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.

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 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. 

It’s how we get from ‘people say X’ to ‘here’s why they feel X.’ It’s the bridge between data and decision-making. And it’s the reason your research doesn’t just tick a box. 

Let’s break down why empathy is still one of the most powerful tools we have – even (and especially) in an increasingly AI-driven world. 

What is empathy in market research? 

Empathy in market research is about understanding people as they are, not as we assume they are. 

It’s the ability to walk in the shoes of your respondents, see the world through their eyes, and create space for them to share openly. That means writing questions that don’t lead. Creating environments where participants feel safe. And analyzing findings with curiosity, not confirmation bias. 

Empathy makes you a better researcher. It helps you design smarter studies, ask richer questions, and uncover the insight hiding beneath the surface.  

It’s also the heart of Human Experience (HX). At Forsta, we believe you can’t deliver great insights without deeply understanding the human behind the response. 

Why empathy still wins in a tech-powered world 

Let’s be clear: We love technology. But as powerful as platforms and analytics have become, they still can’t feel. 

Empathy is what helps you: 

• Spot the awkward pause in an interview that means something was off 
• Hear the hesitation in a voice and know to follow up 
• Read the emotion in a longform text answer  

In short: Empathy helps you connect dots that algorithms can’t. 

And as data becomes more abundant (and more automated), empathy becomes even more valuable. It’s how you find the insight that isn’t obvious. The nuance that matters. The why behind the what. 

What are the four components of empathy? 

(And how they show up in market research) 

Empathy might feel like an instinct, but it’s actually made up of a few key abilities that help us tune into other people’s experiences. In research, these show up all the time – whether we realize it or not. 

Here’s how the four core components of empathy come to life in our work: 

1. Perspective-taking: The ability to put yourself in someone else’s shoes – intellectually and emotionally. In research, this means framing questions that reflect the language, experience, and reality of your audience, and designing inclusive studies that avoid assumption or bias. 
2. Emotional regulation: Staying calm and centered, even when the topic is emotionally charged. In research, this is all about holding space during sensitive interviews, and managing your own reactions so you can focus fully on the participant’s experience. 
3. Self-awareness: Noticing your own lens, and how it might shape what you hear, how you ask, or what you assume. In research, this means being mindful of personal bias when analyzing data, and reflecting on how your worldview may influence question design or participant interaction. 
4. Empathic concern: Actually caring about the people behind the data, and what their voices represent. In research, this means creating environments where people feel heard and safe, and prioritizing authenticity. 

Empathy isn’t just a mindset – it’s a skillset. And in market research, it’s one that shapes everything from the quality of your data to the impact of your final recommendations. 

AI, bias, and why humans are still essential 

Yes, AI is changing the game. It can transcribe interviews in seconds, tag themes, detect sentiment, and even generate summaries. 

But here’s what it can’t do: care. Feel. Question its own conclusions. 

And that’s where human researchers still have the edge. 

Empathy is what helps us: 

• Spot when something just doesn’t feel right – even if the data says it’s fine 
• Question if bias is creeping into our tools, samples, or analysis 
• Handle emotionally complex topics with care and cultural sensitivity 

Left unchecked, AI can amplify bias, oversimplify nuance, or miss out on the very insights that matter most. 

The sweet spot? Use AI to do the heavy lifting – the sorting, summarizing, and organizing – while keeping humans in the loop to add context, challenge assumptions, and inject empathy. 

Because empathy is what makes insight human. And humans still make the best humans. 

How can empathy be measured? 

(Yes, even the fuzzy stuff can be tracked) 

While empathy may feel intangible, there are ways to measure its impact in research – and they’re more grounded than you might think. Here’s how we can begin to spot the signs. 

Qualitative indicators: 

Sometimes, you can feel the rapport in the room (or through the screen). When participants are engaged, open, and expressive, it’s often because they feel safe and understood. Look for things like: 

• Richness of participant responses 
• Willingness to disclose sensitive opinions 
• Level of emotional engagement in a session 
• Participant feedback on how comfortable and heard they felt 

These are the ‘soft signs’ that your research environment is working – and that empathy is playing its part. 

Quantitative proxies: 

Yes, empathy can show up in the numbers too. It might not have a dedicated column in your Excel sheet, but it often correlates with indicators like: 

• Emotional intensity scoring from open-ends 
• Dropout rates vs completion (higher empathy = more engagement) 
• Post-survey ratings on trust, comfort, or perceived respect 

If people feel seen, they tend to stick around – and share more along the way. 

Tech-enabled tools: 

New tools are giving researchers more ways to observe empathy in action – or at least the emotional signals that point toward it. For example: 

• Facial coding and tone analysis in online focus groups 
• Text and speech analytics to detect sentiment 
• Tagging tools that track emotional language patterns 

Used thoughtfully, these tools help you to capture and interpret what participants might not be saying out loud – but are definitely expressing. 

Together, these signals can tell you if your research approach is landing with care and connection – and show you where to dial that human touch up or down. 

Empathy is your edge: Use it wisely  

Empathy isn’t soft. It’s smart. Strategic. And essential for research that actually reflects the people it’s meant to represent. 

In an industry increasingly shaped by speed, automation, and big data, empathy is how we stay human. It’s what helps us build better products, design more meaningful experiences, and truly listen to the voices that matter. 

And yes. Even in a world of AI-powered everything, human insight still can’t be automated. 

At Forsta, we build tools that amplify human insight – not replace it. Whether you’re running global surveys, online focus groups, or in-depth qual, we make it easier to listen with empathy and act with confidence. Find out more today. 

Here’s the good news: You don’t need to slave away for hours to make sense of it all. What you do need is integration. Yep, that unassuming word is doing some serious heavy lifting behind the scenes – especially when it comes to how we are enhancing data visualization. 

The data dilemma 

Modern research platforms are incredible. They let us capture more data from more sources, faster than ever before. But this absolute deluge of information can easily overwhelm. If your data is scattered across disconnected platforms and stored in a dozen different formats, you’re likely spending more time wrangling it than using it. In fact, market researchers spend 80% of their time preparing data and only 20% on actual analysis. 

Not only that, but when it comes to sharing your findings, you’re left with visuals that either oversimplify or completely miss the nuance. And that’s where integration swoops in (with a cape and a can-do attitude). 

So, what’s integration all about?  

Integration: Your data’s new best friend 

Integrated platforms (like Forsta’s HX platform) bring everything together under one neat and tidy roof. They let you: 

• Streamline workflows: From data collection and analysis to reporting and visualization, it all happens in one place. 
• Break down silos: No more ‘wait, which platform was that stored in?’ moments. 
• Speed up time to insight: Because let’s be real, no one has six weeks to prep a deck anymore. 

