work delivered by agencies, consultancies, and research providers). Others include software, analytics platforms, automation tools, reporting systems, data integration, and adjacent insight services. Some measure the supplier side of the industry, while others try to capture the wider demand for research, data, and intelligence inside organizations.

That’s why two credible market size figures can look very different – and the problem isn’t just academic. It creates real challenges for anyone trying to use market size data in practice.

• For agency leaders, unclear definitions make it harder to benchmark agency growth against the wider market. 
• For agency teams, they can make client conversations feel less precise, especially when one source points to steady services growth and another suggests a much larger technology-enabled opportunity. 
• For internal research leads, unexplained variance can make it harder to present defensible numbers to leadership when building the case for investment.

Before citing any market size figure, the first question should always be: what is this source counting? Without that scope, a big number may look impressive without being especially useful.

Services-only vs. software-inclusive: why the figures diverge

1. A services-only estimate focuses on the work delivered by market research agencies, consultancies, and research providers – including survey design, fieldwork, analysis, reporting, strategic consulting, and other research services.
2. A software-inclusive estimate takes a wider view. It can include platforms, analytics tools, reporting systems, automation, text analytics, data integration, and other technologies that help organizations collect, manage, and activate insight.

Neither view is wrong. They’re simply drawing the boundary in a different place.

For agency leaders, a services-only estimate can be a more useful benchmark when comparing agency revenue, growth, and market share. It shows how the supplier side of the industry is performing and gives agencies a clearer way to understand their position in the market.

For in-house leads, a broader software-inclusive view may be more useful when making the case for platform investment internally. If leadership sees market research as nothing more than a series of individual projects, it’s harder to explain why the team needs better infrastructure, but when research is understood as a wider insight ecosystem, the case for platforms, automation, and integrated data becomes much stronger.

Agency revenue vs. department spend: two different markets, one confusing headline number

There’s another distinction worth making: agency revenue and research department spend aren’t the same thing.

• Agency revenue measures what market research agencies and providers earn from delivering research, insight, analytics, consulting, and related services. In other words, the supplier side of the market
• Research department spend captures how internal market research departments invest in research, technology, data, people, platforms, and external partners. Or, the buyer side of the market.

In plain English: one measures what agencies sell; the other measures what internal teams buy, build, or manage to support research across the business.

This matters because one headline number can blur several different things: who’s buying research, who’s selling it, and where platform spend sits.

The scope definitions that matter for agencies and internal teams

The most useful market size figure depends on the decision you’re trying to support, but for much of the industry, there are three scopes that matter: 

Global market size benchmarks for 2025 and 2026

Yes, the industry is big. The smarter answer is which market you’re sizing, and why that boundary matters. The clearest 2025 and 2026 global benchmark comes from the services side of the market. 

The Business Research Company’s 2026 report valued the global market research services market at $93.37 billion in 2025, rising to $96.77 billion in 2026. Regionally, North America remained the largest market in 2025, with Western Europe identified as the second-largest region. The MRS values the UK research insight and analytics industry at £9 billion ($12 billion).

With estimations of $160 billion at the end of 2025, a broader view of the global insights ecosystem is larger because it also includes software, analytics, reporting, automation, and digital data. That matters because the industry’s growth is no longer only about how many research projects are commissioned. It’s also about the infrastructure that helps insight move faster through the business.

The gap between those figures doesn’t mean one is right and the other is wrong. It means they’re measuring different things.

The US market: still the world’s largest at $37.7 billion

IBISWorld estimates the US market research and public opinion polling market at $37.7 billion in 2026, underlining the scale of demand in one of the industry’s most mature and commercially influential markets.

For agency leaders, that scale matters because the US often sets expectations that travel. Client demand for faster delivery, stronger data quality, better reporting, AI-enabled workflows and platform-led research operations can shape competitive standards well beyond the US market itself.

For in-house teams, the US figures support a different point. It shows that insights isn’t a niche support function. It operates at significant commercial scale in the world’s largest research market, giving internal teams a stronger basis for conversations about budget, headcount, platforms and long-term research infrastructure.

Europe and APAC: where regional growth is accelerating fastest

North America leads by scale, but Europe and APAC show why the industry’s future isn’t only being shaped by the largest market. Europe has depth, maturity, and established research expertise. APAC has momentum, expanding digital adoption, and rising demand from fast-growth markets.

The Business Research Company identifies Western Europe as the second-largest region for market research services in 2025. That’s important because it shows mature markets aren’t plateauing. They’re evolving as organizations invest in faster, more connected, and more technology-enabled ways to generate insight.

APAC’s momentum can be seen in markets such as India, where the Market Research Society of India reported that the country’s market research industry reached around $3.5 billion in FY2025, up 10.9% from the previous year. That kind of growth points to expanding demand in markets where consumer behavior, digital adoption and business competition are changing quickly.

Is the market research industry growing or declining?

No, the industry isn’t shrinking into irrelevance. It’s evolving, and the growth rate depends on which slice you measure.

The ‘decline’ question usually reflects two different anxieties:

1. For agency leaders, the concern is commoditization: whether automation, tighter budgets, and changing client expectations will put pressure on margins. 
2. For in-house teams, the concern is internal investment: whether insights teams can still justify headcount, platforms, and research budgets when businesses are under pressure to do more with less.

That means growth won’t only come from running more projects. It will come from scaling output, protecting margins, and helping clients get to insight faster.

CAGR projections through 2030: what the data shows

Forecasts vary because publishers model different market boundaries and time horizons.

For market research services, The Business Research Company projects growth from $96.77 billion in 2026 to $116.02 billion in 2030, representing a CAGR of 4.6%. That may not sound explosive, but in a category already worth almost $100 billion, steady single-digit growth represents a significant commercial opportunity. It supports continued investment in people, platforms, methodologies and delivery models, especially for teams that can scale without adding headcount at the same rate as revenue.

The structural forces pushing the market beyond $100 billion

Digitization has changed how quickly businesses expect feedback. Always-on research programs have made insight more continuous. Analytics demand has pushed research closer to business intelligence and decision support. AI is accelerating parts of the workflow, from data preparation to analysis and reporting. Together, these forces are expanding the market beyond project volume alone.

That matters commercially because growth is increasingly tied to insight delivery systems: the platforms, workflows, and governance that help research move through the business quickly and reliably. Agencies that can deliver that kind of infrastructure become more valuable to clients. Internal teams that invest in it can make a stronger case for research as a strategic capability, not a reporting function.

How AI is reshaping market research industry size and value

AI changes the size story because it changes the value story: faster insight creation can expand demand instead of replacing demand. That’s the important distinction. 

If AI only made research cheaper, the market story would be about cost reduction. But when AI helps teams analyze data faster, cover more feedback sources, update reports more easily and give stakeholders quicker access to insight, it changes where research can show up.

• For agency leaders, that creates a commercial opportunity. AI can help agencies increase output, improve responsiveness, and protect margins without turning research into a people-free function. The value doesn’t disappear from human expertise; it shifts toward better design, stronger interpretation, clearer storytelling, and more scalable delivery.

• For research departments, the same shift creates a stronger internal argument for research investment. AI doesn’t remove the need for insight teams – it simply raises the value of the teams that can combine automation with governance, context, and methodological judgement.

AI as a growth multiplier, not a replacement

Greenbook’s 2026 GRIT Insights Practice Report suggests AI is part of mainstream research operations. Across the industry, agentic AI use has converged around three core tasks: analyzing or modeling data, generating reports and dashboards, and preparing or integrating data. And these are not fringe activities. They sit close to the economic engine of research delivery: turning raw data into usable insight.

The strongest signal from GRIT is not simply that agencies are using AI; it’s that the firms using it with clearer operating models appear better positioned commercially. The report highlights service-led suppliers with 101 to 500 FTE as a standout segment, leading service-led revenue growth while also showing high AI governance maturity, including formal AI rules and strong confidence in AI risk minimization.

Agencies that use AI well can respond faster, scale analysis more effectively, support more complex client needs, and increase revenue without increasing headcount one-for-one. That doesn’t make researchers less important; it makes their judgement more valuable, because the work shifts from manual production toward interpretation, quality control, strategy, and actionability.

Our article on AI efficiency in market research takes a deeper look at the practical workflow implications.

What faster insight velocity means for total addressable market

When research takes weeks to move from data collection to actionable output, some business decisions move on without it. Teams rely on existing assumptions, partial data, or whatever answer is easiest to access in the moment.

AI and automation can change that equation.

With suites like Research HX speeding up the research workflow by 50%, the practical addressable market for insight increases because it brings research into moments where it may previously have been seen as too slow, too heavy, or too expensive to commission.

For operational detail on how AI is changing market research analysis, our article on AI-powered market research analysis explains more.

Why declining data quality is creating demand for platform-grade research infrastructure

GRIT points to a clear shift: trust and quality are becoming much more than operational concerns. Sample integrity, fraud detection, respondent authentication, data provenance, synthetic-data validation and AI audit trails are all becoming part of what clients need to feel confident in the insight they’re using.

According to Qualtrics’ 2026 Global Market Research Trends Report, the majority of research leaders have used synthetic data, and 68% of them would consider themselves experts. Used well, it can help teams explore ideas, model scenarios, and reduce pressure on audiences that are difficult or expensive to reach. But it also needs careful handling. If teams can’t explain how synthetic data was created, where it should be used, and where human judgement still needs to sit, it can create confidence without the evidence to support it.

That isn’t just a concern for regulated industries. Any agency or insights team making decisions from research needs to know the data is credible, the method is defensible, and the output can be trusted.

The growth opportunity isn’t AI on its own. It’s AI with the right infrastructure around it: connected data, governed workflows, quality controls, and human expertise. That’s what turns faster research into better research.

What the industry size data means for research agencies and internal teams

Big numbers only matter if they help you make better decisions. This section turns market size into practical strategy.

How agencies are scaling output without proportional headcount growth

For agencies, market growth is only useful if it can be served profitably. That’s why leading agencies are increasingly looking beyond headcount as the main route to scale. 

Automation, standardized workflows, integrated AI, stronger security, and better unstructured data analysis all help agencies increase output without increasing delivery costs at the same rate. Research-grade infrastructure changes that. It helps agencies to reuse workflows, accelerate analysis, connect data sources, improve reporting, and protect quality as volume increases.

For more on the operational side of this shift, our article on automation in market research shows how automation is changing research efficiency in practice. 

Why the platform you run research on is now a competitive variable

Platform choice now shapes speed, governance, and actionability. That makes it a strategic decision, rather than just a software decision.

Forrester’s 2026 Total Experience Score research makes a useful broader point: growth breaks when experiences fragment. The same logic applies to research. When insight is spread across disconnected tools, teams, and workflows, the business gets a fragmented view of what customers, employees, or markets are really saying. This slows delivery and makes it harder to scale consistently. 

A unified research platform helps prevent that. It gives teams a stronger way to manage data, workflows, security, analysis, and reporting in one connected environment. That means insight can move faster, with more consistency and more confidence.

The market research industry is bigger than the headline number suggests – and getting bigger

Broader ecosystem figures show something bigger: a research and insights industry increasingly shaped by software, analytics, automation, AI, and the infrastructure needed to turn data into decisions.

AI is widening the value of research, but infrastructure will decide who captures that value. The teams that can connect data, protect quality, speed up analysis, and make insight easier to use will be better placed to grow with the market, rather than simply keep up with it.

Forsta helps research teams and agencies build that kind of foundation: connected, governed and ready to scale. So as the market grows, your ability to act on insight can grow with it.

See how Forsta helps research agencies scale faster: Speak with an expert | Forsta

FAQs

How big is the global market research industry?

Most current estimates place the global market research industry in the tens of billions, and broader definitions push the total well past $100 billion. The gap usually comes from scope: some counts cover agency services only, while others include software, analytics, and adjacent insights work. If you’re building a business case, use both numbers and explain what each one includes.

Why do market research industry size estimates vary so much?

Not every source is measuring the same market. Some track agency revenue, some track broader research departments’ spend, and others bundle in platforms, analytics, and automation. That difference can make two credible headline numbers look miles apart.

Is the market research industry growing or declining?

The market research industry is not in broad decline, although some traditional methods are under pressure. Growth is shifting toward faster, digital, and AI-assisted approaches, which expands the value of both agencies and enterprise insights teams. In plain English: the work is changing, but demand for trusted insight is still rising.

Will AI replace market research analysts?

Probably not, and that’s the wrong question anyway. AI may help researchers analyze open-ended responses, summarize patterns, and speed up reporting, but human judgment still matters for design, context, quality, and actionability. The bigger opportunity is augmentation: strong platforms let teams do more without turning insight into a black box.

What does market research industry size mean for your business case?

The numbers tell a clear story: market research is not a niche function; it is core business infrastructure operating at a significant commercial scale. For agencies, industry-scale supports investment in automation and platform infrastructure without requiring proportional headcount growth to justify the investment. For research departments, the same data helps make the case to finance and marketing leadership that speed, governance, and data quality are now competitive variables, not optional upgrades.

Nowadays, a much tougher set of gatekeepers is judging every survey invitation: mailbox providers, corporate security tools, machine learning filters, and most importantly, the people receiving the email. Our latest webinar dived into exactly this, and in this article, we’re summarizing the top 10 tips to email deliverability success.

Treat deliverability as a moving target

The number one rule of email: what worked yesterday may not work today, and what works today may not work tomorrow. 

Mailbox providers and corporate security systems are constantly adapting. At the same time, so are spam tactics, and so is recipient behavior. Every email you send gives filtering systems more information about whether your messages are wanted, trusted, and worth placing in the inbox. That means email deliverability needs ongoing measurement, monitoring, and adjustment.

The practical takeaway: don’t assume past performance guarantees future placement. Watch how your audience responds, learn from the data, and keep refining.

Start with better sample management

90% of all email deliverability issues are caused by poor sample management. It’s easy to think that sending to more people will get you more responses. But that’s not the case. Sending to the wrong people can actually hurt more than it can help.

Poor sample quality. We’re talking aged contacts, invalid addresses, typos, non-engaged respondents, people who don’t remember opting in. All this can drive bounces, lower engagement, and damage sender reputation. Good sample management is about sending the right email, to the right person, at the right time.

SPF (Sender Policy Framework), DKIM (DomainKeys Identified Mail) and DMARC (Domain-based Message Authentication, Reporting, and Conformance) can all help to prove who you are, but your recipient list helps to determine whether mailbox providers believe that people actually want to hear from you.

Rethink links and tracking

Link tracking can be useful, but it can also create problems for email deliverability.

Mailbox providers no longer evaluate just the visible content of an email. Today, they assess the wider email ecosystem, including every domain, redirect, and link destination. Corporate gateways often go even further, actively checking, rewriting, or blocking links before they ever reach the recipient.

That’s a challenge for survey invitations, where tracking links can become long, complex, and difficult for people or security systems to trust. A link that looks suspicious, breaks after being rewritten, or routes through shared infrastructure can massively reduce confidence and engagement.

The cleaner option is often a direct, branded survey link, with performance measured through survey starts and completions rather than every possible click.

Protect your domain reputation

Think of your domain reputation as your sender credit score. If it’s strong, it gives mailbox providers more reason to trust your emails. If it’s poor, even technically correct infrastructure may not be enough to save inbox placement.

One common problem is mixing too many email types on one domain. Corporate communications, marketing campaigns, transactional emails and survey invitations can all carry different engagement patterns and risk profiles. When they’re all blended, one weak stream can affect the others.

Subdomains can help to separate sending activity, protect the root corporate domain, and create clearer reputation signals.

Get authentication right

Email authentication is no longer optional: it’s the entry ticket.

• SPF shows who’s authorized to send on behalf of a domain
• DKIM proves the message hasn’t been altered
• DMARC tells mailbox providers what to do if authentication fails
• rDNS shows that the mail server is legitimate

Together, they create a layered trust model. Without them, even genuine email can look suspicious, spoofed or risky. 

Authentication isn’t just a technical setup step. It’s part of reputation design; helping mailbox providers decide whether your survey invitation deserves to be treated as legitimate communication or potential abuse. And if you’re not fully provisioned with the right DNS records to be sending, you shouldn’t be.

Keep sending patterns consistent

Mailbox providers are increasingly pattern-watchers. They learn what normal sending looks like for your domain and IP, and they pay close attention to spikes in sending.

A sharp spike in volume or an irregular cadence can make a legitimate campaign look like a compromised account or spam burst. The result can be throttling, spam placement, blocking, or damage to your IP and domain reputation.

For market research teams, this can be especially tricky when fieldwork requires urgency. But predictable sending behavior builds trust over time.

Make opt-outs easy to find

Opt-outs are not the enemy. In fact, clean opt-outs go a long way towards protecting sender reputation.

Not everyone wants to be surveyed. Some people might not even remember agreeing to receive your well-intentioned research invitations. Others may simply no longer be interested. Giving them a clear, easy way to opt out lets them leave gracefully.

The alternative is much worse.

If recipients feel trapped, they’re more likely to ignore the message, delete it without reading, or mark it as spam. Mailbox providers interpret those behaviors as signs your emails are unwanted, which can hurt future email deliverability.

The opt-out doesn’t need to dominate the email, but it should be easy to find, easy to understand, and easy to use.

Don’t let campaigns go stale

If recipients see the same design, subject line, message and repeated structure, engagement can start to fade. Mailbox providers notice this and damage email deliverability.

They look at engagement patterns over time: opens, clicks, ignores, deletes, spam complaints, and a whole heap of other signals. If audience behavior suggests people no longer value your emails, inbox placement can begin to suffer too. And that creates a painful cycle.

Lower engagement weakens reputation. 

Weaker reputation reduces visibility. 

Lower visibility drives even poorer engagement.

The fix isn’t constant reinvention. It’s thoughtful refreshment: testing subject lines, reviewing cadence, updating copy, improving targeting, and keeping the invitation experience relevant.

Keep content clear, focused, and mobile-friendly

An incredibly common trap that lots of teams fall into is treating survey emails as web pages. Your emails don’t need six competing messages, three calls-to-action, long intro paragraphs, or heavy image blocks.

They need to be clear, trustworthy, and easy to act on.

A strong survey email usually has:

• One primary call-to-action
• A clear reason to participate
• Mobile-friendly design
• Thoughtful personalization 
• As little friction as possible

It should be readable, scannable, and properly built in HTML so it behaves reliably. Your email has one job: get the right person to the right survey – so keep it simple.

Build for 2026 and beyond

Every email you send is a new opportunity to build or lose trust, and the organizations that succeed are those that continuously measure, learn, and adapt.

AI, machine learning, and behavioral analytics don’t judge emails in isolation. They evaluate the whole sending ecosystem: authentication, infrastructure, links, domains, historical behavior, engagement patterns, security posture, and recipient value. That means every send is connected to the next one.

So actively protect sender reputation, send to recipients who are likely to find the message valuable, maintain consistent sending behavior, authenticate properly, and monitor performance continuously. In B2B research, that might also mean planning for corporate perimeter security.

Where Forsta can help

Email deliverability is technical, but it isn’t just an IT issue. It affects response rates, fieldwork efficiency, research quality, and the overall cost of getting the data your organization needs.

Forsta’s Email Template Analysis and process audit services help teams to understand what’s affecting email deliverability and where improvements can be made. Our subject matter experts ensure your emails follow best practices across template structure, sampling practices, domain use, engagement, recipient experience, compliance, and authentication.

With global emailing experience across hundreds of clients and billions of emails, Forsta helps market research teams to identify immediate, medium-term, and long-term actions to improve email deliverability, protect sender reputation, and support stronger survey response rates.

Inbox placement isn’t luck; it’s good practice, done consistently. To get started, reach out to your account manager. Not yet a customer? Speak with an expert here. And that remains a profoundly human capability, one that isn’t becoming easier to automate.

Now AI is changing expectations again, and something unexpected is happening.

Consumers are embracing AI in their personal lives while becoming increasingly cautious about brands using AI on their behalf. They’re willing to share data but only when they trust the company asking for it and clearly understand what’s in it for them. The brands best positioned for growth over the next decade will be those that earn it.

Loyalty has entered a new era

The old loyalty playbook ran on rewards: repeat purchases, points, retention, satisfaction scores. Deliver a good experience, hand out a perk, book the return visit.

That model still works, but it no longer explains why customers stay.

Today’s loyalty runs on something harder to buy: trust, transparency, consistency, confidence. Edelman’s brand trust report now ranks trust alongside price and quality as a reason people choose a brand at all — a factor that used to sit far below both.

Brand loyalty follows the same logic. Customers keep coming back to the brands they believe will do right by them, not the ones with the slickest points scheme.

The customer trust gap is becoming the loyalty gap

The gap has two sides. On one, customers want everything AI makes possible: personalization and automation, with convenience and speed. On the other, they don’t trust brands to use their data to deliver it.

That space between what customers want and what they’ll trust you to do is the trust gap, and AI is stretching it wider. The more a brand can do with a customer’s data, the more that customer wonders what’s being done with it.

The numbers show how wide it runs. Our own research shows that nearly six in ten consumers are uncomfortable with AI-driven personalization.. Half of U.S. consumers say they’d rather buy from brands that don’t use generative AI in the experiences they see, according to Gartner. People happily use AI themselves, then turn wary when a brand uses it on them. What they’re flagging is a control problem: whose hands the data is in, and to what end.

Left alone, the customer trust gap becomes a loyalty gap. Customers who don’t trust how you’ll use their data share less of it, and less data means thinner personalization and a flatter experience; one they’ve no reason to stay for, and nothing holding them when a competitor makes a sharper offer. The competitive line has moved from who personalizes best to who customers trust enough to let personalize at all.

The loyalty paradox

Line up what customers expect and it reads like a list no brand can satisfy. More personalization, but less data collection. More automation, but more human interaction. More convenience, but more control.

You don’t get to pick a side; they want it all, all at once, and all from you. The modern customer experience has to hold both ends of every pair: tailored yet restrained, automated yet personal, effortless yet transparent. That’s where a brand promise gets tested. Anyone can pledge personalization and privacy in a headline. Delivering both, at once, across every interaction is the promise customers actually judge you on.

Without an underlying foundation of trust, the same demands pull the experience apart: personalization curdles into surveillance, automation reads as a brush-off, and control becomes something the customer has to fight you for.

Why customers stop trusting brands

Every competitor in the category is talking about churn, but few talk about what comes before it. Customer trust rarely collapses in a single moment. It erodes through the experiences a brand never thinks to measure, and customer loyalty erodes with it.

No loyalty program offsets a customer who has stopped believing you’ll do right by them: points can buy a transaction, but not customer retention once trust is gone. Four things wear it down.

Small moments of friction

Trust erodes through friction so minor no one flags it. Repeating your account number to the third agent. A handoff that drops half the context. A personalization engine that greets you by the wrong name. Service that’s sharp on Monday and sloppy on Thursday. Each is forgettable alone. Stacked, they tell a customer the brand isn’t paying attention.

The data bears it out. Ask customers what drives them away and the top answer is a loss of trust (28%), ahead of multiple small inconveniences piling up over time (21%) and a single major failure (19%) (Forsta). Small friction does more quiet damage than the occasional disaster.

Catching it means connecting signals across calls, chats, surveys, reviews, and operational data to uncover the root causes of friction, which is exactly what a customer experience platform is built to do. AI-powered insight helps organizations identify patterns across these interactions, revealing the hidden sources of friction before they become larger loyalty problems.

Even strong customer service only recovers ground that friction keeps giving away. The real opportunity comes from closing the loop; using customer feedback to trigger action, resolve issues quickly and continuously improve the experiences that matter most.

Lack of transparency

Customers now ask three questions before they hand over data:

• Why do you need it?
• How will you use it?
• What do I get back?

When a brand can’t answer plainly, customer trust drops and silence reads as evasion. The brands that hold trust treat every data request as a small, honest exchange, clear on the ask and clear on the return, instead of hoping no one reads the fine print.

AI without explanation

People rarely reject AI on principle. What they reject is AI they can’t follow: a recommendation with no visible logic, or a decision they can’t question. Perhaps a chatbot that loops without ever reaching a person.

Unexplained automation feels like being handled, not helped. Explain how decisions are made, when AI is being used, and when customers can reach a person — why this offer, why now, and how to reach a human — and the same AI that eroded trust starts to build it.

Inconsistent human experiences

One outstanding employee and one indifferent interaction, and customers stop knowing which version of you they’ll get. That unpredictability is its own kind of distrust.

Consistency is a people problem before it’s a technology one: employee experience directly shapes customer experience, and a customer-centric culture is what keeps service steady when no script covers the moment.

Give your support team the context and the authority to act, and every interaction reinforces the same brand identity instead of chipping away at it. That’s where trust is won or lost, one human exchange at a time.

The value exchange brands keep getting wrong

Customers will hand over their data. They’re waiting for a fair trade and most brands are offering a bad one.

Watch how the ask lands. “Share your preferences” gives the customer nothing to weigh: all cost, no visible return. Flip it and name the payoff in the same breath: share this, and you’ll get faster support, fewer repeated questions, recommendations that fit, service shaped around how you buy.

Nothing about the data changed. The framing did, and the framing is the deal.

The wariness is real, and it’s specific. Most consumers are slightly wary about a financial provider using their personal data to personalize the experience. About a third are fine with it when they see clear value in return. Almost a third stay cautious, wanting control over how their data gets used.

People will trade data for something tangible and immediate, and they walk the moment the value goes vague.

Get the exchange right, consistently, and it stops reading as a data grab and starts feeling like service: a brand that remembers you to save effort, not to sell harder. Get it wrong, and the same request reads as extraction, however warm the copy.

Why personalization alone won’t create loyalty

Personalization is a multiplier, and on its own there’s nothing to multiply. The same recommendation reads as surveillance from a brand you don’t trust and as service from one you do — same data, same tactic, opposite feeling. Aim it better without earning trust and you don’t get loyalty; you get a sharper version of the thing already pushing customers away.

Human experience becomes the loyalty multiplier

The framing that pits AI against people gets the relationship backwards. What customers want is both: fast, frictionless digital service for the routine, and a real person the moment things get complicated or personal.

Forsta’s research points the same way: the more complex or emotionally charged the interaction, the more people want a human on the other end. That’s the idea behind Human Experience (HX): customer, employee, and brand experience treated as one connected system instead of three separate channels.

Used well, AI makes those human moments count for more.

When automation absorbs the password resets and order lookups, your people are free for the interactions that actually decide loyalty: the claim filed after an accident, the account that won’t reconcile, the complaint that’s really about trust. Give those moments to someone with the context and authority to fix things, and every one of them becomes a reason to stay.

The new trust equation

Trust reads as a feeling, which is why most brands treat it as one. But that reduces trust to something you hope to earn, not something you can manage. Break it into parts and it turns into a system you can actually build against:

Trust = Transparency + Reliability + Consistency + Humanity

• Transparency means customers understand what you’re doing with their data and why, with no fine print doing the real talking.
• Reliability means the experience works: the app loads, the handoff holds, the promise is kept.
• Consistency means every interaction reinforces the last, so customers always know which version of you they’ll get.
• Humanity means people feel understood, not processed, especially when something goes wrong.

Miss one and the whole thing wobbles. A transparent brand that can’t deliver loses trust as fast as a competent one that feels cold. The value of the equation is that it makes trust diagnosable: when confidence slips, you can name which term broke instead of guessing.

Score yourself honestly on all four, and you know exactly where the next fix goes.

Five questions every CX leader should ask

A framework only earns its keep if it changes what you measure. So point the trust equation back at your own program. Take these five into your next leadership review and watch which ones draw a confident answer and which draw a pause:

1. Can customers explain why they trust us?
2. Would customers willingly share more of their data with us?
3. Are we earning trust faster than expectations are rising?
4. Does every AI interaction strengthen confidence?
5. Can employees deliver experiences AI can’t?

The pauses are the map. Every question you can’t answer cleanly is a place trust is leaking and loyalty is following it out. Pick the weakest answer and fix that first.

That’s where AI-powered insight becomes most valuable, not simply identifying where trust is breaking down but helping organizations prioritize the actions that will have the greatest impact on customers, employees, and the business.

Turning trust into growth

Stop pitching trust as a way to “improve loyalty” and start framing it as a growth engine, because that’s what it becomes the moment it compounds. Here’s the chain. ‘

Trust earns permission to collect data. More data sharpens your insight. Sharper insight builds better personalization. Better personalization creates better experiences. Better experiences deepen loyalty.

And loyalty raises lifetime value, which funds the next round of better experiences and starts the chain over, stronger each time.

The trust-to-growth flywheel

That chain isn’t a funnel, and the difference matters. A funnel is linear and one-way: pour prospects in the top, convert a few, then refill it next quarter from scratch.

A flywheel keeps its momentum. Every turn makes the next one easier, because the trust you earned last quarter is still working for you this one.

The future belongs to trusted brands

For years, a better experience was the edge. Now expectations climb faster than any experience can keep pace with, and AI only accelerates the climb. Customer journeys will keep getting reshaped. Personalization will stop being a differentiator and start being the baseline. Automation will fade into the background until customers barely notice it running.

When all of that is table stakes, the algorithm stops being the thing that sets you apart. The brands customers remember will be the ones they trust enough to share their data, believe their recommendations, and come back to again and again.

Because in the age of AI, trust isn’t the outcome of loyalty. It’s what makes loyalty possible.

Forsta helps organizations measure, understand and improve the experiences that build customer trust. Explore how AI-powered insights and Human Experience (HX) solutions can help you strengthen customer loyalty and drive better business outcomes.

Almost everyone is using it (well, 62% of research teams last year, which, compared to 39% the year before, is a pretty hefty shift). Certain tasks that once took days now take minutes. Survey drafts, summaries, even initial insight narratives can be generated almost instantly. For agencies and in-house teams alike, the barriers to producing stakeholder-ready research have dropped dramatically, and a potential economic shift is on the horizon.

Leaders are often asking:

• Can we deliver projects with fewer hours?
• Can we improve margins without compromising quality?
• Can we increase throughput without increasing headcount?
• Can we compete when clients can access many of the same AI capabilities themselves?

Let’s explore.

Why human context is the real future of market research

As Harvard Business Review recently observed, when every company can use the same AI models, context becomes a competitive advantage:

“In procurement, systems of record capture purchase orders and approvals but not how exceptions are negotiated or supplier risk is interpreted. In customer service, ticketing systems log resolution codes but not the coordination patterns that prevent escalation. In finance, ERP systems record transactions but not the judgment behind credit decisions or capital allocation trade-offs. Across functions, systems of record capture outcomes. They rarely capture how execution unfolded.

Context becomes competitive advantage when it is coherent, aligned with strategy, and reinforced through daily action.”

And when it comes to market research, that context is in the grounding of human judgement, professional experience, and decades of unrecorded processes. The goal isn’t preserving manual work. The goal is automating low-value work so humans can spend more time creating high-value insight.

No agency wins because its researchers spend hours formatting PowerPoint decks, manually merging datasets, or building repetitive charts. The agencies creating competitive advantage are using AI and automation to remove operational friction while concentrating human expertise where it matters most: interpretation, commercial guidance, and decision support.

People are how you avoid a race to the bottom.

The race to ‘good enough’

There’s a useful analogy here.

When car manufacturing became standardized, it didn’t eliminate competition. Once reliability became expected, buyers differentiated on safety, service, design, and brand.

Research is going through something remarkably similar. If AI makes it easier to produce structured surveys, automated analyses, and polished slides, those elements become little more than baseline expectations. They’re necessary, but not distinctive. Speed, efficiency, and even basic competence become table stakes.

The market research differentiators move up the value chain, creating opportunities to protect margins and command premium pricing even as automation drives down the cost of execution.

More data doesn’t mean more insight

AI increasing volume doesn’t automatically increase clarity. In fact, many organizations are already experiencing the opposite. As AI accelerates output, they face an abundance of summaries, dashboards, and reports. What becomes scarce isn’t information – it’s prioritization.

When information becomes abundant, judgment becomes a premium skill. Teams that interpret, contextualize, and connect insights to decisions will be better positioned to protect revenue, retain clients, and avoid competing purely on cost.

Market research differentiators: from execution to interpretation

The agencies most likely to thrive won’t necessarily be the ones with the most AI tools. They’ll be the ones combining automation, expertise, and operational efficiency more effectively than competitors in ways that go beyond just speeding up.

Five capabilities are emerging as key market research differentiators:

1. Methodological rigor

Methodological depth matters. Success in this area goes beyond knowing how to run a study to understanding when something like synthetic augmentation adds value or when it starts to introduce risk. Sampling decisions, bias, and validity don’t disappear because a tool can automate parts of the process.