But one of the most powerful outcomes of integration? That’s right, it’s all about enhancing data visualization.

Why visualization matters 

People don’t make decisions based on data. They make decisions based on the stories that data tells. And the best stories are the ones that are easy to understand, quick to digest, and hard to forget. That’s where good data visualization comes in.

A messy chart is like a joke with too many punchlines. It confuses. It distracts. It doesn’t land. But clear, well-designed visuals? They spark ‘aha’ moments, shine a light on the insights that matter, and do it in a way that sticks. 

Enhancing data visualization with integration

Here’s how an integrated platform transforms your visuals from functional to phenomenal: 

One source of truth 

When your data lives in one place, you’re not second-guessing whether that chart is pulling from the latest numbers. You’re not manually updating visuals or stitching together reports from seven different systems. You get real-time, accurate insights – every time. 

Faster, smarter reporting 

Imagine you could speedrun PowerPoint reporting. Less hours manually tinkering means you can focus on the story, not the formatting. 

Custom views for different audiences 

The CEO wants headlines. Your analyst team wants the nitty gritty. Your client wants something in between. Integrated platforms let you easily filter visual outputs to suit whoever’s on the receiving end. 

Interactive dashboards 

Static charts are so 2009. With integrated dashboards, stakeholders can explore the data themselves. Slice it. Dice it. Zoom in. Zoom out. It’s data on demand and it keeps people engaged. 

More time for human thinking 

When your platform takes care of the heavy lifting, your team is freed up to do what humans do best: ask smart questions, spot patterns, and find meaning. 

The clarity crisis (and how integration helps) 

Your clients aren’t short on data. They’re short on clarity. What they need is to: 

• Get insights faster 
• Make those insights more accessible to stakeholders 
• Tell stories that lead to action 

Integration solves this by unifying the research process, reducing manual effort, and enabling visuals that are beautiful and meaningful in equal measure. Because when insight is buried under complexity, it can’t do its job. 

Real talk: What this looks like in practice 

Scenario 1  

Imagine you’re running a global brand tracker. You’ve got survey data from six regions, across multiple years. In a non-integrated world, this might mean different platforms, different formatting, and a lot of copy and pasting. 

With integration? That’s all happening in one ecosystem. Your data lands in the same place, gets harmonized automatically, and turns into visuals that update as new waves come in. You’re not just saving time. You’re delivering insights that are easier to act on and harder to ignore. 

Scenario 2 

Let’s say you’re working on an omnibus study, and the feedback needs to be displayed and branded differently for each client. Normally, each of these outputs would need to be manually designed and presented, with hours dedicated to switching out logos and eye-dropping colors.  

With integration, everything lives on one platform. Your survey data can roll in, text analytics can uncover common themes, sentiment analysis identifies trends, and visual dashboards with pre-saved client themes bring those stories to life in half the time.  

Integrated platforms allow for real-time reporting, so you’re not relying on outdated charts by the time you hit send. Stakeholders stay informed, decisions get made quicker, and your team gets back hours each week. Time is money, and integration puts some of that precious time back in your pocket. 

Don’t just show data: Show impact 

Visualization isn’t about making data look pretty; it’s about helping people get it. And that only happens when your tools are connected, your workflows are seamless, and your story is clear. 

With integration, your data isn’t just stored; it’s activated. It becomes a living, breathing part of your business decisions. And with visuals that do your insights justice, you’re not just reporting the facts. You’re shaping the future. 

That’s the power of integration.  Now go forth and visualize boldly. 

Download our latest ebook and take a deep dive into the power of enhancing data visualization.  

At our recent webinar, data visualization experts shared exactly how agencies and in-house teams can stop dashboards drifting into irrelevance. Their advice was blunt, practical, and rooted in years of building dashboards that actually get used. 

Clients are not asking for more dashboards to be the future of visualizations. They are asking for faster answers, simpler storytelling, and evidence that sparks action. GRIT’s 2025 report confirms: Research teams are shifting from “showing the data” to “guiding the decision.” AI is accelerating delivery, but human storytelling is what makes the message stick. 

Watch now: Picture this: The future of visualizations 

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

Learn from our experts: Realities behind dashboard stickiness 

• Dashboards are never set-and-forget: One expert described a program that has been live for seven years, but is never static. Annual reviews align dashboards with new tech, design trends, and business priorities. 
• Fix what wastes time: A single client saved hours per cycle when manual reporting steps were automated, expanding dashboard access from a small marketing team to over 300 stakeholders. 
• Standardization delivers speed: Productized dashboards, templated designs that can be cloned, skinned, and deployed, turn repetitive requests into rapid delivery. 
• Adoption drives retention: Dynamic, easy-to-use dashboards create stickiness. Clients who like the tool tend to stay with the agency longer. 

Why dashboard retention matters 

Adoption equals influence, now and in the future of visualizations. A dashboard that isn’t used is a wasted investment. And adoption doesn’t come from cramming in charts. It comes from role relevance, process automation, and design choices that reduce cognitive load. It also increasingly comes from blending AI speed with human storytelling, because decision-makers respond to emotion as much as logic. 

The role of AI in the future of visualizations

Our panel agreed: AI is becoming a less-than-silent partner in dashboard delivery. It can: 

• Automate repetitive reporting tasks, like building first-pass decks or highlighting anomalies. 
• Create summaries and topic lists from collected data. 
• Accelerate data cleaning and reformatting, especially for qualitative data. 

But despite having a place in the future of visualizations, AI has limits. Today, it gets you 70–80% of the way there. However, it lacks the human social skills that can craft stories that resonate with executives, nor can it replicate the nuance of strong design. As Rachel Cummins, Director, Data Management and Delivery at The Directions Group, puts it: “Clients don’t want the machines to do everything for them. They still want a human to be involved and make sure everything is right.” 

Looking ahead, the group pointed to conversational AI as the next frontier. Instead of clicking filters, users will ask: “How are sales trending since Q2?” and see a visual answer. That lowers the barrier to entry, but it comes with risks. Hallucinations, misinterpretations, and overconfidence in machine answers. Educating clients on the limits of AI is now part of the researcher’s role. 

Emotional storytelling: Why it works 

Good visualization is not just logical, it is emotional. Emotional storytelling connects with audiences on a deeper level, making insights memorable and actionable. Tobi Andersson described it as “the journey the user takes, how they consume data, step by step, to start to understand what kind of decisions to take and act upon.” In a world where attention spans are shrinking, emotional resonance makes the difference between charts that inform and stories that inspire. This is the future of visualizations.