2. Better integration

Survey data alone is rarely enough. Agencies that combine survey research with behavioral data, customer feedback, social listening, operational metrics, and other signals can deliver a more complete picture of customer behavior.

3. Commercial relevance

Research must answer more than “what happened?”

It must answer:

• What should the client do?
• What is the commercial impact?
• What decision changes as a result?

4. Scalable operations

Margin pressure isn’t going away.

Agencies that streamline workflows, consolidate platforms, automate repetitive tasks, and reduce operational complexity create more room for growth and profitability.

5. Trust and expertise

As AI-generated outputs become commonplace, accountability becomes more valuable.

Clients still want experts who can explain findings, challenge assumptions, and stand behind recommendations.

Trust becomes premium

Initial excitement about automation is evolving and will continue to shift toward more deliberate conversations about supervision, transparency, and validation. And in that environment, trust becomes the differentiator.

• Who can stakeholders rely on when outputs are automated?
• Who can explain how insights were generated?
• Who can confidently stand behind a recommendation?

Trust isn’t built by speed alone. It’s built by clarity, transparency, and consistent quality.

The role of platforms in protecting differentiation

Infrastructure matters, and maintaining quality at scale depends increasingly on the systems teams build around it. When research workflows are designed with governance and transparency in mind, teams can move quickly without losing confidence in the outcome.

That’s where solutions like Forsta’s Research HX excel. Combining AI acceleration with structured governance, connected workflows, and a human-led model of insight creation. The focus isn’t just on generating output, but on making sure that insights are actionable, defensible, and aligned to business outcomes.

In an AI-saturated market, access to technology won’t be the differentiator. The new market research differentiators will be the ability to turn technology into commercial advantage through deeper, newer and more contextually aware offerings. And that remains a profoundly human capability, one that isn’t becoming easier to automate.

Discover more about what it means to use AI and agentic to enhance your workflow by exploring our solutions.

And this is a general estimate; for market research, the numbers look even more extraordinary.  

In this article, I want to share my personal take on operational excellence in the era of agentic AI, especially as it relates to market research teams. At Forsta, we’ve been blending powerful technology with human expertise, and I’ve seen firsthand how a confident, candid approach can help research organizations work smarter without losing their human touch. Let’s talk about what that looks like in practice – how AI can augment our processes; how it can streamline workflows by up to 50%; and why the future of insights will still depend on human judgment, storytelling, and empathy. 

AI as an augmentation

One thing I’m adamant about is helping clients improve without overhauling everything they do. We just don’t need to. The goal isn’t to rip up or replace well-honed methods, but to augment them where it makes sense. Think of AI as an accelerator and assistant, not an existential threat. In fact, industry experts agree: McKinsey’s research on operational excellence finds that the highest-performing organizations use technology to “augment human capabilities, rather than simply replacing humans with machines”. 

AI can take on the repetitive, time-consuming tasks; it can clean your data overnight. This doesn’t make human researchers obsolete. I love how a recent World Economic Forum piece put it: AI doesn’t remove humans; it removes friction. It cuts out the drudgery that prevents humans from doing their best work. 

Crucially, this means you don’t need to discard your existing processes overnight. We can plug in AI to streamline operations. Your core expertise, methodological rigor, and knowledge remain intact. For example, if your team has a solid survey design process, an AI assistant can take a questionnaire draft and script it into a survey platform in seconds, preserving your question flow but sparing you the laborious coding. In my experience, this augmentative approach lowers the fear factor significantly.  

And incremental change is often the smartest path. Organizations, especially large ones, evolve incrementally even as technology leaps exponentially. Instead, we pilot, test, and iterate, giving our teams time to relearn and build trust in AI outputs. This risk-minimizing transformation makes adoption more sustainable. People get to see AI as a helpful colleague rather than a disruptor. 

Transforming with AI

First, sticking with wider operational excellence practices, it helps to ground the transformation in some data and reality. We know the long-term potential of AI is huge. McKinsey pegs the opportunity at up to $4.4 trillion in added value for companies globally. For research, meeting that sweet spot is a measured approach: set ambitious goals for efficiency and quality, but break the journey into safe, pilot-sized steps. For instance, start by automating one piece of the workflow (maybe try Word Importer on a small project, Research Agent to refine slides for a low-pressure presentation) and measure the impact. In parallel, invest in governance and upskilling. Deloitte’s research emphasizes that many companies hit barriers because they lack clear governance models for AI and haven’t adjusted operating processes to support it. So, establishing guidelines (for quality checks, for data privacy, for when human review is required) reduces the risk of using AI. It sets guardrails so people trust the new tools. 

Also key is simplifying the message and focusing on the bigger picture. I’ve found that when introducing AI changes, they’re most enthusiastically received when we frame it in terms of story and purpose: What is the story we’re trying to tell with these improvements? Usually it’s something like, “Imagine if we could deliver client results in half the time, with higher confidence – what would that do for our business?”  When people see the big-picture narrative – faster insights, happier clients, more room to innovate – the tools become just a means to that end. As research leaders, we must be storytellers to drive change and meet ever-evolving operational excellence needs. Simplify the message: AI is here to take the boring stuff off our plate so we can shine in the interesting stuff. Period. That clarity helps everyone see why we’re doing this, not just how, and it creates a shared vision that makes adoption a mission, not a mandate. 

From data to story: Keeping the human touch

Let’s talk about what truly excites me: the storytelling and insight that humans – and only humans – can deliver. We often say internally that freeing up time with automation is only half the equation. The other half is, what do you do with that time saved? This is where operational excellence transcends efficiency and becomes about elevation of work. 

When AI slices out 50% of your production timeline, it forces a new question: if the late nights of data prep are gone, how will you redeploy that energy? I put this challenge to my teams and our clients’ teams alike. The answer we keep coming back to is: focus on the story, focus on the why. In a world where AI can churn out decent-looking charts or basic analysis in seconds, the differentiator becomes the human narrative and interpretation. We need to spend our reclaimed hours connecting the dots, finding the “so what” in the data, and crafting the story that will resonate in the boardroom or with the end client. 

Read more: Transform data visualization: Speak the language of leadership

In fact, at Forsta we explicitly designed our Research HX platform to help researchers focus on strategic storytelling and delivering deeper insights, rather than getting stuck in operational weeds. As I noted during our launch, the goal is to remove the roadblocks of outdated systems so teams can “focus on strategic storytelling, deliver deeper insights, and create tangible value”. That means using the time and mental bandwidth AI gives us to elevate the research deliverable – turning findings into a narrative that decision-makers can act on. It’s the difference between handing over a data dump versus telling an insights-driven story that inspires action. The latter requires empathy, context, and creative thinking – qualities uniquely human. 

There’s also a bigger-picture benefit here: differentiation. If every firm has access to the same AI analysis tools, reports could start to look eerily similar. (Large language models, after all, tend to produce the same “plausible prose” based on common training data.) This is the challenge of automation: you get efficiency, but you risk losing originality. The part where you infuse the findings with meaning, prioritize what matters for that particular client, and add the creative “spark” is where human researchers make the difference. It’s our job to ensure that efficiency doesn’t come at the cost of a bland, one-size-fits-all output. So operational excellence must include excellence in storytelling. We’ve got to see the forest for the trees and help our stakeholders see it too. 

Beyond automation: Elevating your team (and yourself)

Now, let’s circle back to that tantalizing notion of extra time and what we do with it. If operational excellence in the AI era gives you breathing room, use it to invest in your people and your own growth.

Read more: The future of insights leadership

For teams, this means reskilling, coaching, and thought leadership. The tasks that AI automates are often the entry-level, mechanical tasks that junior researchers used to cut their teeth on. So how do new researchers learn and grow when AI handles, say, the first pass of data cleaning or chart-making? The answer is we train them differently. We elevate their starting point. Instead of spending weeks on drudgery, entry-level team members can spend more time interpreting data, learning to ask the right business questions, or honing their storytelling skills. We need to coach teams to elevate their game – teaching things like how to critically evaluate an AI-generated insight, how to cross-examine multiple data sources, how to inject human empathy into an analysis. These are higher-order skills, and developing them early is a huge win for the next generation of researchers. 

From a leadership perspective, I also ask: what new questions should we be asking now that AI is picking up the slack? One big question is how to ensure human judgment and empathy stay front and center. Our clients entrust us with understanding human behaviors and emotions – that doesn’t change in the AI era. In fact, it becomes even more important. The Forsta ebook “Human Experience in the AI Era” makes a compelling point: as AI gets more powerful, the premium on uniquely human traits like empathy, imagination, and ethics only increases.  

At the end of the day, operational excellence isn’t just about efficiency metrics – it’s about building a smarter, more resilient, more human-centric operation. That includes technology and people in harmony. Yes, we streamline processes and cut out waste (who doesn’t love a 50% time savings?), but we also invest that dividend back into our teams’ growth and our clients’ success. 

Embracing augmented excellence

If there’s one takeaway I want to leave you with, it’s this: the future of AI in research is bright and it’s fundamentally human. We have an unprecedented opportunity to redesign how our teams work. By letting AI handle the rote and routine, we unlock time and creative headspace for the work that truly differentiates us. That’s the work that fuels innovation, strengthens client relationships, and drives growth. 

At Forsta, we’re excited (and frankly, honored) to be on this journey with our clients as a forward-thinking partner. We’re blending powerful technology with human expertise because we believe that’s the winning formula. The early results are validating, but what’s truly gratifying is seeing researchers light up at what they can do now. When a team realizes they can deliver a project in days instead of weeks or finds themselves brainstorming insights instead of battling Excel at midnight, it’s like a whole new world opens up. 

If you haven’t started, start small but start now. Pilot an AI tool on one of your processes. Encourage your team to experiment. And remind everyone (including yourself) that the goal isn’t to work faster for faster’s sake.  It’s to work smarter and create space for what truly matters. 

Discover more about what it means to use AI and agentic to enhance your workflow by exploring our solutions.

The issue isn’t visibility. The signals are there. The feedback exists in reviews, surveys, contact center conversations, and employee comments. The problem is that the people who can fix it often don’t see it until it’s too late.

The regional manager won’t see it until next month’s report, the store manager won’t hear about it until corporate flags it, and by then the weekend rush has come and gone. So have several hundred customers who walked into the same friction and didn’t bother to write it up.

This is the gap most experience programs are stuck in. Organizations understand more about their customers than ever before, but most still can’t turn it into outcomes in time to matter. Closing that gap is what separates the programs that drive business outcomes from the ones that just report on them.

 AI is changing what’s possible, turning experience signals into recommendations and helping frontline employees respond in real time.

From insight to outcome

Customers don’t wait for the next reporting cycle. Research in our 2025 State of CX report found that 59% of consumers expect brands to respond within 24 hours, and 67% expect some kind of follow-up after an interaction. A complaint posted Friday morning carries an expiration date. So does the goodwill of the customer who posted it.

Meanwhile, the people best positioned to fix the problem rarely have what they need to do it. Frontline employees field competing priorities, work from incomplete context, and often lack the authority to make the call without escalation. By the time guidance reaches them, the customer has moved on, somewhere further along the customer journey where the friction has already done its damage.

The traditional reporting model wasn’t built for this. Feedback gets collected, analyzed, packaged into a slide, presented in a meeting, debated, eventually assigned, and finally acted on. Each step adds days. Some add weeks. The signal arrives at the operational team long after the moment that produced it.

AI allows organizations to move from insight to outcome in time to matter.

Organizations that do this well are connecting customer, employee, and operational signals into a shared understanding of what’s happening and what needs to happen next. That’s the foundation of Human Experience (HX): understanding experiences across people, not just touchpoints.

Modern HX platforms combine listening, real-time analytics, and AI-driven recommendations to help organizations respond while experiences are still unfolding. The shift is from generating insight to delivering outcomes.

Why experience data isn’t enough

Most experience programs can answer three questions well:

• What happened?
• Where did it happen?
• How often did it happen?

The dashboards are good. The reporting is detailed. The data flows.

What they struggle with is the next set: who should act, what they should do, and how fast they can respond. Those questions don’t get answered by another report.

McKinsey research found that companies operationalizing customer experience grow revenue at roughly twice the rate of competitors that don’t. Yet only 15% of companies routinely use customer insight to steer decisions, and just 23% follow up to confirm they’re delivering value. 

The reasons are familiar to anyone running a multi-location experience program:

• Customer and employee feedback live in separate systems, owned by different teams
• Reporting cycles are too slow to catch issues while they’re still small
• Information overload buries the signals that matter under everything else
• No one owns the follow-through, so issues get acknowledged but not resolved
• Frontline teams are already juggling more than they can handle

In turn, experience programs stall in the same place. Insight is plentiful. Execution lags. The gap between what the program sees and what the operation does is where customer loyalty gets lost. As we explored in our article titled Operationalizing CX, the harder question for most organizations isn’t whether they have enough data. The harder question is whether they’ve built systems that turn that data into something the people closest to the customer can actually use.

How AI enables frontline teams to take action

Field enablement isn’t just training. It isn’t workforce management either. Those are pieces of it, but they describe an organizing function, not what frontline employees actually need in the moment.

Modern field enablement is the ability to put four things in an employee’s hands at exactly the moment they can act on them:

• Relevant context. What’s happening, where, and why it matters to this specific employee right now
• Recommended actions. A starting point for what to do, not just a signal that something’s off
• Prioritized issues. A short, ranked list, not an inbox of equal-weight alerts
• Continuous feedback. Whether the action worked and what to adjust

When that system is wired into how teams operate, daily work changes across industries:

Retail

A store manager gets a Friday morning alert that service scores have softened across three weekend shifts running, with a suggested staffing adjustment for the rush ahead.

Healthcare

A unit leader sees emerging patient concerns in feedback signals before they become systemic, with enough context to investigate the cause.

Hospitality

A property manager opens her tablet to automated summaries of recurring guest issues from the past week, sorted by impact.

Financial services

A branch manager identifies service friction in new account opening before it shows up as customer attrition.

In every case, the employee isn’t being asked to comb through hundreds of comments or interpret a dashboard. AI-powered summaries and alerts handle the volume, sorting thousands of signals into the few that need attention now. The person does the judging and the acting. That division of labor is what makes field enablement work at scale.

From dashboards to recommendations

The old model was a pipeline: collect, analyze, report, discuss, act. Each step had its own cycle time. Add them up and the lag was measured in weeks.

The AI-driven model compresses the middle: collect, analyze, recommend, act. The recommendation lands close to the moment, and it lands with the person who can do something about it.

This is a meaningful shift, and it’s the one most experience programs haven’t fully made. More dashboards aren’t the answer. Better decision support is. AI earns its place in an experience program through pattern detection and prioritization at a scale humans can’t match.

Modern text analytics tools, built on machine learning and sentiment analysis, can process thousands of survey verbatims, chat transcripts, reviews, and support notes in the time it would take a team to skim a fraction of them. The output is real-time insights an operator can actually use.

What lands on the operator’s screen looks different too. Instead of another chart, they get a shortlist:

• The emerging themes worth attention
• The anomalies that don’t fit the usual pattern
• The risks ranked by likely impact
• The root causes behind recurring problems
• The next-best actions, with the reasoning attached

That’s the move from reports to recommendations. Done well, it’s also the move from analyst-driven programs to operator-driven ones. The analyst still matters. Judgment, context, and advocacy are still human work. But the bottleneck of manual sorting goes away.

Solutions like Forsta AI are built to handle this layer of the work: scanning thousands of comments across surveys, transcripts, and reviews, surfacing what matters, and routing it to the people who can act. As we wrote in Closing the customer insight-to-action gap with AI, let the technology handle the sorting and keep people on the steering wheel.

Four ways AI turns insight into action

AI-powered experience management helps organizations close the gap between insight and action. The shift from dashboards to recommendations plays out in four practical ways once it’s wired into how experience programs operate.

1. Spotting emerging issues earlier

Predictive analytics applied to real-time data can detect that service complaints are rising at a specific cluster of locations before that cluster shows up in the monthly rollup. It can flag employee burnout signals trending in pulse data, or product issues appearing across reviews, calls, and chat at the same time. Early visibility is what makes early intervention possible. Catching a pattern in week one prevents the problem from spreading to weeks two through six.

2. Prioritizing what actually needs attention

Not every signal is urgent. AI helps separate minor concerns from operational risks, loyalty threats, and compliance issues, so teams can put their attention where impact is greatest. Without that triage, the alert volume becomes its own problem and the urgent gets buried with the routine.

3. Delivering guidance, not just data

There’s a meaningful difference between “customer satisfaction declined this month” and “wait times jumped 18% during peak periods at locations A, B, and C — consider adjusting weekend staffing.” The first is a finding. The second is a starting point. AI-driven guidance reduces decision friction by translating signals into possible moves, with the data the person needs to evaluate them.

4. Closing the loop

Real-time alerts are only half the job. The other half is making sure feedback actually results in measurable improvement. AI can route issues to the right owner — store manager, regional leader, operations team, HR — based on the type of issue, the location, and the severity. Once assigned, the system tracks whether the follow-up happened, what was done, and what the outcome was.

Tools like Forsta’s action management capability are designed to do exactly this: monitor signals across the program, assign cases by configurable rules, send alerts to mobile devices so on-the-go staff can act in the moment, and enforce conditions that must be met before a case can be marked closed.

The goal goes beyond tracking activity. The goal is making sure feedback ends in something the customer can feel — a fix, a response, a change in how the next interaction goes.

These four shifts together change what an experience program is for. It stops being a measurement function reporting on what already happened and starts being an operational function shaping what happens next, connected to the people, the workflows, and the decisions that actually move the business.

How leading organizations operationalize experience insights

The teams pulling this off share a handful of habits that distinguish them from the programs still stuck in quarterly readouts.

They connect experience data across channels

Surveys, reviews, contact center transcripts, employee feedback, and operational data sit in one place and inform each other. A spike in service complaints gets correlated with a drop in employee engagement at the same locations and a staffing change two weeks prior. The full picture lives in one view, not five.

They measure programs by actionability, not activity

Response rates, resolution times, and the experience improvements that follow are the success metrics. Survey completion rates are a means, not an end. Gartner found that 85% of customer service leaders are already exploring or piloting customer-facing generative AI, a clear signal that the operational bar is shifting toward acting on insight, not just collecting it.

They deliver insight where the work happens

The store manager sees what they need on their phone before the weekend rush. The regional director sees portfolio-level patterns in their morning briefing. The contact center supervisor sees real-time alerts as conversations unfold. Insights don’t live in an analyst’s report. They live in the workflows of the people making decisions.

They measure impact in business terms

Faster issue resolution. Higher employee engagement. Stronger user experiences. Customer loyalty and customer success outcomes that compound over time. Revenue retention. The point of an experience program is changing what those numbers look like a year from now, not generating more accurate descriptions of what they look like today.

None of this requires a heroic transformation. It requires building the connective tissue between what the program sees and what the operation does. That’s where the competitive advantage compounds: the teams that learn to act faster also learn faster, and the gap widens.

The last mile matters most

The hard part of experience management was never collecting feedback. Customers, employees, and operations have been telling organizations what they think for years, in surveys, reviews, calls, comments, and conversations. The hard part is the last mile: getting the right insight, with the right context and the right suggested action, in front of the right person at the moment it matters.

That’s the gap AI is finally helping experience programs close. Not by replacing human judgment, but by removing the friction that kept frontline employees from acting on what the program already knew.

The data was always there. What was missing was the system to turn it into something usable in the moment: alerts a store manager can act on before the weekend, summaries a property manager can read between check-ins, recommendations a branch leader can take into a 1:1 with their team.

For experience leaders, the question to take into the year ahead has changed shape. The work is no longer about collecting more feedback. The work is making sure the feedback already being collected reaches the people who can do something about it, fast enough to matter.

Real-time action has stopped being a luxury for organizations operating at scale. It’s becoming the baseline expectation for customers, for employees, and for the business outcomes experience programs are measured against.

See how Forsta helps organizations activate experience data. Speak with one of our experts about closing your real-time experience gap.

But despite all the innovation, one thing hasn’t changed: the need for human empathy.

This blog explores the increasingly critical balance between artificial intelligence and emotional intelligence in data visualization. Because while machines can speed up delivery, only people can deliver meaning.

Where AI fits in

AI has unlocked enormous value in the world of data storytelling. It can handle vast datasets, spot trends faster than any human, and eliminate hours of manual chart-making. For insight teams under pressure to deliver more with less, it’s a game-changer.

Some of AI’s greatest strengths include:

• Pattern detection: AI can surface interesting correlations, anomalies, and outliers that would take humans hours (or days) to find manually
• Automating the repetitive stuff: From chart creation to labeling and tagging, AI handles the boring bits so humans can focus on strategy
• Smart suggestions: Many tools now recommend charts, layouts, or visualizations based on the type of data being used
• Scalable personalization: AI can help tailor dashboard content to different users, learning preferences, and behaviors over time

These strengths are particularly powerful when paired with real-time data environments or high-volume datasets – areas where automation is not just helpful, but essential. In these scenarios, AI acts as an accelerant, driving efficiency and freeing up human capacity.

Find out more: Sign up for our latest demo, Meet your research agents, on Insight Platforms.

The limits of AI in storytelling

But for all that AI can do, there are meaningful gaps it simply can’t bridge.

• It lacks human context: AI can help you get closer to the ‘so what’ of the data provided, but it doesn’t understand the wider business context that humans will be immersed in
• It can’t sense tone or timing: AI can’t know when an insight is potentially sensitive, or when a team might not be ready to hear a tough truth
• It can’t detect bias: AI systems are only as unbiased as the data they’re provided. Without careful design, dashboards can end up replicating – or even amplifying – biases in the data that may not be relevant
• It misses the emotional resonance: AI can pick out the most important bits of information but can’t craft a narrative that will capture the emotions of those consuming the data

In short, AI is brilliant at the mechanics of storytelling – but it struggles with the next level of meaning.

Empathy is the human advantage

And that’s where we come in.

Human-centered dashboards are built with curiosity, lived experience, and ethical awareness. They’re shaped by the kinds of questions only people ask:

• What does this data really mean?
• Why should anyone care about this?
• What action do we want to inspire?

Empathy is more than just a soft skill. It helps us understand how different people will interpret the same chart, choose colors or language that feel accessible and respectful, and even sense when an insight could cause confusion, fear, or resistance.

In our Art & Science of Data Visualization ebook, we talk about how the best dashboards create emotional connections. That’s not something you can automate. It’s something you design for – with intent, compassion, and a deep understanding of your audience.

Read more: Art & Science of Data Visualization

AI and empathy = the dream team

The good news? This doesn’t have to be an either/or situation. In fact, it absolutely shouldn’t be.

AI and empathy aren’t rivals. They’re collaborators. And when you join the speed of AI and empathy built from years of human experience, your dashboards become exponentially more powerful. As our ebook, The Art and Science of Data Visualization, puts it:

“Machines will accelerate analysis, automate repetitive tasks, and surface patterns, but humans will continue to shape the narrative, provide context, and ensure that insights remain trustworthy, relevant, and empathetic.”

Here’s what that looks like in practice:

• AI finds the pattern; human decides whether it’s relevant, useful, or actionable
• AI recommends the layout; human adjusts based on stakeholder knowledge and emotional tone
• AI and automation translates data to visuals; human checks for bias, clarity, and resonance

It’s a bit like cooking with a sous-chef: AI can prep the ingredients, but you still need a human to taste, season, and plate the final dish.

• Read more: Incredible dashboard design principles that make data land
• Read more: Transform data visualization: Speak the language of leadership
• Read more: Boost data impact: Simplifying your visualizations

Keep the human in the loop

As AI gets more advanced, it’s easy to be dazzled by what it can do. But the best insight stories – the ones that shift strategy, spark ideas, and bring people together – are still shaped by people who understand people.

So yes, lean into AI. Use it to do the heavy lifting. Let it surface the interesting stuff. But don’t forget the empathy, context, and storytelling magic that only humans can bring.

Because in the end, data doesn’t drive decisions. People do.

Discover more about what it means to use AI for visualizations by exploring our solutions.

Customers can tell. According to Rio SEO’s 2025 Local Search Consumer Behavior Study, 67% of consumers say it’s important for brands to follow up about their experience, and 59% expect a response within 24 hours when they reach out.

The appetite for engagement is there. But most programs aren’t built to meet it at speed.

The organizations that break the pattern pair the platform with the people who know how to operationalize it. Most organizations struggle to act on data because they lack the resources, the right insight layer, or a clear place to start.

Why most Voice of Customer programs fail to drive action

There’s a common assumption that implementing a Voice of Customer (VoC) platform means having a mature CX program. It doesn’t. It means having the capability to build one.

The pattern repeats across enterprise programs:

• Teams get dashboards but lack direction on which signals matter
• Insights are generated but never prioritized against business outcomes
• Data sits accessible but disconnected from operations, strategy, or the daily decisions of frontline teams

A feedback tool is only as valuable as the decisions it informs. Plenty of organizations roll out enterprise-grade platforms expecting customer satisfaction scores to climb on their own, then watch the numbers flatline through quarter after quarter of board reviews. The platform isn’t the problem. The team around it is.

Mature programs treat CX not as a reporting layer but as a thread that runs through corporate strategy. The brand promise made in marketing has to match the experience delivered in operations, and that match doesn’t happen because a dashboard exists — it happens because someone built the workflow that closes the gap.

CX and EX initiatives that stay siloed from each other tend to produce the same disconnect on the inside. A customer-centric culture isn’t declared in a town hall; it’s built when employee experience and customer experience are measured, owned, and improved together, with leadership treating both as the same problem.

CX maturity is measured by decision speed — how quickly the organization moves from a signal to a fix to a result the customer can feel. Most programs stall above the platform layer, not inside it.

What CX consulting does (beyond the platform)

The role of consulting in a mature CX program isn’t to run the platform. It’s to make sure the platform runs the right things. That work breaks down four ways.

1. Direction

A consultant helps the organization decide what matters most: which signals to prioritize, which to ignore, and how to tie CX metrics to business KPIs the C-suite already cares about. Without that filter, every metric feels equally urgent, which means none of them are.

2. Accelerating time to value

Programs that try to mature on their own tend to take twelve to eighteen months to find their rhythm. Consulting compresses that timeline by bringing the patterns, frameworks, and missteps of comparable programs into the room from day one.

3. Cross-functional alignment

CX, EX, digital, and operations usually run on parallel tracks with parallel data. A consultant brings them into the same conversation, connects the data sources, and builds a shared view of experience that survives leadership changes and re-orgs.

4. Embedding CX into the business

This is the hardest one. Moving from reports to workflows, from insights to decisions, from a dashboard people glance at to a feedback loop that shapes operations and strategy. A 24-hour response expectation isn’t met by a quarterly readout; it’s met by an operating rhythm. A consultant designs that integration; the platform makes it run.

How RS Group elevated its VoC program with CX Consulting

RS Group came to Forsta with a familiar challenge. Customer feedback was fragmented across systems. The data existed, but unifying and operationalizing it was the gap between a reporting function and a program that could actually shift the business.

The work paired the Forsta platform with CX consulting expertise to embed targeted microsurveys across the moments that shape buying decisions: search, checkout, product pages. The volume tells one part of the story: over 22,000 pieces of actionable feedback, tied directly to product decisions and customer journey improvements.

The shift in how the program operated tells the rest. RS moved from looking at customer experience after the fact to acting on it close to real time.

The lesson isn’t to run more surveys. It’s better signals, captured at the right moment, turned into action without delay.

What to look for in a CX consulting partner

Not every consulting engagement earns its line item. The good ones share a profile; and recognizing it before the contract is signed saves twelve months of frustration.

They challenge assumptions, not just execute them. A strong partner pushes back on the brief. They’ll tell you when the metric you’re chasing is the wrong one, when your customer journey mapping has a blind spot, or when leadership support for the program is softer than the org chart suggests. Execution-only consultants are easy to find and easy to outgrow. The ones worth keeping are willing to disagree.

They tie CX to business outcomes a CFO would recognize. Customer Lifetime Value, retention rates, share of wallet, cost-to-serve, brand advocacy that converts to revenue. A consultant who can’t draw a line from a CX signal to a number in the financial model isn’t helping the program survive its next budget review.

They help you prioritize, not just analyze. Reporting paralysis is the failure state most programs slide into — endless dashboards, no decisions. A strong partner walks in and asks which three things matter most this quarter, not which forty-seven things the platform can measure. They reduce noise. They flag the CX gap that’s actually moving customers, and they ignore the ones that aren’t.

They build cross-functional teams, not the dependency. The best consultants leave a stronger internal CX function behind them, with sharper judgment, clearer playbooks, better-trained owners across operations, digital, and customer-facing teams. They contribute to your CX foundation and CX vision rather than substituting for them. Strategic planning gets handed back. Day-to-day performance management stays in-house.

The simplest test: a year into the engagement, is your team better at running the program without the consultant than they were when the engagement started? If yes, it’s a partnership. If no, it’s a dependency dressed as one.

When you need CX consulting (and when you don’t)

A few patterns suggest the platform alone isn’t enough:

• The VoC program feels reactive. Feedback comes in, gets logged, gets reported quarterly. Nothing in the operating rhythm forces a decision in between. The program is responding to data instead of using it to set direction.
• Insights aren’t turning into action. Agent workflows haven’t changed in a year. Customer journey mapping sits in a slide deck nobody references. The insights exist; the bridge from insight to operations doesn’t.
• Teams aren’t aligned on priorities. CX has one priority list, EX has another, digital has a third, operations has a fourth. Without leadership support to converge them, every team optimizes locally and the customer feels the seams.
• Data volume is growing but business impact is flat. More surveys, more dashboards, more responses — same retention numbers, same CLV, same complaints. That gap is the clearest signal that the program needs help moving from measurement to outcomes.
• Data quality is degrading. Duplicate records, inconsistent tagging, surveys firing at the wrong moments. Data relevance erodes quietly until one day the dashboards stop being trusted, and once trust is gone, the program is harder to rebuild than it was to launch.

Any one of these on its own is fixable internally. Two or more together is the point at which outside help compounds and the longer the patterns persist, the more expensive the eventual fix.

Why CX consulting and technology drivers faster CX maturity

Platforms provide the capability. CX consulting provides the direction. Together they build the operating system that turns Voice of Customer data into measurable business performance.

The organizations moving fastest right now aren’t the ones with the biggest CX budgets. They’re the ones who paired their platform with the strategic planning, human oversight, and outside perspective needed to operationalize it. They built a CX foundation that scales. They closed the gap between insight and action. And they did it on a timeline their competitors can’t match.

Want to see what that looks like applied to your program? Connect with our experts and explore how consulting + technology can accelerate your CX strategy.

What market research teams do need are tools that fit the reality of the work: The deadlines, the complexity, and the constant pressure to do more with less. That may sound obvious, but in practice, plenty of platforms still expect researchers to bend to the software, rather than creating platforms that meet researchers’ needs straight off the bat.

So, what does it really take to build for market research?

We spoke to Debi Hart, VP Product Manager at Forsta, about the thinking behind Research HX, how development decisions get made, and why staying close to real research workflows matters so much.

What ‘built for market research’ really means

When Forsta says it’s ‘built for market research’, what does that look like in practice?

At its core, it comes down to understanding the work itself.

All companies rely on customer feedback, but we take it a step further.  Forsta’s solutions are specifically designed to support agencies and in-house insights teams, shaped by a deep understanding of the workflows behind how research gets done. That level of domain expertise doesn’t sit in one team either.

Our client support team, tech support team, and even account managers have over 10 years of experience in Market Research – many of them coming from the client side. Our platform engineers also have years of experience with our products and MR. The result is a platform that isn’t trying to retrofit research into a generic system, but one that’s built for market research around the realities of the job.

Where do ideas come from, and how do they move from suggestion to something that’s built?

Ideas come from everywhere: Clients, internal market research teams, product owners, and engineering. But the most valuable ideas tend to come from collaboration – taking those inputs and refining them into something that can deliver real value.

Once ideas are shaped, they’re prioritised in partnership with market research leadership. Roadmaps are developed and then shared with key clients to sense-check direction: What resonates, what’s missing, and what would make the biggest difference in practice.

There’s also a more structured way of capturing input. Client-facing teams gather feedback continuously, and those suggestions are fed into a crowdsourcing tool where the wider organisation can vote on what matters most. The highest-priority ideas are then pulled into future roadmaps.

Not everything can get built, but the goal isn’t to meet every individual request; it’s to create the most value for the largest number of researchers.

A lot of companies talk about feedback loops. What does that look like in practice?