Plays to make dashboards stick 

1. Start with the “why” (and roles, not charts): Tie every dashboard to a business purpose. A CMO wants movement in market share. A sales lead wants territory performance. Build with those lenses first. 
2. Build once, reuse often: Productize trackers and pulse studies. This means finding consistent KPIs across clients and waves. Faster for you, clearer for them. 
3. Blend live and batch: Stakeholders still want decks. Keep one governed dataset that powers both interactive dashboards and editable PowerPoint exports. 
4. Automate the annoying: Free researchers from repetitive reporting so they can focus on insight generation. 
5. Simplify story complexity: Think about story arc, role relevance, and delivery formats before you launch. Avoid information overload. Design for cognitive ease, not chart density. 
6. Measure the mundane: Track adoption, time-to-insight, and the number of decisions influenced. In MR, these are the real KPIs for whether your dashboard program survives. 

If you want to make your dashboards indispensable, treat them like products. Pick one high-visibility program, redesign it around roles and the “why,” automate what you can, and measure adoption and engagement as rigorously as you would revenue. 

The future visualizations will not be defined by dashboards alone. It will be defined by the fusion of AI’s efficiency with human creativity and emotional storytelling. That is the modern alchemy that moves insights from charts to change, and from research to real impact. 

Why AI ethics matters more tomorrow than today 

AI is becoming less of a tool and more of a teammate. That means more autonomy, more velocity, and more potential for mistakes at machine speed. An ethical foundation lets you move fast without breaking trust. It protects participants, preserves panel health, and strengthens client confidence. It also aligns you with the 2025 ICC and ESOMAR International Code, recently updated to emphasise the need for AI ethics, which centers duty of care, data minimization, privacy, transparency, bias awareness, synthetic data, and human oversight. 

Read our full article on the 2025 ICC and ESOMAR International Code: AI in Market Research: Five rules to live by 

The ICC/ESOMAR Code at a glance 

What it is 

The ICC and ESOMAR International Code on Market, Opinion and Social Research and Data Analytics is the global self-regulatory standard for our profession. The 2025 revision updates the Code for today’s tech stack, with clear expectations for AI-enabled work. 

Why it matters 

The Code protects participants, preserves public confidence, and sets a bar that often goes beyond law. It clarifies responsibilities for researchers and clients, so everyone in the chain knows what “good” looks like. 

Key items researchers should apply now 

• Article 1 – Duty of care. Conduct research with due care, avoid harm, and keep a bright line between research and non-research activities. 
• Article 2 – Children and vulnerable people. Obtain appropriate consent and ensure methods are age and context appropriate. 
• Article 3 – Data minimization. Collect and process only data that is relevant to the purpose; pass only the minimum personal data to suppliers. 
• Article 4 – Primary data collection. Identify who you are, secure informed consent, explain recontact, and allow withdrawal; if automation is used in collection, say so. 
• Article 5 – Secondary data. Ensure new uses are compatible with the original purpose; respect restrictions; prevent harm. 
• Article 6 – Data protection and privacy. Provide a clear privacy notice; prevent re-identification even with advanced analytics; secure data; limit retention; handle cross-border transfers and breaches responsibly. 
• Article 7 – Fit for purpose (client responsibilities). Use methods suitable for the population and objective; disclose when AI or emerging tech meaningfully informed analysis or interpretation and state the extent of human oversight. 
• Article 8 – Transparency, confidentiality and responsibility. Be open about potential biases, respect IP, and keep results and communications confidential unless agreed. 
• Article 9 – Publishing findings. Give enough information for the public to assess validity and disclose whether AI or synthetic data played a significant role and how humans oversaw the work.
  • This area of AI ethics is particularly important as studies show that while current synthetic models are representative of the United States, they quickly become less accurate as the cultural distance widens. 

Make trust a measurable feature 

Trust grows when people can see what you do and why. Build that into your process and your product. 

“Trust in the data we collect and analyze, and the insights we provide is paramount to the future of market research. With the new Code, ESOMAR provides the ethical guardrails to ensure that what we do is honest and transparent. As we charge headlong into the AI-driven world, this new code is designed to guide us as human researchers to use AI with humanity.” — Lucy Davison, ESOMAR Council Member 

Practical moves 

• Disclosure by default. Tell clients when AI is involved in sampling, analysis, or reporting. State the extent of human oversight. Tell participants when they interact with an automated interviewer and how their data is protected. 
• Minimum in, maximum protection. Collect the least personal data required. Process in secure, access-controlled environments. Delete or anonymize as soon as the purpose is complete. 
• Bias checks on a schedule. Compare AI outputs with human-coded samples across languages, ages, and cultures. Adjust methods or switch tools when fairness fails. 
• Plain language method notes. Replace mystery with clarity. What data went in? What the system did. Where it performs poorly. Who reviewed and approved? 

AI advances are here. The question is not whether you will use them, but how. Digest and own the ESOMAR Code and you will move faster and win more trust. 

What are diary studies?  

Participants chronicle their day-to-day interactions with a product or service over a set period, often uploading photos, videos, or audio clips alongside text entries. The result is a rich narrative of customer experience, complete with vivid context and emotion. 

When they first started, diary studies were all about the written word, asking customers to write about their experience in a paper journal. Although that’s still an option, digital diaries are now the norm, and participants are requested to submit rich media content, like photos, audio recordings, or videos.

Recent advances in open-text analytics mean it’s easier than ever to analyze unstructured feedback. The ease of producing transcripts of audio and video files, allowing you to dig into them alongside textual entries or even survey responses, has resulted in a renewed interest in qual research.  

Read more about the advantages of diary studies: Digital diaries: Join your customer on their journey 

The future of digital diary studies 

Like the advancements online features gave diary studies, the next wave of change is ready to transform how you interact with your participants, and they with you.  

The concept of data integration in market research is well established and well understood to be a huge benefit to the research process, doubling the speed to insight and saving hours of manual work. What magic happens when you start integrating modes of research? You get something that looks like this: 

An integration between diary studies and online focus groups. 

This means you can schedule and conduct real-time, fully-fledged group discussions or one-on-one interviews (IDIs) directly within a diary study, without juggling separate platforms. Imagine kicking off a diary project to capture a week’s worth of experiences and then inviting those participants to a live video focus group or interview, all in the same interface. Researchers can seamlessly create, schedule, moderate, and later review these live sessions right from the diary project workspace. Participants don’t have to fumble with external links or new logins; they simply join the interview or group chat from the diary app or web portal they’re already using. 

This diary-discussion integration brings the best of both worlds and an entirely new data-collection experience. You get the depth of longitudinal, in-context feedback and can immediately follow up with face-to-face (virtual) conversations to probe further. It streamlines workflows by keeping all qualitative activities in one place, so you can gather, analyze, and visualize the data together.

No more siloed data or exporting of diary entries to prep for a separate focus group. Everything from a participant’s week-long journal to the recording and analysis of their live session is unified. This not only saves time, but it also adds contextual depth. For example, you might notice a diary entry about a frustrating experience; with one click, you can invite that participant to a live discussion to unpack their feelings in real time.

By combining techniques, researchers capture both the authentic, in-the-moment reactions from diaries and the nuanced, interactive probing of a focus group or interview. As Forsta’s Tobi Andersson puts it, this integrated approach creates “an easy-to-use space for two-way conversation that focuses on human connection”, capturing verbal and non-verbal cues – so brands can understand the “why” behind the diary data and act on it with confidence. 

Minimizing the limitations of diary studies 

New tech developments are tackling some long-standing limitations of diary studies. Integration with other modes, such as focus groups, breaks down barriers of communication and enables deeper data collection from an already rich source. Let’s explore some more: 

Integration with everything 

Plugging diary studies into a wider network of market research tools across the whole market research process streamlines every step. Solutions like Forsta’s Research HX super-speed research by allowing data to flow straight from collection to analysis to dashboarding and visualizations. Each stage benefits from a reduction in manual work, less chance for human error as data stays in one system, and faster speed to insights as reporting can update in real time. 

Scaling up 

While diary studies have traditionally been associated with small sample sizes and exploratory research, we’re breaking that mold by making Digital Diaries a heavy-duty tool suitable for large-scale, enterprise-grade projects that can scale up to 2,000 participants in a single diary project. Yes, you read that right – two thousand. This means you can effectively run a mixed qual-quant study within one platform: Thousands of people capturing qualitative feedback while still retaining the depth of a diary approach. This allows insights professionals to swim in a much bigger data pool without losing the storyline of individual customer experiences. 

AI-powered summaries 

Gone are the days of manual coding. Auto-generate a concise summary for any video or audio recording a participant submits. The platform’s AI will produce a written summary of the key points they mentioned, dramatically reducing the need for manual transcription and note-taking. The summaries are available in over 25 languages and are editable, meaning you can fine-tune them or integrate quotes into your reports easily. 

Global reach 

AI-driven translations for both activities and responses break down language barriers, so you can instantly translate diary entries or questions into a common language. This is a game-changer for multi-market research, accelerating project speeds and facilitating global studies and teams.  

New question types 

Self-reporting naturally comes with an element of bias. A diary is from our perspective, we want to be liked, we automatically filter ourselves to present the best version of us. 

Sprinkle in more question types to engage participants and gather data in a wider variety of formats for a full emotional picture. Present image-based multiple-choice questions (Image Select), ask them to rank items in order of preference, gather rating scale feedback (stars, emojis, Likert scales, etc.), or even deploy grid/matrix questions for comparing options. 

Why does this matter? It means you’re not limited to free-text diary entries or participant uploads. Within the flow of a diary study, you can integrate structured questions to get data on specific topics and accept video or photo uploads. These new formats provide quantifiable data points alongside rich narratives, adding another layer to your analysis.  

Collage Activity 

A standout is the Collage Activity, a creative exercise where participants can respond to a prompt by assembling images and text into a digital collage. It’s an unscripted, imaginative way for customers to show how they feel about a product or idea without having to articulate it in words. Collages can serve as a fun icebreaker or as a subtle avenue to express feelings about sensitive topics. In practice, researchers have used collages to help define user personas and uncover subconscious associations which a traditional Q&A might miss. 

Deeper insights, fewer barriers, greater impact 

Diary studies have always been treasured for the depth of insight they unlock, capturing customers’ behaviors and emotions in real life. Now, with the recent innovations in Forsta’s Digital Diaries, that depth is amplified by breadth and efficiency. You can collect in-the-moment, multi-modal feedback at scale, then pivot to in-platform discussions for even more context, all within one integrated system. The new capabilities like autosave, AI summaries, and flexible activities aren’t fancy add-ons; they directly tackle common pain points (missed data, tedious analysis, participant boredom) to improve data quality and researcher productivity.  

If you’re a decision-maker or power user in market research, it’s time to rethink what diary studies can do for you. With workflows streamlined and walls between methods broken down, you can focus on what matters: Uncovering and acting on insights that drive business decisions.  

See it for yourself: Book a demo

It’s an idea that’s been gaining traction in the research world. Not because it’s trendy or because it goes against the flow of instant-access to all the information in the world that AI has encouraged, but because it opens the door to more nuanced insight. The kind that helps you understand not just sentiment, but how beliefs are formed, where behaviors stem from, and what might shift them. 

What are misperceptions in research? 

Let’s start with a distinction: Attitudes, beliefs, and knowledge are not interchangeable. Yet in many surveys, we treat them as if they are. We ask how strongly someone agrees with a statement or how much they trust a brand. But what factual knowledge are they working with? 

If you want to truly understand a respondent’s mindset, you need to know whether their beliefs are anchored in reality or floating freely in assumption. And that’s where things get interesting. 

Because it turns out, people often feel extremely confident about things they’re completely wrong about. 

Why misperceptions matter more than ever 

In a landscape flooded with misinformation, social echo chambers, and algorithm-driven content bubbles, what people believe is increasingly shaped by exposure, not expertise. The Ipsos Perils of Perception studies repeatedly show that people consistently misjudge everything from demographics and crime rates to obesity levels and wealth distribution. 

These gaps are insight-rich indicators of how people filter the world. 

For researchers, misperceptions represent: 

• A window into cultural narratives. 
• A lens on behavioral intent. 
• A diagnostic tool for understanding resistance, bias, or decision-making. 

Asking someone’s opinion without first understanding what they believe to be true can be misleading. If a respondent strongly agrees with a policy but misunderstands its core facts, then the data tells a partial story at best. 

From measurement to meaning: What this looks like in practice 

Let’s say you’re researching attitudes toward electric vehicles (EVs). You ask how favorable someone feels about EV adoption. Great. But what if that person overestimates the percentage of EVs on the road by 300%? Their response suddenly carries a different context, doesn’t it? 