Feedback is only useful if it leads to something actionable. At Forsta, that means combining external input from clients with internal expertise from teams who understand market research deeply. Ideas are validated internally, then put in front of clients as early as possible to test how they perform in real-world scenarios.

That iterative loop – build, test, refine – continues until a product is not just technically complete, but genuinely useful in practice. In other words, when we get to ‘done-done’. It might sound like a subtle distinction, but it’s the difference between simply releasing a feature and knowing it actually works in the context it was designed for.

Many platforms expect researchers to adapt their workflows. Forsta has taken a different approach – why?

Because the work is already complex enough. Research teams are juggling multiple projects, stakeholders, and timelines. Asking them to fundamentally change how they operate just to fit a tool creates more friction than it removes.

Instead, the focus is on understanding how clients already work and then building solutions that make those workflows more efficient. That understanding comes from staying close to the industry.

Our teams regularly engage with clients, attend conferences and webinars, and follow emerging trends to ensure the platform continues to reflect how research is evolving. The aim isn’t to redesign the way researchers work. It’s to make the existing workflow smoother, faster, and more intuitive.

Forsta has a lot of market research expertise embedded in its teams. Does that genuinely change the outcome?

Absolutely! Having people involved in product development who’ve worked on the operational side of market research brings a different level of understanding. They know what good looks like, where things typically go wrong, and where efficiencies can be gained without sacrificing control or methodological rigor. That balance is important.

It’s not just about making processes faster. It’s about making them more efficient while still giving researchers the flexibility and precision they need to do their jobs properly. It’s hard to do that without understanding the realities of research.

There’s an ongoing debate in software between tech-first and product-first. Where does Forsta sit?

Forsta is firmly product-first. That means technology is used as an enabler. New capabilities are only developed if there’s a clear benefit for the end user.

We’re not interested in building cool technology for its own sake. If it doesn’t fit into our product strategy or make a researcher’s jobs easier, it doesn’t get built.

This approach is particularly important as new technologies – including AI – continue to evolve. Our focus will always remain on how those capabilities can be applied in a way that adds value within real workflows, rather than chasing innovation for the sake of it.

You’ve worked across different organisations. What stands out about how Forsta builds?

Two things really stand out. The first is focus. Having leadership support to stay aligned on priorities makes a big difference. In many organisations, shifting priorities can make it difficult to deliver anything meaningful. At Forsta, there’s a clear direction, which allows teams to follow through on what they set out to do.

The second is collaboration. There’s a strong sense of shared ownership across teams, with people working together to improve the product rather than operating in silos. It’s less about individual ownership and more about shared goals. We leave egos at the door, we have fun, and we deliver meaningful value for our customers.

Why this matters for research teams

As research teams face increasing pressure to move faster, handle more data, and deliver clearer insights, the tools they rely on are under more scrutiny than ever.

The difference isn’t just in what platforms can do, but in how they’re built – and whether they genuinely reflect the realities of the work researchers do every day. The most effective research environments reflect this – connecting data, automation, and human expertise in workflows that keep researchers firmly in the loop.

Building for market research isn’t about technology alone; it’s about understanding the work well enough to make it better.

Debi Hart, VP Product Manager

Debi Hart leads the development of Research HX at Forsta, a seamless, integrated platform designed specifically for market researchers. With over 20 years of experience in technology, product development, and market research, Debi has been at the forefront of incorporating AI into cutting-edge tools that empower researchers to unlock deeper insights and drive meaningful action.

Discover more about what it means to use tech built for market research by exploring our solutions.

In fact, studies show that the human brain can process images as quickly as 13 milliseconds. So you want them to get the right first impression. Clutter kills comprehension. Clarity creates conviction.

Why less is more

Clarity seems obvious until you try to design it.

Most dashboards don’t set out to overwhelm, but in the name of ‘just in case’, it’s easy to cram every possible chart, metric and filter into a single screen. After all, if the data’s there, someone might need it. Right?

Maybe. But maybe not all at once.

One of the most important principles of dashboard design is deceptively simple: make it easy. That means guiding the user’s eye. Prioritizing what matters most. Giving insight room to breathe. Not dumping a whole load of data and calling it a day.

Because when your dashboard feels easy to use, users feel more confident. More engaged. More likely to act. And that’s the whole point.

And the stakes are real: Gartner reports that poor data literacy is one of the top challenges inhibiting data asset success.

The principles of simplifying your visualizations

So, what does clarity actually look like?

1. Visual hierarchy: Use layout, size, and color to guide attention. What’s the story? What’s the takeaway? Put those front and center – not buried in tab 12
2. Intentional whitespace: Don’t cram every pixel with content. Space helps your most important insights to stand out, while giving the user’s brain a breather
3. Progressive disclosure: You don’t have to show everything at once. Reveal complexity only when it’s needed. Start with the headline insight, then let users dig into the detail if they want it
4. Reduce chart junk: Drop the shadows. Lose the 3D pie charts. Get rid of anything that doesn’t help the user understand the story. Clean visuals beat flashy ones every time
5. Design for scannability: Use bold headers, grouped sections, and consistent formatting so users can find what they need at a glance. The goal is insight in seconds – not minutes of decoding

Together, these techniques turn a cluttered dashboard into a clean, compelling one – the kind that people actually want to use.

This is exactly where tools like Research Agent come into play. Instead of relying on manual reviews or subjective opinions, teams can now pressure-test their dashboards in seconds, ensuring every visual earns its place and every insight is crystal clear.

Clarity = empathy (and that means emotional impact)

Data visualization is about emotion as much as information. People don’t just read dashboards; they react to them. A cluttered dashboard can make users feel anxious, confused, or like they’re failing at understanding something they’re ‘supposed’ to get.

A clear dashboard, on the other hand, says you’ve got this. It builds confidence, trust, and momentum.

This is empathy in action, and simplifying your visualizations is how you deliver it. By removing noise and focusing attention, you’re showing respect for your users’ time, energy and cognitive load. You’re saying: “I see you. I know what you need. Let me make that easier.”

And that emotional response – that feeling of clarity – is what helps insight land harder.

But I need all this data… right?

Totally fair. Sometimes your audience really does need detail.

But simplifying your visualizations doesn’t mean stripping your dashboard bare. It means designing for focus first, and flexibility second.

If you’re scratching your head, here are some easy ways to balance completeness with clarity:

• Smart defaults: Show the most common or important view by default, with filters to explore more
• Tabs or sections: Break up content logically, instead of stacking everything on one screen
• Drill-down paths: Let users click into detail when they want it, but don’t force everyone to start there

These aren’t hacks; they’re simply part of progressive disclosure – helping users to navigate complexity on their own terms. And when done right, they make everyone’s life easier.

Research Agent reinforces this approach by acting as a built-in reviewer. It assesses dashboards based on clarity, credibility, and decision-readiness, highlighting where too much detail muddies the message and where simplification strengthens the story.

Instead of second-guessing what to cut or keep, teams get instant, research-backed recommendations on layout, narrative strength, and visual design. The result: fewer review cycles, faster delivery, and insights that actually stick.

Cut the noise, increase the impact

If you take one thing from this blog, let it be this: simplicity isn’t basic. It’s brave.

Creating a clear, focused dashboard takes more than design skill. It takes empathy. It takes restraint. And it takes the confidence to leave out what doesn’t serve the user – even if it looks pretty darn cool.

But when you effectively nail simplifying your visualizations, you’ll start seeing something else:

• Users who understand your insights the first-time round
• Teams who feel empowered, not overwhelmed
• Decisions made faster, and with more conviction

So next time you’re building a dashboard, remember: clarity = impact. And impact is what makes all of the data wrangling worth it.

Ready to turn clutter into clarity?

If your dashboards are doing more confusing than convincing, it’s time to rethink the way you design, review, and refine your insights.

Research Agent helps you bridge the gap between data and decision by ensuring every dashboard is clear, credible, and ready for action. No guesswork. No bottlenecks. Just better insights, faster.

Find out how Research Agent can transform your reporting and visualization workflows here.

In the world of market research, a beautifully designed data visualizations means nothing if nobody understands it – or worse, nobody uses it. And yet, so many teams fall into the trap of designing for data, not people.

If you want your data to have an impact, you need to start by knowing who it’s for. In other words, data visualization design isn’t just about what you want to show – it’s about what your users need to see.

This blog blends classic data design principles with modern thinking from Forsta’s ebook, The Art and Science of Data Visualization, to give you a fresh, human-centered take on usability.

Read more: The art and science of data visualization: Turning numbers into narratives

Start with the person, not the platform

Before you touch a chart or drag in a dataset, you need to define who you’re designing for. Are you building a snapshot for a busy CMO? A detailed view for a data analyst? A quick-glance summary for a field sales team? Each of these roles will have wildly different needs, levels of data literacy, and time constraints.

The trick is to treat your audience like personas – because once you understand their goals, motivations, and limitations, you can design with empathy. It’s a reminder that how people feel when using your data visualizations – overwhelmed, frustrated, confident, curious – will influence how (and whether) they act on the insights.

Why data visualization falls flat

When dashboards or presentations fail, it’s rarely because of bad data. It’s because they weren’t built for the people who actually need to use them.

Too often, data visualizations are designed around what’s technically possible, not what’s practically helpful. The result? Beautiful charts that no one looks at. Cluttered screens no one understands. Reports that leave stakeholders with more questions than answers.

We’ve all seen dashboards or presentations that try to be everything at once: loaded with KPIs, jammed with filters, riddled with competing charts. They might look impressive at first glance, but they don’t help anyone actually do anything.

Some common missteps:

• Designing for data completeness rather than clarity: Trying to include every datapoint often leads to visual overload and cognitive fatigue
• Prioritizing visual flair over functional flow: When design dazzles but distracts, insight gets lost in the noise
• Cramming too many metrics into one screen: Even well-labeled charts become white noise if there are too many to digest
• Failing to consider user context or environment: A beautiful dashboard that’s unreadable on a tablet or unworkable for a remote team huddle helps no one
• Ignoring role-specific needs or data literacy levels: The same data presented to a CFO and a customer service agent should look and feel very different
• Assuming interactivity equals usability: Just because a dashboard has filters and toggles doesn’t mean users will know how to use them

These traps are easy to fall into, especially when there’s pressure to show everything. But more data doesn’t mean more insight. In fact, it often means more noise.

The fix? Start with your core audience. Ask what they need to know in order to act. Then remove anything that doesn’t serve that need.

Less isn’t just more. It’s more usable, more helpful, and more likely to land.

Read more: Incredible dashboard design principles that make data land

Design for different minds

Let’s say your data visualizations will be shared across departments – from execs to researchers to frontline teams. You can’t assume a one-size-fits-all view will work.

The solution is audience segmentation. Not just for your research participants, but for your dashboard users too. Here’s how:

• Executives: Want high-level summaries, trends, and red flags. Think headlines, callouts, and one-click access to detail (if they ever need it)
• Analysts: Want depth. They’ll benefit from drill-downs, raw data access, and customizable filters
• Operational teams: Want relevance. Give them what affects their patch, product, or customer group

Democratization doesn’t mean dumbing down. It means making data accessible in the way that makes most sense to the user. That means providing:

• Tailored entry points based on role
• Role-based permissions (so users aren’t overwhelmed)
• Consistent design language to reduce cognitive friction

The goal is to create a dashboard that adapts to the person using it – not the other way around.

Make insights findable and usable

Even the most gorgeous dashboard can fail if people don’t know where to look or what to do next. Navigation matters. Hierarchy matters. Defaults matter.

Here are a few user-first design moves to consider:

• Progressive disclosure: Show the most important insights first, with the option to explore more. This reduces overload and guides the user naturally
• Guided pathways: Design flows that help users reach specific business questions or decisions
• Smart defaults: Pre-set views that reflect what most users want to see first, based on role or common behavior

This kind of frictionless experience builds trust. And trust builds usage.

A well-designed dashboard makes the complex feel simple; not because the data is simpler, but because the interface is smarter.

Research Agent: The colleague that knows it all

Research Agent helps turn all of this guidance into something teams can actually execute, not just aspire to. Embedded directly in Visualizations, it acts as an always-on reviewer, analyzing dashboards and reports at the visual level to assess clarity, narrative strength, and decision-readiness based on what stakeholders really see. It flags clutter, weak “so what” statements, and confusing layouts, then suggests how to refine them so insights are clearer, more relevant, and easier to act on. In practice, that means researchers spend less time second-guessing design choices and more time delivering dashboards that land with every audience, from execs to end users, without adding complexity or extra tools.

Human-first visualizations drive real decisions

Designing for humans means more than just clean lines and tidy charts. It’s about recognizing that every data point has a human on the other side of it – and every user has a decision to make.

A human-first dashboard:

• Reflects the needs and context of its users
• Balances clarity with depth
• Makes exploration intuitive and rewarding
• Encourages curiosity without causing confusion
• Translates insight into action

Read more: Human-centered design for market research

Ultimately, the best dashboards feel like they were designed just for you. They speak your language. They fit your workflow. They don’t just show you data – they help you understand it.

Because when people feel empowered, they use the insight. And when they use it, things change.

Now, we’ve used dashboards as an example throughout this, but the same principles apply to all types of data visualization. If you’d like to learn more about how you can make sharing your data quicker, easier and more appealing visit our Visualizations webpage.

We’re now in the next great leap: shoppers are turning to AI to cut through the noise and help them make shopping decisions.  AI is going beyond improving efficiency; it’s driving measurable results. In fact, 69% of retailers using AI report increased revenue, reinforcing its growing role in shaping customer experience and business performance.

Our recent research backs this up. Fifty-eight percent of shoppers made a purchase based on an AI recommendation, and 91% either purchased or seriously considered a purchase after AI input. But AI’s role is different from past digital channels: it excels in planning, discovery, and comparison, especially where choices and stakes are high. It helps shoppers make decisions with confidence. 

But AI isn’t acting alone. While it’s great at narrowing choices, retail expertise is what reassures shoppers they’re making the right decision. AI provides retailers with deep product knowledge, transparent policies, and trusted reputation. Retail brands who can clearly express that authority will be the ones shoppers choose. 

AI in retail customer experience benefits are a two-way street for both consumers and retailers.  Consumers get faster access to relevant information, while AI helps surface retailers’ expertise, empower employees, and elevate the customer–retailer interaction.  So when retail employees wonder “will AI replace me?”, it’s actually going to unlock more time for them to do meaningful, higher-impact work. 

How shoppers use AI today 

Understanding the impact of AI in retail customer experience helps retailers adapt to meet shopper needs effectively.

Shoppers lean on AI in retail customer experience most in high-consideration categories where the stakes are greater, and the number of choices can feel overwhelming. Forty-eight percent of consumers used AI for price comparisons in electronics, and 36% looked to AI for apparel inspiration. In planning-heavy moments (hosting, décor, special-occasion attire), shoppers used AI for ideas and comparisons far more than instant checkout. Only 5–6% completed purchases directly through AI platforms.  

That aligns with my own recent shopping experience. I used AI to narrow down options for a new wearable health device that needed to be comfortable, stylish, and capable of meeting specific health-tracking needs, without breaking the bank. Not long ago that process would have meant reviewing numerous product pages, social posts, reviews, and prices across multiple sites. Now AI can gather, consolidate, and offer personalized recommendations and pros and cons for each in seconds.  

That’s the pattern that is playing out more broadly. AI is compressing the most time-consuming part of the shopping journey from product discovery, comparison, and early evaluation with fewer touchpoints for the consumer.  

During my time in e-commerce retail, I remember how much effort went into improving product discovery: better search, richer content, clearer filters, and more helpful reviews. I think about how AI in retail customer experience today is using that product data, content, and consumer provided information like ratings & reviews, user-generated insights, and more to pull together those recommendations for shoppers.  

This shift creates a clear inflection point for retailers. As AI takes on more of the planning and evaluation work, the quality, clarity, and consistency of the information retailers provide increasingly determines how and whether they show up in these new customer shopping journey moments. 

How retailers show up in an AI‑influenced world 

As shoppers increasingly rely on AI to plan and compare, it’s imperative that a retailer or brand’s information is set up to signal to AI that it is the one to recommend. AI won’t create a brand’s expertise—it will surface whatever is already there.  If a brand’s product details, policies, and content are thin, inconsistent, or hard to interpret, AI can’t confidently do that.  