Understanding misperceptions doesn’t invalidate attitude data; it adds dimension to it. That’s the key. You’re not undermining opinions; you’re enriching your interpretation of them. 

And there’s more. Misperceptions can tell you: 

• Where messaging may be landing off-target. 
• What level of public understanding your client needs to work with. 
• How to segment audiences not just by demographics or preferences, but by knowledge baselines. 

Confidence ≠ accuracy: A subtle danger in surveys 

Here’s one of the most quietly problematic dynamics in research: High confidence in low knowledge. It’s the Dunning-Kruger effect in action. Respondents with the least accurate information often feel most sure of themselves, which can skew interpretation if we’re not careful. 

By incorporating questions that measure factual accuracy (without encouraging cheating! After all, about 30% of respondents will look up the results to factual questions) and self-rated confidence, researchers can build a clearer picture of not just what people think, but how strongly—and wrongly—they hold that belief. And that’s the kind of nuance clients love. 

Making the most of misperceptions  

We’re not here to tell seasoned researchers how to do their job, but if you’re thinking of adding a few knowledge or misperception elements to your next study, here are some techniques that help keep the experience respectful, insightful, and free from awkward “gotcha” vibes: 

• Make it feel like a conversation, not a test: The tone of your question matters. Stay neutral and curious, rather than corrective or academic, making it clear that there are no penalties for being wrong and no reward for being correct. Just saying “There are no wrong answers,” doesn’t go far enough. 
• Give them an out: Including a “don’t know” option can stop respondents from feeling forced to use Google to backseat respond. Alternatively, ask for their best guess and frame it accordingly (“Even if you’re not sure, take a stab at it. We’re just curious!”). It keeps the pressure low and the responses honest. 
• Tell people what’s going on: Transparency builds trust. If you’re slipping in knowledge-based questions, say so. Let them know you’re exploring what people know (and don’t know), and that “wrong” answers are just as valuable. It makes respondents feel like participants, not exam-takers. 
• Show the answers at the end: Most people love a good quiz—as long as they get to see how they did. Revealing the correct answers post-survey can add a layer of engagement and even a bit of learning, without undermining the respondent.  
• Mention it upfront: If you plan to show the answers later, say so from the start. It reassures participants that this isn’t a trick test and reduces the temptation to slip in a sneaky side search or straight up Google their way through. It’s about perception, not perfection. 
• Pair knowledge checks with attitudinal questions: Juxtaposing what people believe with what they actually know can generate rich insight, not because one is more valid than the other, but because the gap between them tells a deeper story.  
• Layer in confidence ratings: Confidence doesn’t equal accuracy, but it can highlight when a belief is deeply held, even if it’s entirely off-base. 
• Keep it light, keep it strategic: A few well-placed misperception checks are enough. After all, you’re not writing a final exam. You’re surfacing subtle, context-rich clues that can enhance how your data is interpreted. 

AI and misperceptions: The human touch 

Now AI and human misperceptions have both one big thing in common and one big thing that sets them apart.

AI is often, very, very confidently incorrect. Remember when we tried to get ChatGPT to count? Now, tools that have a more specific role than a LLM and are only using the data you’ve provided don’t tend to run into this problem. But whether it’s right or wrong, AI doesn’t care. AI doesn’t believe in anything. The gap is just that, a gap. 

That’s what makes misperceptions so profoundly human. Bias, belief, misunderstanding; these aren’t bugs in the system. AI can tell you what, but humans can tell you why, because we’re often invested in our beliefs. That little difference in emotion is what separates us from robots and gives insight into the human experience. And if you want your research to reflect the real world, you’ve got to follow that path. Because an incorrect stat to AI can be replaced, but for a human it reveals something about who they are and how they might act.  

The future of knowledge testing  

It’s not about proving people wrong; it’s about understanding the cognitive scaffolding that supports their attitudes and decisions. And when done well, this approach doesn’t just elevate your insights—it makes your research feel smarter, deeper, and more attuned to how people operate in the world. 

Because, as every good researcher knows, the most interesting data isn’t always what people tell you. It’s what lies between the lines.  

We’re not talking about robots replacing researchers; we’re talking about making qualitative insights easier to gather, faster to analyze, and a whole lot more scalable. Thanks to AI, qual is shaking off its reputation for being slow and hard to quantify, and stepping into a new era of speed, structure, and smarts. 

Let’s explore how it’s happening. 

Why we still love good old-fashioned qualitative research

There’s a reason qualitative research never went out of style. It gives you the stuff quant can’t. Human nuance, emotion, motivation, context. A real sense of self. It’s how you find out that people buy your product not just because it’s cheaper, but because it reminds them of home. Or that they’re loyal to your brand because it makes them feel seen, not sold to. 

The stories people tell, the language they use, the moments of hesitation or surprise in a conversation, that’s where the magic happens. 

But here’s the catch: As valuable as qual is, it hasn’t always been the most agile method in the researcher’s toolkit. 

The traditional qualitative research pain points 

We all know the usual headaches: 

• Small sample sizes due to time or budget constraints. 
• Manual analysis that takes weeks (and a strong tolerance for Post-its). 
• Subjective interpretation that’s harder to defend with stakeholders who want stats and certainty. 
• Scalability issues when you’re trying to pull themes from hundreds (or thousands) of open ends. 

In short: It’s rich, but messy. Insightful, but slow. Powerful, but not always practical. Especially at scale. 

AI is changing the qualitative research game 

Enter AI. Suddenly, that pile of messy verbatims starts looking a lot more manageable. AI can now analyze open-ended responses, surface themes, cluster ideas, track sentiment, and even generate hypotheses based on qualitative inputs. What used to take a full team days or weeks to do, AI can now do in hours, or even minutes. 

According to the 2024 GreenBook report, one of the greatest advantages that AI brings to market research is the ability to uncover insights that were previously unattainable: “Tasks that once required considerable time, money, and effort are now made easier with AI”.  

Little wonder then that 69% of technology providers and 56% of full-service research providers are already using Generative AI to supplement work processes: “This widespread adoption enables researchers to extract actionable insights faster by analyzing unstructured data, summarizing meetings, and even drafting reports. This means better, more actionable insights for businesses, derived from previously hidden patterns and connections.” 

Even better? It’s not just about tidying up your data. AI is also helping to enrich it. 

AI probing helps you go deeper without doing more 

One of the more exciting developments is AI-assisted probing, where algorithms are trained to ask the kind of follow-up questions a seasoned moderator might.  

You can now run surveys that automatically ask clarifying questions when someone gives a vague or intriguing answer. For example, if a respondent says, “I just don’t trust that brand,” AI can prompt them with, “What don’t you trust about the brand?” 

This simple, scalable probing can turn a throwaway comment into a rich insight. It’s making depth accessible to more projects, more clients, and more timelines. 

What qualitative research at quantitative scale looks like 

It’s not just probing. Researchers are using a variety of AI-powered tools to analyze qualitative data like it’s quantitative: 

• Thematic clustering of open ends using machine learning. 
• Sentiment analysis across entire datasets. 
• Text coding and tagging in real time. 
• Pattern recognition across interviews, focus groups, or open survey responses. 
• Automated summarization to create digestible insight narratives. 

Some teams are even running large-scale qual studies with 1,000+ participants – something that would have been unthinkable a few years ago. 

And yes, human oversight still matters (and always will). But with AI doing the heavy lifting, researchers get to spend more time interpreting and storytelling, and less time sorting through spreadsheets or sticky notes. 

What this means for insight teams 

AI-powered qual doesn’t just save time. It opens doors. 

• More usability: Teams with smaller budgets or tighter timelines can now include qual in their mix. 
• More agility: You can iterate faster, test messaging with more nuance, or respond to emerging trends in real time. 
• More credibility: When you can show stakeholder-friendly charts from your qual data, you’re more likely to get buy-in and action. 
• More creativity: Freed from the grind of manual coding, researchers can spend more energy on the why, not just the what. 

In short: Qual isn’t being replaced. It’s being supercharged. 

So, what’s next? 

The momentum is real, and it’s pushing qual into places it couldn’t previously reach. 

Forsta’s own AI capabilities (including smart text analytics and responsive follow-up questions) are already making this possible. And as we build, refine, and learn, we’re making sure qual keeps its human heart; even when it’s processed by machines. 

In fact, AI-powered tools like Forsta’s AI probing personalizes surveys in real time to give customers what they really want: More meaningful interactions. The benefit for brands is faster insights, stronger engagement, and the ability to stay ahead of issues before they even arise. And with tools like Forsta’s AI Summary condensing large volumes of feedback into digestible quant-like insights, decision-makers can act fast without wading through endless qual responses.  

The future of qualitative research

For years, qualitative research has been the soulful side of insights. Rich, deep, and (let’s face it) a little unruly. 

AI won’t change that. But it does mean we can scale soulfulness, and bring more of that depth into decisions, campaigns, strategies, and boardrooms. Because when you can treat open-ended data with the same rigor (and speed) as quant, the possibilities multiply. And we think that’s something worth getting excited about. 

Conversational AI is complimenting traditional surveys. Allowing a blend of qual and quant to create adaptive, engaging experiences that not only keep people intrigued but also yield richer, more authentic insights. Let’s explore how this game-changing technology is reshaping the landscape of market research, one question at a time.

The problem with static surveys

Surveys are often stuck in the past. They pose pre-scripted questions in a rigid format, assuming one size fits all. But people aren’t cookie-cutter respondents. They’re diverse, dynamic, and full of unique perspectives. A generic survey simply can’t do them justice.

This static format leads to predictable issues: participant fatigue, rushed answers, and a lack of meaningful data. Worse, participants can feel like they’re not being heard, a fatal flaw when your goal is to understand their thoughts and experiences.

Enter conversational AI: the survey game-changer

Conversational AI flips the script by turning surveys into real-time dialogues. Powered by natural language processing (NLP), this technology creates personalized, adaptive experiences that feel less like interrogation and more like a friendly conversation.

Imagine a participant being asked about their favorite coffee brand. With conversational AI, the survey doesn’t stop at “Brand A.” It follows up with meaningful, follow up prompts, like: “What do you love most about Brand A’s coffee? Is it the taste, the sustainability, or something else entirely?” Suddenly, the survey feels tailored, and the participant feels valued.

The best part? This approach isn’t just fun, it’s effective! By encouraging detailed, thoughtful responses, conversational AI transforms surface-level data into rich, actionable insights.

Boosting engagement, one conversation at a time

Participant engagement is the holy grail of survey success. The more engaged someone is, the more likely they are to provide meaningful, detailed responses. Conversational AI excels here, keeping participants hooked by tailoring the survey flow to their individual answers.

A study published in Behavior Research Methods showed that participants interacting with chatbot-driven surveys reported higher engagement levels than those completing traditional surveys. Why? Because conversational AI makes the process feel interactive and relevant.

By ditching generic scripts and embracing dynamic conversations, these adaptive AI solutions ensure your participants stay engaged from start to finish.

Data quality: from noise to nuance

We’ve all seen it: surveys plagued by random clicks, half-hearted responses, and outright nonsense. These issues aren’t just frustrating, they compromise your data quality. Conversational AI is the ultimate clean-up crew, tackling these problems head-on.

Here’s how researchers are using AI to improve data quality:

1. Dynamic probing: AI generates on-the-fly follow-up questions based on initial responses, digging deeper into participants’ thoughts and emotions.
2. Real-time validation: AI detects gibberish and flags low-quality responses before they infiltrate your database.
3. Multi-modal inputs: Participants can respond using text, voice, or video, adding richness and nuance to the data collected. AI can amalgamate this into one type of data.

By refining questions in real time and validating responses, conversational AI ensures the data you collect isn’t just accurate, it’s meaningful.

Scalability meets depth: the best of both worlds

One of the standout features of conversational AI is its ability to scale qualitative depth across a quantitative reach. Traditionally, deep insights meant small focus groups or interviews, while large-scale studies traded depth for breadth. Conversational AI bridges this gap. It’s like having a team of expert interviewers working around the clock. Except they never tire, and they never forget to ask, “Why?”

Conversational AI in action

Let’s take a look at how Forsta’s solutions are transforming market research:

1. Open-end analysis: Summarize respondent verbatims, identifying themes and saving time manually analysing reams of text.
2. Open Assist: Enrich your surveys with conversation. Follow-up prompts to open-ended responses keep respondents talking, giving you richer answers.
3. Battle the bots: Integrations with sample providers allow us to fight fraudulent AI answers. AI itself can weed out the patterns that flag a generated survey response, keeping your data organic. Longer participant responses, encouraged by prompting, give this detection more to work with.
4. API integrations: Forsta integrates seamlessly with additional AI tools. Add in voice-enabled surveys or chat/video bots.

These innovations make conversational AI an essential tool for agencies looking to deliver insightful, actionable results.

The power of personalization

One of the standout features of conversational AI is its ability to personalize every interaction. By analyzing previous answers and adjusting in real time, AI is capable of creating a unique survey path for each participant.