What AI needs to confidently represent a retailer:  

• Complete, structured product fundamentals: Clear attributes, use-case context, compatibility, care, and warranty details—supported by structured data (such as product schema markup) that makes this information machine-readable. AI uses these signals the same way a store associate would to understand relevance and suitability.

• Decision-support content that mirrors how shoppers think: Comparisons, size guidance, FAQs, “best for” framing, and clear tradeoffs. These act as the confidence‑building moments that shoppers traditionally get from expert associates or detailed product pages.  

• Consistent service and policy clarity: Availability, fulfillment options, returns, pickup, and service commitments. When AI pulls these details into recommendations, it reduces uncertainty and shapes which brands shoppers trust. 

• Credible brand signals: Reviews, user-generated content (UGC), accessibility and sustainability claims, and consistent expression of brand expertise and values. AI uses these cues to judge credibility when shoppers ask for “trusted,” “reliable,” “durable,” or “ethical” recommendations. 

As AI reshapes discovery, information quality increasingly determines visibility. In many ways, AI-ready content is becoming the next evolution of SEO. It’s not about gaming algorithms but supplying clean, structured information that AI can interpret accurately.  

Retailers earn trust in AI‑supported experiences the same way they’ve always earned trust in stores: by showing their work. That includes: 

• Reliable product attributes and naming conventions 

• A content supply chain that keeps every SKU complete and updated 

• Structured data that AI systems can parse 

• A single, trustworthy source for policies and service information 

• Governance to keep content fresh as assortments change 

This makes it incredibly easy for AI to understand, trust, and advocate for your brand. 

How retailers prove credibility and expertise in an AI-led journey 

Shoppers increasingly see AI as credible. Forty-one percent say AI recommendations are equal in value to retailer advice, and 38% say they value AI even more. But trust isn’t simply given. Concerns about privacy, accuracy, transparency, and bias remain widespread, often moderate rather than extreme, which means shoppers are willing to engage, but they’re watching closely.  

Retailers earn trust by showing their work: 

• Being transparent about where and how AI is used: Clear labeling of AI‑generated suggestions or automated decisions builds confidence rather than confusion. 

• Citing the data behind recommendations: Whether it’s product information, reviews, or policies, customers expect to know why something was suggested. 

• Explaining the logic (“best for X because Y”): This mirrors how the best in‑store associates talk—specific, contextual, and confidence‑building. 

• Auditing for accuracy, bias, and hallucinations: Shoppers notice when AI gets something wrong. Retailers who monitor quality build long‑term trust. 

• Protecting privacy and giving customers control: Clear consent, control over data, and practical privacy choices reassure shoppers that the value exchange is fair. 

Done right, AI in retail customer experience amplifies brand credibility. And this is where human expertise comes back into the picture. AI can inform and guide, but people validate, reassure, and help customers feel confident in their final decision. 

What I’d do as a retailer: Where to invest AI in retail customer experience for real impact 

If I were a retailer, I wouldn’t go in trying to involve AI everywhere, and I don’t think most retailers think that. But they’re certainly likely overwhelmed wondering whether to start internally or externally… I say both. I’d start with where decisions are the hardest and confidence matters most. This supports both the customer and employee (a win-win). 

1. Prioritize AI where decisions are hardest

I’d focus on high intent, high complexity categories where shoppers face choice overload. AI is most effective at doing the heavy pre‑work. It shortens the journey and gets customers to a point where a meaningful conversation can happen, faster. 

2. Make sure your brand is eligible to show up in those moments

Before investing in new AI experiences, I’d make sure my product and brand information is strong enough to be recommended in the first place. Priority categories need to be clearly described, easy to compare, and supported by consistent product and policy information. If complex categories aren’t well defined, retailers simply won’t appear when customers are deciding. 

3. Equip associates to build on AI research—not repeat it

What’s great about customers arriving having already used AI is that it speeds up the conversation, so associates can pick up where they left off versus starting at the beginning. They should be trained and equipped to ask what the shopper already considered, understand what AI recommended (and why), and identify the missing context AI didn’t capture. Great associates don’t just answer questions. They surface the questions customers didn’t think to ask such as usage, priorities, and what their own customers have shared back with them.  

4. Use AI to support associates in real time 

To make this work at scale, I’d invest in AI tools that put product knowledge, comparisons, inventory visibility, and policy clarity directly in associates’ hands. This will help eliminate time spent searching for information, reinforce consistent brand guidance, and free up mental space for empathy, listening, and personalization. The result is more confident associates and better customer moments. 

5. Redefine success for high-intent and complex categories (your AI pilot categories)

I’d measure success differently in these categories. While improved conversion rates are usually the largest goal, lower return rates and fewer post-purchase issues can also result, along with higher associate productivity, measured by sales per hour and time spent advising vs. searching.  

AI creates efficiency; the retailers who win will reinvest it into higher‑touch service that builds confidence and loyalty. 

The bottom line 

AI in retail customer experience is the largest disrupter in retail’s evolution since online shopping. It’s becoming a planning partner shoppers rely on when decisions are complex and confidence matters. That’s good news for retailers who are willing to do the work: prepare your data, empower your people, and design the handoff between digital guidance and human credibility. 

The brands that win won’t be the ones trying to replace humans with machines. They’ll be the ones who use AI in retail customer experience to clear the path and let their people do what only people can do: connect, reassure, and earn customers for life. 

None of these moments define customer experience on their own but how quickly you respond to them does.

Wait too long, and that gap fills with frustration. Stretch it further, and it turns into churn. Leave it unanswered, and it becomes distrust.

That’s the pressure on CX today.

And that means speed isn’t a nice-to-have. It’s the experience.

The brands pulling ahead aren’t just listening better. They’re listening faster and doing something with it while it still matters.

This is the essence of quick listening. Quick listening captures insight in the moment and puts it in the hands of people who can act. It collapses the distance between signal and response, closing the loop before the customer has moved on to a similar brand. And that changes everything.

Get it right, and feedback becomes a live wire — something you can feel, respond to, and turn into impact across empowered teams throughout the organization.

Why speed now defines the customer experience

In today’s fast-paced environment, quick listening in CX is more crucial than ever for maintaining customer loyalty.

Customer expectations didn’t evolve gradually; they’ve accelerated. Most CX programs (and most customer service models) weren’t built for the pace.

Today, when customers share feedback, they expect a response, and quickly. According to a 2025 Local Search Consumer Behavior study, 20% of U.S. consumers expect a response the same business day, and 39% expect one within 24 hours.

That’s not a stretch goal. It’s baseline customer behavior.

Always-on digital channels, including chat, social, messaging, and the call center, have compressed patience windows. Customers move fluidly between them, carrying the same expectation of speed at every step.

So, when responses lag, the signal is immediate. It doesn’t feel like a delay in the process, but a lack of attention. That’s why speed now defines the experience. Customers judge how present, responsive, and human your brand feels based on how quickly you respond, moment to moment, across every interaction.

A fast response feels human. A slow one feels automated.

The breakdown of traditional listening programs

Most traditional listening models were built for reporting, not responding. They capture customer insights at the end of a journey, long after the moment has passed and any chance to influence customer satisfaction is gone.

Think about the flow:

• A customer has an experience.
• Days or weeks later, they’re asked about it.
• That feedback moves into CX analytics, gets aggregated, analyzed, and eventually surfaced.

By then, it’s too late. The issue has either been forgotten or escalated.

Centralized CX teams add another layer of delay. Insights have to be reviewed, prioritized, and routed before anything happens. What starts as a signal turns into a queue.

Most dashboards don’t fix that. They store insight rather than creating action. So even when patterns are clear, the response isn’t immediate. It’s scheduled, managed, or deferred.

Meanwhile, CX expectations keep moving in the opposite direction — toward immediacy, responsiveness, and real-time engagement.

That’s the gap: The way most organizations listen is fundamentally out of sync with how customers experience.

To move faster, organizations don’t just need better data. They need to listen differently.

What “quick listening” really means

Quick listening isn’t faster surveys. Speed shifts both how you collect customer signals and where you collect them from.

Instead of relying on delayed surveys, capture real-time feedback across the full customer experience from digital touchpoints and service interactions to conversations and everything in between.

That includes:

• Conversations happening in the moment
• Text and voice feedback from real interactions
• Reviews from the time of doorstep delivery
• Operational data and employee input
• Open-ended, unstructured signals that don’t fit neatly into a form

This is where digital transformation shows up in CX: not as more systems, but as faster connections between experience and response.

When you capture feedback in real time, something else changes significantly. It’s not just when you hear it; it’s who can act on it.

Real-time responsiveness depends on getting insight into the hands of the people closest to the moment — your frontline teams, service reps, and in-location staff. Not just analysts reviewing dashboards after the fact.

And that’s where speed to insight becomes speed to action. With real-time analysis, signals don’t sit. They move. They trigger, prompt, and guide.

Quick listening isn’t about collecting more data. It’s about relevance — capturing the right signal, at the right moment, when it still has the power to change the outcome.

Turning insight into action before trust is lost

Insight without action builds CX debt, and every piece of feedback you collect but don’t act on adds to it.

Customers feel that gap immediately. They’re not measuring your data velocity or your speed-to-insight, though. They’re measuring what happens next.

Did anyone respond? Did anything change? Did the experience improve?

They remember the follow-through. A fast acknowledgment signals presence. A fast resolution signals accountability. Together, they shape brand reputation in real time.

This is where speed creates real value. Real-time tracking lets you see what’s happening, but time to value comes from what you do about it; how quickly you close the loop and turn feedback into action.

Wait too long, and the cost compounds:

• Issues escalate instead of resolve
• Frustration turns into negative reviews
• Private moments become public dissatisfaction

And once that happens, you’re not recovering an experience, you’re repairing trust. Chances are, you’ll have to win that customer back from a competitor, too, as CXDive reports that about 3 in 5 consumers report leaving a brand after just one poor experience.

Speed changes the dynamic by turning signals into something you can respond to, resolve, and learn from while it still matters.

Because the faster you act, the more likely you are to keep the customer.

AI’s role in accelerating CX, without replacing human judgment

Speed at this level doesn’t happen on its own… it needs help.

AI-powered solutions make real-time CX possible, not by replacing people but by clearing the path for them to act faster and with more clarity.

Generative AI can surface patterns instantly, pulling meaning from unstructured feedback that would otherwise take days to process. It can flag emerging issues, highlight shifts in sentiment, and prioritize what needs attention now.

It turns noise into something usable. And in AI-supported service environments, speed matters. It means frontline teams aren’t waiting for reports. They’re responding in the moment, with context already in hand.

But speed without judgment becomes risky. Automating action without understanding the situation — especially in emotional, high-stakes, or trust-sensitive moments — can do more harm than good. A fast response that misses the nuance feels just as wrong as a slow one.

That’s where people come in. Human oversight brings context, reads tone, and knows when a situation needs care, not just closure.

AI accelerates the listening, but human judgment guides the response. And that’s where trust is built.

Empowerment is the real speed multiplier

Remember, speed doesn’t come from dashboards; it comes from decisions, and how quickly that can happen depends on who’s allowed to act.

Most CX programs slow down at the same point: insight reaches a central team, and everything waits. Approval, prioritization, escalation. What should be a moment turns into a process.

That’s the bottleneck, so fast organizations remove it.

They democratize access to insights, putting real-time visibility into the hands of the people closest to the customer. Frontline teams don’t have to wait for direction. They have what they need to respond in the moment.

That in turn shifts where action happens: Closer to the experience. Closer to the problem. Closer to the customer.

But speed without structure doesn’t hold. The teams that move fastest operate with:

• Clear guardrails that define what good looks like
• Shared accountability across roles and locations
• Confidence in the data they’re acting on

That combination creates momentum — where people trust the signal and know they’re empowered to act on it, so they don’t hesitate. They respond.

The fastest and most effective CX teams are the ones built and empowered in that way.

Speed reveals what averages hide

Speed exposes what averages hide. Most CX reporting smooths things out by aggregating scores, blending metrics, and delaying trends. It tells you how the customer experience performs overall, but doesn’t tell you where it breaks.

Quick listening does.

When you connect signals across the full customer journey — social media analytics, CRM systems, digital touchpoints, and the voice channel — you start to see where time creates friction, in real-time.

You see that delayed response after a service interaction. A missed follow-up between teams. A gap between online intent and in-location experience.

These are the moments that erode customer loyalty, and with quick listening, they’re no longer invisible until it’s too late.

Speed changes that.

It highlights where intervention matters most, where acting quickly can prevent churn instead of reacting after the fact. It surfaces handoffs between teams as risk zones, where delays compound and accountability blurs.

That’s the advantage of journey-level insight. You’re not just tracking what happened. You’re seeing where it’s slowing down, and where to step in before the experience breaks.

What CX leaders must do now

Speed isn’t a CX metric. It’s a strategic capability, and it needs to be treated that way.

Most programs still measure success by how quickly they can report insight, but reporting doesn’t change experiences. Action does.

So the question shifts from “How fast did we analyze this?” to “How fast did we respond?”

That’s the standard now, and it forces a different kind of clarity:

• How quickly do we actually respond to feedback?
• Who is empowered to act in the moment?
• Where are we still slow by design?

Those answers reveal the truth. They’ll tell your organization where friction lives, and show you where decisions stall. You’ll see clearly where customers are left waiting.

Customers aren’t expecting perfection. They want to see presence, and to feel heard and acknowledged while the moment still matters. Quick listening signals that. It shows respect and care. It demonstrates accountability.

The brands that win in 2026 won’t just understand their customers better. They’ll respond faster, and that gap is what will separate leaders from laggards.

Turn insight into action while it still matters.

Discover how Forsta helps organizations move from delayed feedback to real-time, actionable experience intelligence.

It didn’t spark curiosity.
It didn’t feel simple or clear.
It didn’t make the story obvious.

That’s why step one in any good dashboard strategy isn’t about KPIs or chart types. It’s about how the experience feels to the people you’re trying to reach. Before you think about what goes into your dashboard, you need to consider how your audience will connect with it.

First impressions shape engagement

Designing a dashboard is a lot like writing a story. If the first few seconds don’t grab your reader (or in this case, your stakeholders), you risk losing them for good.

That’s not about being flashy. It’s about visual clarity and emotional ease – making the data feel approachable, relevant, and purposeful from the very first glance.

A dashboard that looks easy to read is far more likely to be explored. A dashboard that looks confusing is often ignored (regardless of how brilliant the insights might be).

That’s why dashboard design matters. A lot.

Increase the data-ink ratio (without removing the soul)

One of the original golden rules of dashboard design is to increase the data-ink ratio – in other words, minimize unnecessary visual fluff so that every element on the screen is carrying its weight.

Less chart junk = more cognitive breathing space.

But in 2026, we also need to be careful not to go too far. If we strip away too much in the pursuit of minimalism, we can end up with dashboards that feel clinical, cold, or just plain boring.

Modern data storytelling is about striking a balance:

• Remove noise, but keep personality
• Cut clutter, but keep context
• And don’t just reduce – refine

Think about what will help the user feel the meaning, not just see it.

Dashboard designs that look easy to read

This one’s deceptively simple – but critical.

In a world overloaded with infographics, animations, widgets, and toggles, stakeholders are tired. Their attention is fragmented. So, if your dashboard looks hard to use, they won’t even try.

This is how you make it look easy:

• Use consistent, intuitive layouts: Users need to know where to look
• Reduce unnecessary color noise: Use accent colors to guide attention
• Align visual hierarchy with business hierarchy: Put what matters most at the top
• Use plain language: Avoid jargon, labels that confuse, or metrics without meaning

It’s not about dumbing down. It’s about designing with empathy to create emotion.

Research Agent speeds time to dashboard design

If your team does not have the time, headspace, or hands-on bandwidth to constantly fine-tune dashboards for this balance, Research Agent can take care of these rounds of feedback for you.

It helps teams move faster from raw results to clear, compelling stories by surfacing what matters, cutting through clutter, and shaping insights into outputs people can actually use. That means less time wrestling with layout, structure, and signal-to-noise, and more time focusing on what the data is saying and what to do next.

In practice, Research Agent helps you create dashboards and deliverables that are cleaner without feeling cold, sharper without losing nuance, and efficient without stripping out the human touch that makes insights stick.

Find out more: See Research Agent in action

Emotion is the shortcut to action

When we talk about emotional impact in dashboards, we don’t mean making people cry (although we’re not against it). We mean creating a sense of connection. A feeling that ‘this matters’.