This personalization doesn’t just enhance engagement, it builds trust. Participants feel like their input truly matters, which encourages them to open up and share more. The result? Deeper insights, better data, and happier participants.

Why agencies can’t afford to ignore conversational AI

Agencies are under constant pressure to deliver faster, cheaper, and better insights. Conversational AI isn’t just a tool, it’s a lifeline. By automating repetitive tasks and enhancing engagement, it frees up your team to focus on strategy and innovation.

With conversational AI solutions, you can:

• Run large-scale studies with qualitative depth
• Ensure data accuracy through real-time validation
• Deliver engaging, interactive surveys that participants actually enjoy

In an increasingly competitive landscape, these advantages can make all the difference.

The future of market research

The integration of conversational AI into survey methodologies is more than a trend. It’s part of the dawn of an era of interconnected and deeper insights. By combining scalability with personalization, this technology opens new doors for understanding human behavior.

At Forsta, we’re leading the charge, offering cutting-edge solutions that enhance data quality while creating enjoyable experiences for participants. Because when surveys feel like conversations, everyone wins.

That’s why we’re sharing five guiding principles to help you master the art of integrating AI into your market research.

But before we dive into all that, let’s start with a quick reminder of why AI can be your ally, rather than an enemy to be afraid of.

Why AI is great for market researchers

Artificial IntelligenceI offers a great big bundle of benefits for the market research industry that goes beyond timesaving and towards all-out revolution. As well as allowing for faster data processing (giving you valuable insights in minutes rather than weeks), AI’s ability to handle repetitive tasks means researchers can focus on high-value activities like strategy and storytelling.

Imagine having a dataset that behaves like your target market, but doesn’t involve waiting for responses (this is synthetic data at its best). Or automating mundane tasks like sorting through open-ended survey responses or generating initial drafts for reports—letting AI do the heavy lifting, so you can spend more time thinking critically and creatively. And because AI can analyze vast amounts of data at once, it can uncover trends and patterns that might be missed manually; kind of like having a super-smart research assistant that never gets tired or distracted.

Now, let’s see how you can make the most of it…

AI for market research, in five simple steps

1. Take time to adapt

First things first—chill. Despite the hype, AI isn’t here to replace you or your research team. As a rather phenomenal tool, it’s actually in the game to take a load off your plate, pick up the boring stuff, and free you up to add value with your own expertise. All while empowering your teams to collaborate across the market research process. And honestly? You’re likely to get left behind if you don’t embrace it.  

Sidenote: take a look at how AI is transforming market research analysis. 

It’s also important to keep in mind that AI tech still has its limitations—whether it’s the cost of building foundational models, limited access to quality training data, or the simple reality that AI is not perfect.

In other words, you’ve got time. Time to experiment, time to adjust, and time to integrate AI in a way that works for you. And we are here to help you every step of the way. AI should be there to assist, not overwhelm, so start by identifying your key challenges and the areas where you could really use an efficiency boost.

Here are a few easy examples of how to integrate AI into the market research process:

• Project timelines dragging due to manual data processing? AI tools can help to automate repetitive tasks and speed things up
• Struggling to brainstorm fresh ideas for survey questions? AI assistants can craft creative prompts to get you started
• Spending too much time drafting proposals? Use AI to generate initial drafts based on past work, saving you valuable time for that all-important human intelligence and refinement

2. Demystify, pilot, and iterate

Remember when you were a kid, and magic tricks were mind-blowing…until someone explained the trick behind them? AI is a bit like that—it’s not magic; it’s just really, really good at specific things (and sometimes surprisingly bad at others).

So, let’s demystify AI and learn the basics—from how machine learning models function to what a language model actually is. And in case you were wondering…

Machine learning models are systems that learn from data to make predictions or decisions without explicit programming, like recognizing patterns in survey responses. A language model, on the other hand, is designed to understand and generate human-like text, which can help researchers draft initial content or analyze qualitative feedback. 

Armed with that knowledge, you can dive into piloting solutions by starting small. That could mean an AI tool to speed up data analysis, or something that helps with proposal writing—whatever seems manageable. The important thing is to get a feel for what AI can do, and more importantly, what it can’t. The golden rule here is: pilot, test, and iterate. AI should evolve alongside your processes, not dictate them.

3. Understand the ‘jagged frontier’

The “Jagged Frontier” is a nifty way of saying that AI progresses in uneven leaps. It can analyze a thousand survey responses in the blink of an eye, but it might still fail hilariously at consistently counting the number of R’s in words like ‘strawberry’ and ‘market researchers.’ AI is astonishingly competent in some areas and surprisingly clueless in others.

Understanding this unevenness is critical. AI is brilliant at data crunching, pattern spotting, and automating routine tasks. But (and it’s a big but) human judgment is irreplaceable for context, ethical considerations, and critical thinking. AI might give you a thousand potential insights, but only YOU can determine which ones are actually meaningful.

The bottom line? Be the expert who adds the ‘so what’ to AI’s insights.

4. Make AI your constant companion

AI isn’t your replacement; it’s your sidekick. The Robin to your Batman. The most successful market researchers will be those who embrace AI as a daily partner. Whether you’re brainstorming research methodologies, summarizing interview transcripts, or drafting initial concepts, invite AI to the table.

Use AI assistants like ChatGPT or Claude to kickstart idea sessions, generate rough drafts, or even summarize lengthy documents so you can focus on the juicy parts. The more you engage with these tools, the better you’ll get at prompting them—learning to ask the right questions and refining their outputs to suit your needs.

Think of AI as that enthusiastic intern who always has a thousand ideas. Some of those ideas will be terrible, some will be great, and some will be just the inspiration you need to build something wonderful.

Using integrated tools is the simplest, and most secure way to keep AI close at hand. Seamlessly integrated AI tools help you cut inefficiencies, exceed client expectations, and confidently lead the future of market research.

5. Be the human in the loop

AI is powerful, but it’s not foolproof. It has a habit of hallucinating (generating false info), producing biased outputs, oversimplifying complexities, or, let’s be honest, getting lost in its own logic. That’s why it’s crucial for you to remain the human in the loop—the one who provides oversight, interprets data, and ensures the output is accurate, fair, and contextually relevant. 

Your expertise is what gives AI outputs value. Your knowledge of client history, market dynamics, and the nuances of consumer behavior will help to translate AI’s raw output into actionable insights. So, always double-check AI-generated results, maintain ethical standards, and remember that while AI can process data, you’re the one who possesses true understanding.