Great dashboards don’t just explain what’s happening. They help people feel the urgency of a problem, the potential of a solution, or the significance of a shift – often in a matter of seconds. That emotional response is what moves stakeholders from passive readers to active decision-makers.

To build emotional impact into your dashboard design:

• Focus on visual clarity that builds trust and confidence
• Use language that emphasizes the human context behind the data
• Choose framing elements (colors, icons, layout) that reinforce the tone of the insight
• Bring the data closer to real-world outcomes or individual experiences, wherever possible

Emotional resonance makes insight more memorable, more persuasive, and more likely to spark action.

Decorative framing isn’t fluff – it’s emotional UX

There’s a fine line between decoration and distraction. But used well, decorative framing can significantly boost engagement and memorability.

This could mean:

• A branded header that makes the dashboard feel familiar
• Iconography that reinforces meaning
• A cover page or intro screen that sets the tone
• Light framing visuals that reinforce the purpose of the data

As long as it’s not interfering with clarity, these emotional cues can anchor your audience and create a sense of narrative continuity.

Done right, this kind of visual framing actually makes data feel more human.

Make your dashboard designs worth reading

Put all of these principles into practice, and step one looks something like this:

• Remove visual noise (but not personality)
• Make your dashboard look instantly scannable and simple
• Use emotional cues like framing and iconography to guide attention
• Lead with storytelling, not just stats
• Always design dashboards for human connection, not just data logic

The dashboards that stick aren’t the most complex – they’re the ones that make meaning feel intuitive. And that starts from the first glance.

Now, we’ve used dashboards as an example for data design throughout this, but the same principles apply to all kinds of data visualization. If you’d like to learn more about how you can make sharing your data quicker, easier and more appealing visit our Visualizations webpage.

As this rollout includes features like “Screen Unknown Senders,” short message service (SMS)-based outreach, which has long considered one of the most effective channels for customer feedback, new sources of friction will inherently be introduced. And while the immediate impact may appear modest, the implications are much bigger. 

This update may seem small in nature, but it’s a clear signal of where customer experience (CX) is heading next. In this post, we will share what the future of SMS surveys may look like given the new iOS 26 update and how CX leaders can revolutionize their digital feedback strategy to align with shifts in the market. 

What SMS responses look like post iOS update 

When analyzing how the iOS 26 update is impacting survey response rate, early analysis shows a slight but measurable decline in SMS response rates. This doesn’t come as a surprise given the new hurdle customers have to face to receive notifications from a business. 

Filtering unknown senders introduces an additional barrier between brands and customers. This is especially when messages come from numbers that aren’t saved in a user’s contacts, which is often the case. 

This isn’t alarming, given that response rates across digital channels have been declining for years. 

What this represents an acceleration of an existing trend. 

Customers are becoming more selective. More protective of their attention and what they consume. They’re also becoming less tolerant of outreach that feels impersonal or interruptive. It may appear as though Apple is contributing to this imposition, but in reality, customer expectations are shifting simultaneously.   

In other words, this isn’t about Apple. It’s about expectations. 

Why this matters beyond SMS 

It would be easy to frame this as a tactical issue: adjust your outreach mix, optimize your SMS strategy, and move on. But that misses the bigger shift. What’s happening with SMS is part of a broader reality: No single channel is guaranteed anymore. 

What we are seeing is: 

• SMS can be filtered  

• Email can be ignored and unsubscribed 

• Push notifications can be turned off 

• Apps can be muted  

• Surveys can be abandoned  

The reliability of any of the traditional outreach channels is decreasing. That means CX programs built around channel optimization alone are becoming more fragile. The organizations that adapt fastest will be tasked with rethinking how they listen. 

From channel strategy to connected listening 

The real takeaway isn’t SMS weakening. It’s that fragmented listening strategies are no longer sustainable. 

When feedback lives in silos, small disruptions create outsized impact. A dip in one channel suddenly looks like a data problem. But it’s actually a much larger visibility problem. 

Leading organizations and CX leaders are responding by moving toward connected listening, a strategy and practice that brings together: 

• Customer feedback across channels  

• Behavioral and transactional data  

• Operational signals  

• Employee insights  

• Unstructured feedback such as reviews, transcripts, and comments 

The goal is to reduce dependency on any single source of truth. Churn risk, dissatisfaction, and loyalty shifts show up in different channels which means you must listen to each to get the full picture of your CX efforts. 

What high-performing CX teams are doing differently 

Mature CX businesses are leading the charge by focusing on building a resilient strategy. To do so, they’re actively taking the following steps.  

1. Diversifying outreach without breaking measurement 

Email is proving to be a stable complement to SMS, especially as filtering increases. Additionally, email tends to be a reliable channel in terms of open rates, with the average open rate sitting at 35.63%. But switching channels only works if you can maintain consistent measurement. This is where many CX programs struggle. 

Without consistent measurement across channels, changes in outreach strategy can distort trendlines and erode confidence in insights. It’s imperative that you have a connected listening system in place to ensure you’re able to both diversify and compare. 

2. Reducing friction at the source 

Reducing friction at the customer-level is one of the most effective solutions and also one of the simplest to implement such as: 

• Encouraging customers to save contact numbers  

• Using QR codes or in-moment prompts to build recognition  

• Embedding feedback opportunities within existing journeys 

• Continue to build trust with your audience with credible first- and third-party reviews  

• Timing outreach to moments of highest relevance 

• Leveraging omnichannel reinforcement 

Trusted senders get seen. Unknown senders get filtered. Experience and trust now influence deliverability. 

3. Moving from feedback collection to signal intelligence 

Even with optimized outreach, response-based feedback will always be partial. 

The highest-performing organizations are expanding beyond surveys to include: 

• Digital behavior  

• Service interactions  

• Operational data  

• Unstructured feedback (reviews, transcripts, comments)  

This creates a more complete picture, especially for the silent majority who often don’t respond to surveys. 

4. Prioritizing action over volume 

More responses don’t automatically lead to better outcomes. In fact, many CX programs already have more data than they can act on. 

The differentiator isn’t how much feedback you collect but rather how quickly and effectively you act on it. 

That’s where artificial intelligence (AI) is increasingly playing a role by: 

• Identifying emerging risks earlier  

• Prioritizing issues by business impact  

• Triggering workflows that drive response  

• Triggering workflows that drive response 

• Surfacing root causes across fragmented data sources 

• Enabling real-time, closed-loop feedback across the organization 

In this model, CX shifts from listening at scale to acting with the precision and swiftness consumers have come to expect. 

The bigger shift: CX in a post-channel world 

iOS 26 is a great reminder that customer access is no longer guaranteed. Brands don’t control the inbox, nor do they control the device. Increasingly, they don’t control whether a message is even seen. 

What they can control is how well they understand and respond to experience signals. That’s why the future of CX isn’t tied to any one channel. 

It’s defined by connected listening systems and coordinated action.  

Where Forsta fits in 

At Forsta, we see this shift clearly. We understand that organizations need a better way to connect data everywhere it lives. 

By unifying customer, employee, and operational data into a single experience intelligence layer, Forsta helps organizations: 

• Maintain visibility as channels evolve  

• Link experience to revenue, cost, and risk  

• Identify emerging issues before they escalate  

• Turn insight into coordinated, enterprise-wide action  

Forsta’s HX Platform allows you to connect these insights into one comprehensive view so you can act decisively and quickly. This allows you to, in turn, build a CX strategy that holds up no matter how the channels change. 

The future of SMS surveys 

SMS isn’t going away. Email isn’t replacing it. And iOS 26 isn’t the last change we’ll see. 

The path forward is clear. 

Customers are setting the expectations, and technology is reinforcing them. CX programs need to evolve accordingly to keep up with customer demands. iOS 26 tells us CX practitioners that this isn’t a temporary channel issue that we need to temporarily adjust our tactics for. It requires a structural shift that builds a stronger CX strategy, one in which connect, resilient, AI-powered listening and action takes place at scale. 

Forsta can help you get there. 

This isn’t just a story about faster survey programming, quicker summaries, or dashboards that build themselves. That’s only part of the picture. The bigger pressure now is that market research teams are being asked to move faster and prove more value at the same time.

That’s why the AI conversation is maturing. The question is no longer ‘Can AI automate this task?’ It’s ‘How does AI help research teams get from insight to action more effectively?’

The pressure isn’t new, but the environment is

One of the strongest themes from Forsta’s recent AI Efficiency webinar was that the core pressure on insights teams hasn’t really changed. Researchers have been dealing with shrinking resources and rising expectations for years. In that sense, AI hasn’t created the problem; it’s simply arrived in the middle of it.

Our Chief Customer Experience, Research Officer Luke Williams described this well: Teams on both the MR and CX sides are being asked to do more with less, while also facing growing scrutiny around ROI. Executives know there is more data available than ever before. They also know AI is changing what seems possible. The result is an expectation that insights teams should be able to produce more value, more quickly, and with fewer bottlenecks.

That expectation lands heavily on market research teams. Agencies feel it from clients who want faster turnaround and clearer commercial relevance, while in-house researchers feel it from stakeholders who are under pressure to move quickly and make better decisions.

So while AI can automate parts of the workflow, the real issue isn’t just efficiency for efficiency’s sake. It’s what that AI efficiency creates room for.

Speed only matters if it leads somewhere

This is where market research can borrow something useful from CX.

Customer experience teams have long been pushed to think not only about measurement, but about what happens next. Data alone is not enough. The challenge has always been moving from signals to decisions, and from decisions to change. That same lesson applies to MR right now.

Forsta’s General Manager of Market Research Tobi Andersson made the point that the familiar big buckets of the research process still remain: Design the questionnaire, collect the data, clean and prepare it, build the outputs, then discuss what it all means with the buyer. Those stages are not disappearing. What is changing is how teams move through them.

Instead of manually scripting surveys, cleaning data, and repeating setup work for each project, AI is starting to take on more of that foundational workload. As Tobi described the perfect future, using AI, a questionnaire written in Word could form the basis of a survey, with routing and structure generated automatically, and flow all the way through to accessibility recommendations for data visualization.

That doesn’t remove the need for researchers. What it changes is where their time goes. And that matters, because the most valuable part of research was never the repetitive setup. It was the interpretation, the nuance, the skill to spot what matters, and the quality of the conversation that follows.

What AI is already good at

For market research teams, AI is already proving useful. A consistent theme throughout the webinar was the welcome reduction in manual drudgery. Work that once took a lot of copying, pasting, routing, cleaning, and formatting can now be streamlined significantly. That alone is meaningful.

AI also helps with summarization and pattern detection. Luke’s framing here was useful: AI works well as a thought partner, not a thought replacer. It can surface patterns, highlight anomalies, summarize large amounts of material, and get researchers from a standing start to a more informed first draft.

What AI still does not replace

This is the part worth holding onto.

There is a lot of noise in the market about AI replacing human expertise. That fear is understandable, but it tends to flatten the reality. As Tobi pointed out, the market research industry has gone through several waves like this before. Postal surveys were supposed to disappear. Then CATI. Then traditional approaches were going to be overtaken by online panels, or passive data, or scraped data. Each shift changed the mix – but none erased the need for researchers.

This moment is no different.

AI may be able to help script, clean, and summarize, but it can’t replace what Tobi described as ‘that fingertip feel’ – the subtle judgment that comes from experience, context, and knowing what a piece of data actually means for a client or stakeholder.

Luke made a similar point. AI does not replace context, critical thinking, or strategic interpretation. It can identify patterns and summarize information, but it doesn’t have any meaningful grasp on the competitive realities that shape business decisions.

AI efficiency is not the same thing as value

There is a temptation, when AI is discussed, to focus entirely on time savings. Faster setup. Faster analysis. Faster reporting. Those gains are real, and they matter, but the more interesting question is what teams do with the time they get back.

Luke put it bluntly: If AI reduces a hundred steps in your day, what are you going to do with the time that creates? More work? More creativity? More strategic thinking? More time spent helping stakeholders understand what matters? Take a break?

That is where market research teams can turn AI efficiency into differentiation.

If the time saved simply disappears into more output, AI becomes a throughput story. If that time is reinvested into better stakeholder conversations, stronger interpretation, sharper storytelling, and more commercially useful recommendations, it becomes a value story. And that’s a much more powerful place to be.

What should market researchers do now?

1. Start using these tools. Luke was especially direct on this point: Mastery of AI is a differentiator right now, but it won’t remain one for long. The sooner teams understand what these systems can and can’t do, the better positioned they’ll be to apply them effectively.
2. Keep humans in the loop. Not as a defensive slogan, but as a practical necessity. Governance, validation, and quality control might not be glamorous, but they are what keep AI from becoming a trust problem.
3. Treat AI as infrastructure for better research. AI is a supportive tool – not a substitute for research thinking.The strongest use cases are the ones that remove friction from the workflow so researchers can spend more time on insight quality, communication, and action.

From AI efficiency to impact

For all the focus on automation, Tobi’s perspective is a useful anchor.

The structure of market research isn’t going anywhere. The same core stages still exist, and the same need for judgment and interpretation remains. What’s changing is how quickly teams can move through those stages – and where they spend their time.

This is where research-specific AI capabilities begin to hold real value.

Tools like Forsta’s Research Agent are useful – not because they replace researchers, but because they reduce some of the drag that slows good teams down. They help market researchers get to stronger outputs faster, freeing them up to spend more time refining what matters.

The future of AI efficiency in market research isn’t about cutting humans out of the process. It’s about making more room for the parts of research that humans are best at: Judgment, storytelling, challenge, interpretation, and helping stakeholders make better decisions.

That is the AI efficiency that matters.

They’re telling you what’s working, what’s broken, what’s confusing, and what’s costing you trust. They’re saying it in surveys, chats, reviews, support transcripts, emails, and call center notes.

The signal is there. In most CX programs, the problem is not a lack of feedback, but the lag that follows.

Too often, teams collect the voice of the customer (VoC) insights in real time yet act on it far too late. Feedback comes in fast; analysis takes longer. Alignment takes longer still. This delay is what many organizations now recognize as the customer insight-to-action gap.

Customers feel this lag, too. Research shows that 59% of consumers expect brands to respond within 24 hours and 67% expect follow-up after an interaction. When feedback cycles stretch into weeks, brands risk falling behind those expectations.

During our recent webinar, High-tech, high impact: AI efficiency for insights teams, we explored how the volume and speed of customer feedback is changing the way organizations approach VoC programs. More importantly, we discussed how AI is helping CX teams close a growing gap between what customers say and how quickly organizations act on that insight.

Here are a few of the key CX takeaways from the conversation.

The real challenge is speed

Customers won’t sit around and wait for your next quarterly readout. Customer feedback moves faster than many CX programs can process.

They tell you what happened the moment it happens, in a survey response after a branch visit, or perhaps in an online review posted before they leave the parking lot. It may happen in a support conversation that starts with one issue and ends with three more. In chat transcripts, social channels, call notes, and those support tickets your team is still working through.

It should be good news for CX leaders that you have more ways to hear the customer than ever before. You can tap into customer sentiments across the full journey, from digital friction to service recovery to brand perception. You can see what customers say, where they say it, and often how they felt when they said it.

But abundance has its own problem: the pile keeps growing. Customers share their experiences in real time, and increasingly, they expect brands to respond just as quickly.

This creates what many CX leaders now recognize as the insight-to-action gap.

In most organizations, feedback flows through a familiar lifecycle:

• Feedback is collected through surveys, reviews, support interactions, and social channels.
• CX teams analyze that feedback to identify patterns and themes.
• Insights are shared with operational leaders.
• Teams take action to improve the experience.

In theory, that cycle runs continuously. Feedback arrives, insights surface, and improvements follow.

In practice, each step often slows the next. Data waits to be analyzed. Analysts spend time organizing feedback instead of interpreting it. Patterns surface weeks after customers first experienced the issue.

By the time the insight reaches the team who can fix the problem, the moment has already passed. Customers have moved on, and the issue may have spread to more journeys, more interactions, and more frustrated customers.

The challenge isn’t that organizations lack insight.

It’s that they struggle to operationalize insight while it still matters.

Don’t chase automation; capitalize on the momentum

This is why most AI conversations in CX start in the same place: “We need faster survey programming! Faster dashboards! Faster reports!“

Those improvements matter. No one misses the hours spent scripting a survey or rebuilding the same presentation deck every month, and AI-powered tools can remove a lot of that operational friction.

But those wins, by themselves, are not the breakthrough. The real value of AI in CX shows up when the entire insight lifecycle starts to move faster.

Think about how customer feedback typically flows through a CX program:

1. Collect feedback through CSAT surveys, call transcripts, social media coverage, and support interactions.
2. Analyze feedback using feedback tools and reporting systems.
3. Identify patterns that signal emerging issues or opportunities.
4. Communicate insights to operational and executive teams.
5. Take action that improves the experience.

In theory, that cycle should run continuously, in a loop. Feedback comes in. Insights surface. Teams respond.

In practice, though, each step slows the next.

Data sits waiting to be analyzed. Analysts spend time organizing feedback instead of interpreting it. Patterns surface weeks after customers first experienced the problem.

By the time insights reach decision-makers, the issue has already spread across more journeys, more customers, and more support interactions.

Where traditional workflows break momentum, AI changes the pace.

Instead of waiting for periodic analysis cycles, CX teams can work with real-time insights drawn from large volumes of feedback, including signals from call transcripts, CSAT surveys, and social media coverage. When these are analyzed as they appear, patterns emerge earlier, and teams respond sooner.

And this is where AI customer feedback analysis becomes transformative.

It goes far beyond automating tasks, actually compressing the distance between what customers say and what organizations do next. As that distance shrinks, momentum builds. Feedback moves faster through the system. Insights reach decision-makers sooner. Teams can act while the signal is still fresh.

That’s when customer feedback stops being a reporting exercise and starts becoming a driver of real operational change.

Open-text feedback is where the real insight lives

If you look closely at where that lifecycle slows down, the bottleneck often appears in the same place: open text.

Scores move quickly through a CX system. CSAT, NPS, and rating scales can be aggregated and visualized almost instantly. But the richest feedback customers leave behind rarely comes in the form of a number.

It comes in their own words via:

• Survey verbatims
• Chat transcripts from the contact center
• Product reviews
• Email responses to support teams
• Comments from product reviews
• Notes captured during customer service interactions.

This is where customers explain what actually happened. They describe the moment a process broke, or why something felt frustrating or confusing. They reveal the details behind falling customer satisfaction, rising customer churn, or declining loyalty.

For CX teams, this kind of feedback is incredibly valuable. It provides the context that structured metrics alone cannot.

But historically, it has also been the slowest part of the analysis process.

Traditional text analytics requires building complex rule systems. Analysts create taxonomies to categorize feedback, define keyword rules, map synonyms, and adapt models to handle spelling variations and multiple languages. Over time, those models require constant maintenance as customers describe experiences in new ways.

The work is detailed and often highly specialized.

Because of that complexity, analysis rarely happens in real time. Teams may review only small samples of feedback, or they wait for periodic updates to text models before new insights appear.

By the time those insights surface, the underlying issue may have already spread across multiple customer journeys.

Ironically, the most actionable insight in VoC programs often sits inside open text.

But traditional methods make it difficult to operationalize that insight at the speed modern CX requires.

AI is helping close the insight-to-action gap

This is where AI is beginning to change the pace of CX analytics. And consumers are increasingly open to AI when it improves the experience. Nearly half say they’re comfortable with AI-led CX if it delivers faster service.

On the other hand, 85% of customer service leaders are already exploring or piloting customer-facing generative AI solutions.

Modern AI tools can process far larger volumes of feedback than traditional approaches ever allowed. Instead of sampling small portions of survey responses or support transcripts, organizations can analyze 100 percent of their feedback data across channels.

AI models can automatically identify themes in open-ended comments, detect sentiment patterns, and surface emerging trends across surveys, call transcripts, and social conversations.

Signals that once required weeks of manual analysis can appear almost immediately.

When those signals appear sooner, organizations can respond sooner.

Instead of spending hours preparing data, CX teams can focus on the work that actually improves the experience. They can investigate root causes behind recurring customer issues, prioritize improvements that affect retention or loyalty, and bring clearer insight into conversations with operational leaders.

Importantly, AI doesn’t replace human expertise.

AI excels at scanning large volumes of data and identifying patterns. Human CX leaders still play the critical role of interpreting those signals, deciding what matters most, and guiding the organization toward the right actions.

When used effectively, AI becomes a research assistant for CX teams, accelerating the work machines do best so people can focus on strategy and improvement.

Turning feedback into forward momentum

During the webinar, we also discussed how AI-powered analytics tools like Narrative HX are helping organizations accelerate this process.

Narrative HX uses generative AI to transform open-ended feedback into structured insight at scale. Instead of building and maintaining complex rule-based text analytics models, teams can generate tailored models in minutes and analyze feedback across surveys, contact center transcripts, social conversations, and other sources.

Because the models rely on large language models rather than rigid keyword rules, they can interpret context across different languages and phrasing variations without constant manual maintenance.

The insights don’t sit in a separate system, either. Narrative HX feeds results directly into existing Forsta dashboards, so teams can see themes and sentiment developing across their VoC analytics environment without learning a new tool.

The result is a much faster path from feedback to action:

• Feedback enters the system.
• Patterns emerge across customer data.
• Teams focus on what needs to change.

And when that happens, Voice of the Customer programs stop chasing insight after the fact. They start driving forward motion across the customer experience.

Let AI do the sorting, and keep people on the steering wheel

AI brings with it legit concerns for teams. If machines can analyze feedback faster than humans, what happens to the people whose job it is to interpret it?

The reality is more practical than dramatic. AI excels at the parts of VoC analytics that involve scale.

Natural language processing allows systems to scan thousands of comments across surveys, support transcripts, and social media channels, detecting patterns that would take analysts far longer to find. AI can categorize feedback, summarize themes, and surface signals across large volumes of CX data. It can highlight emerging issues across the customer journey and feed those insights into real-time dashboards where teams can review them quickly.

In other words, AI is very good at sorting the signal.

What it cannot do, at least not reliably, is decide what that signal means for the business.

Context still matters. Understanding the operational realities behind a customer complaint requires knowledge of the organization, its processes, and its priorities. Strategic decisions require judgment. CX leaders must decide which issues matter most, which ones can wait, and how to balance competing priorities across the business.

Human expertise also plays a critical role in governance and ethics. Customer data carries responsibility, and organizations must decide how it is used, who has access to it, and how AI-generated recommendations are validated.

And perhaps most importantly, insight still needs a human advocate.

VoC programs succeed when someone can translate Voice of the Customer data into a story that resonates with executives and operational teams. That requires influence, communication, and the ability to connect insight to action.

AI customer feedback analysis works best in this environment when it acts as a research assistant rather than a decision-maker.

It accelerates the work that machines do well — pattern detection, categorization, and large-scale analysis — so that humans can focus on what they do best: interpreting context, shaping strategy, and guiding improvements across the customer experience.

Let the technology handle the sorting, and keep people on the steering wheel.

The teams that move faster will learn faster

The conversation around AI in customer experience often focuses on capabilities.

• What can the technology do?
• How accurate are the models?
• Which tools should we invest in?

But the real differentiator may turn out to be something simpler: learning speed.

Organizations that integrate AI into their Voice of the Customer programs earlier gain more opportunities to experiment, test ideas, and refine their understanding of the customer journey.

They can explore patterns in feedback faster, validate assumptions more quickly, and build stronger instincts about what drives customer satisfaction and loyalty.

Over time, those learning cycles compound.

Within a few years, AI capabilities will likely be embedded across most CX platforms. The technology itself will no longer be the differentiator.

How organizations use it will be.

And the teams that start now will already know how to translate customer feedback into faster operational decisions.

Watch the full webinar

The insights above only scratch the surface of the discussion.

In the full webinar, we explore how evolving shopper expectations are reshaping retail CX, how brands can identify emerging signals earlier, and how AI-driven analytics is helping organizations close the gap between customer feedback and meaningful action.

Watch the full webinar replay to see these ideas in action and learn how leading brands are adapting their CX strategies.

The shift introduced by agentic AI isn’t simply about generating smarter outputs. It’s about connecting tasks across a project – allowing information to move from design through to analysis and reporting with greater continuity. 

Moving beyond task-based AI 

Traditional AI systems tend to work within clearly defined boundaries – classifying responses, identifying sentiment, or automating repetitive steps in a process. Helpful, but not groundbreaking. Generative AI expands on those capabilities by helping researchers to draft surveys, summarize findings, or generate insight narratives. And agentic AI? Well, agentic AI introduces something different entirely. 

Instead of supporting individual tasks, agentic systems aim to coordinate multiple steps in a workflow – pulling together information from different sources, making recommendations based on evolving inputs, and executing sequences of actions that previously required manual intervention. 

In theory, this could allow parts of the research process to run more fluidly. A study design might inform sample selection, which in turn shapes analysis approaches and reporting outputs, all within a connected system. But coordination calls for more than capability. 

Why research presents a unique challenge 

Research projects tend to evolve as new info emerges, priorities shift, or stakeholders call for more perspectives. Interpretation often depends on context that isn’t easily captured in structured data, and it’s this complexity that makes research a pretty tough playing field for agentic systems. 

When research tasks are linked together across a workflow, small issues can escalate quickly. A slightly ambiguous prompt might produce a workable output in isolation but create inconsistencies when passed into later stages of analysis or reporting. 

Without clear visibility into how those decisions are being made or what assumptions sit behind them, researchers may struggle to judge whether automated recommendations are appropriate. 

Connecting these systems also introduces practical challenges, particularly when multiple platforms need to interact without compromising transparency or data integrity. For an agent to act meaningfully across a workflow, it has to connect with multiple platforms – survey tools, analytics environments, reporting systems – while maintaining transparency around data sources and decision logic.  

In short, agents don’t just need instructions. They need boundaries. 

The foundations that make agentic research possible 

Before agentic AI can deliver real value in market research, certain foundational elements need to be in place. 

• Integration is paramount. Systems must be connected in a way that allows information to move safely between stages of a project. 

• Data governance becomes critical. Researchers must understand where automated outputs originate and what assumptions could be influencing recommendations. Without this visibility, it becomes difficult to assess quality or identify bias. 

• Permission structures matter. Agents operating across datasets must respect organizational policies and privacy requirements. 

• Validation remains an important part of the process. Techniques such as automated clustering, draft reporting, or visualization suggestions may help teams move more quickly, but they still need to be reviewed to make sure findings are interpreted appropriately within broader context. 

While agentic systems can assist with execution, accountability for how those insights are used continues to sit with researchers. 

Read more: Agentic AI: Your personal research assistant 

Human oversight is still central 

Much of the current excitement around agentic systems focuses on autonomy – the idea that AI might one day manage research workflows independently. In reality, most useful applications today involve collaboration. 

Agents can help to draft discussion guides, organize qualitative themes, or highlight emerging trends in community feedback. They can surface potential patterns that might otherwise be missed in large datasets. They can even suggest visualization approaches to improve stakeholder engagement. But researchers still play a critical role in evaluating those outputs.  

Once findings are shared more broadly, it also becomes important to consider how they’re framed, particularly when decisions may follow on from them. This kind of contextual judgment can’t be delegated entirely to an automated system. 

Building toward responsible integration 

As organizations start to test agentic capabilities in practice, attention tends to move beyond experimentation. Questions crop up around how these systems fit into existing workflows, what kinds of safeguards are needed, and who remains accountable as automation expands. 

In many cases, this means weighing potential efficiency gains against the need for appropriate governance. 

Accelerated analysis may come with some compelling advantages, but only if researchers retain confidence in the methods used to generate findings. Put simply, agentic AI doesn’t eliminate the need for methodological rigor; it actually increases the importance of structured oversight. 

Supporting human-led research at scale 

In market research, agentic AI is more likely to support existing workflows than replace the people working within them. 

Coordinating routine tasks and surfacing relevant information at key moments can help teams to manage increasingly complex datasets, stakeholder expectations, and timelines. This may create more space for interpretation, communication, and strategic alignment across projects. 

Read more: AI agents simplify dashboards into actionable storytelling 

Platforms built with governance and connected workflows in mind can play an important role in supporting that balance. Approaches such as Forsta’s Research Agent aim to embed agentic capabilities within structured environments, enabling teams to benefit from automation while maintaining oversight. 

In doing so, they help organizations to move towards a model of research where AI supports execution, and human expertise continues to guide understanding. 

They don’t see the careful sampling or the hours you spent cleaning verbatims. They see the headline, judge at one chart, and make a sweeping generalization at one color palette. Perhaps not the most helpful at a time when the demand for useful data is at an all-time high. But hey, we’re visual creatures, so you can’t totally blame the non-researchers. 

Can AI help with data visualization? 

In MRII’s global study, 62% of market researchers said they or their team are already using AI tools, up from 39% the year before. Now, so far, most researchers have been using AI to speed up steps in the research process. We’ve seen this, with Research HX using AI and integration to cut research time in half¹.  

But what if we can apply AI to visual storytelling too? Taking a step beyond just speeding things up and moving towards improving how we can communicate data.  

Visualizations: The high-stakes step 

We have enough data, we now have the speed to process it, we have the human research expertise to know what data points make important insights for the audience. This is no longer the bottleneck, understanding is. 

We know we only have a few seconds to capture our audience’s attention. Attention spans have been shrinking since the dawn of the smartphone, and with publications like TIME reporting this means our focus is now worse than that of a goldfish, it dramatically highlights another problem when presenting to non-researchers. Data literacy. Because that common 9-second goldfish stat (while incidentally proving the point) is entirely made up, they can remember things for months.  

Data literacy = how well someone can read, comprehend, analyze and communicate data. Studies show that 88% of our audiences may struggle with this. 

This is why you’ve been tasked with the research, because this is a skill you can bring to the table that your stakeholders have recognized they need. So how do you communicate well with someone with the attention span of a fly, who can’t understand the data you’ve collected, and who probably wants their own opinions reinforced by you? You already know the answer… 

This is why data visualization is such an essential step; getting it right can make or break all the work you’ve already put into reaching that point. Because when a stakeholder says, “I don’t get this chart,” they’re not being difficult. They’re human. 

Why this matters for market research visualizations: If your chart requires a mini lecture, you’ve already lost. A visualization isn’t ‘supporting evidence.’ It’s the whole case, all by itself. 

Read more: Seeing is believing: How to display your data story 

That’s exactly why Research Agent exists: It’s built to make visualizations clearer, conclusions stronger, and reporting faster, by reviewing the actual slides/infographics/tables stakeholders see and coaching you in the moment to make adjustments that will help your insights land. 

What are ‘research agents’? 

Research agents are born from the latest AI developments and the research industry’s gradual move towards agentic. To simplify, they can appear as chatbots that live in your existing workflows to help you to improve the creation, analysis and the display of data.  

Read more about AI agents: Agentic AI: Your personal research assistant

For example, within Forsta’s Visualizations, Metadata Agent cleans and standardizes data before reporting even begins to acceplerate set up. And Research Agent’s job is to evaluate whether an insight within a report is clear, credible, and decision-worthy, based on what’s actually on the slide: 

• Research Agent strengthens the “so what” by identifying weak or unclear takeaways and helping refine them into confident, decision-ready conclusions, with an exec-ready focus. 
• It also gives design and layout guidance to reduce rework caused by cluttered slides. 
• Acts as a first-pass reviewer to reduce iteration cycles and speed delivery.
• Supports iterative conversation, so insight development becomes exploratory instead of linear and fragile. 
• Provides design and layout guidance to reduce rework and reliance on specialist designers. 
• It’s embedded directly in Visualizations, keeping insight work in flow with fewer tools and less friction.

This bundle of functionalities tackles the real problem: Slide quality is a messy mix of analysis, design, and storytelling, and most teams are trying to do it all with fewer people and less time, and in many cases, without a data design background. 

The bottom line 

Research AI agents help you to deliver what execs actually want from insight teams: Clarity, confidence, and all this at speed. It assists you turning technically correct charts into insights that can and will guide decisions, surfacing the “so what” instantly so leaders can move with full confidence in you.

Lean teams can produce senior-level outputs without ballooning headcount, while research-native intelligence keeps conclusions credible, methodologically sound, and fit for high-stakes decisions. Faster insights, fewer bottlenecks, and no compromise on rigor. 

So go ahead and ask your AI agents.

Just make sure it’s an agent that understands research, respects rigor, and improves the story on the slide where decisions actually get made.

Find out more about Forsta and research agents on our dedicated webpage.

1. Forsta research: A side by side comparison producing a project where questionnaire is 30 questions, sample 5000 respondents, 100 slides in ppt and tables where demographics are crossed by all questions is the deliverable, fieldwork is 10 days.