But being the human in the loop isn’t only about quality control—it’s about guiding AI to become a tool that augments your abilities, rather than taking over as an autonomous decision-maker. Your role is to make sure the AI doesn’t just spit out answers but delivers insights that actually matter.

Balancing the best of both worlds

Integrating AI into market research is less about jumping on the trend train and more about creating a seamless partnership where AI amplifies your human expertise. It’s about demystifying the tech, starting small, adapting steadily, and keeping your hands firmly on the wheel.

At Forsta, we’re excited about the potential of AI—but we know it’s nothing without the brilliant researchers using it. So, as you embark on your AI journey, remember: AI isn’t here to replace you, it’s here to help you be even better at what you do. In fact, we’re bringing AI into our own solutions to make it easier for you to start embracing its rich and varied potential! And we’re always on-hand to help you navigate the journey. 

Check out Integration: the new frontier of insights, for more on the power of all in one solutions.

The problem with fragmented workflows 

Market researchers often rely on a patchwork of tools for each step of the process. Many tools and contacts for data collection, sampling, panel management, analysis, reporting, and visualization. While each tool may be excellent in isolation, switching between them creates inefficiencies, increases the risk of errors, and adds unnecessary complexity.  

A study by Greenbook found that researchers struggle with workflow inefficiencies, with most spending up to 80% of their time preparing data rather than analyzing it. This not only limits your team’s productivity but also delays delivering insights to your clients. Ultimately, impacting your ability to stay competitive. 

How integration solves these challenges 

Integration brings together the disparate parts of your research process into a seamless workflow. Solutions like Research HX demonstrate the power of integration by connecting every step of the process and enabling parallel workflows. Here’s why this matters: 

• Boost your efficiency: integrated workflows reduce the need for manual data formatting and transfers, cutting project timelines significantly. Less time spent on repetitive tasks and more time focusing on strategic analysis 
• Reduce human error: fewer manual steps mean fewer errors. Integrated systems maintain data integrity across all stages, ensuring that insights are reliable and consistent 
• See insights faster: with parallel workflows and AI-enhanced automation, insights can be delivered in less than half the time, meeting your clients’ increasing demand for faster results. We’re talking about slashing the time needed to complete a project in half, or more1.  

The strategic answer for agencies 

For agency owners, integration isn’t just about solving logistical issues, it’s a strategic move. By adopting integrated platforms like Research HX, you can: 

• Expand your capacity to handle more projects 
• Differentiate your agency as an innovator in delivering high-quality insights at speed 
• Make way for new revenue streams. Perhaps you could explore social listening or CX programs?! 

With integration, you’re not just keeping up. You’re staying ahead. 

Ready to explore the future of research? 

If fragmented workflows are holding your agency back, it’s time to explore the benefits of integration. Download our eBook for a wider view into the state of the market research industry and how tools like Research HX can transform your workflows. 

Download the eBook 

What’s coming next 

We’re going to explore each part of the research process over the coming months. Unpicking the potential integration, AI and automation can unlock for your agency. We’ll have more on:  

• Data collection 
• Sample marketplace 
• Panel management 
• Advanced analysis 
• Reporting 
• Visualizations 

References: 

1. 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.​ 

The need for speed: why efficiency matters for research agencies

In today’s market, agility isn’t just a trendy term—it’s your agency’s secret weapon. Clients want insights faster than ever to make those big moves, and old-school research methods just don’t cut it anymore. Enter agile market research: a slick, supercharged approach that dishes out timely, relevant data. By embracing agile methodologies, research agencies can shift gears effortlessly, giving clients what they need, exactly when they need it. No sweat, no hassle.

To put things into perspective, 71% of companies are now using agile methodologies to keep up with rapid market changes and growing client demands. But this trend isn’t just about speed—it’s about keeping things relevant, delivering accuracy, and staying ahead of the pack.

Automation: the secret sauce of efficiency for research agencies

Let’s face it—automation is your agency’s best friend. Forget the boring stuff; automation lets you focus on what matters. PLUS, you get to:

• Generate custom reports and visuals on mass (hallelujah!)
• Reduce human error for higher quality results
• Free up time by letting AI take care of repetitive tasks

For research agencies, it’s the secret ingredient that makes the magic happen. From data collection to analysis, automation tools are making everything faster, smarter, and a whole lot less painful. For instance, the cost of a typical online attitudes & usage survey has dropped by nearly 50% in the past five years thanks to automation—meaning you get more bang for your buck, and faster results.

Think of AI as an ace up your sleeve. AI-powered platforms are chewing through massive datasets in minutes, uncovering trends that even your most overly-caffeinated analyst might miss. In fact, the automation potential for data processing tasks in market research increased to over 90% last year with the development of generative AI, which means your insights are deeper, your delivery is faster, and you’ve got the edge that clients can’t get enough of.

Of course, as die-hard fans of Greenbook, we couldn’t resist sharing some key insights from the 2024 GRIT Business & Innovation Report. With top strategies for market research agencies aiming to up their agility in 2024 and beyond, here’s what they’re saying:

Embrace generative AI

Agencies are increasingly adopting GenAI to streamline and replace entire work processes, especially when it comes to analytics and strategic consulting. Because this allows for greater speed and efficiency, agencies who choose to embrace GenAI will be leading the way.

Foster stakeholder collaboration

Involving stakeholders early on in your agile journey (and throughout the entirety of the project cycle) helps to make sure that every move is relevant to wider business goals, while smoothing the integration of those all-important insights. The result? Far more efficient resource allocation! Read our thoughts on collaboration in an AI era.

Diversify service offerings

Full-service research models continue to attract and grow, providing a safe haven for companies post-pandemic. Offering a reassuringly diverse range of services allows agencies to scale and adapt to the different needs of their very unique clients.

Leverage AI for unstructured data analysis

GenAI excels in analyzing unstructured data, which is of course a key offering from most strategic consultancies and full-service providers. Utilizing AI for unstructured data analysis and text analytics is great for enhancing data interpretation and insight generation – something your clients will come to expect.

Embracing the future: 5 automation trends to watch out for in market research 

As well as essential strategies for research agencies, let’s take a peek at what’s coming up! These trends are set to take market research automation to the next level:

1.   Faster iterative testingQuick, iterative testing cycles are becoming the gold standard, allowing agencies to gather and act on consumer feedback in real time. This continuous refinement helps to make sure your clients stay ahead in fast-moving markets.

2.   Real-time data analysisBig data isn’t just big—it’s immediate. With real-time data processing, agencies can deliver insights exactly when they’re needed, allowing clients to make better, faster decisions.

3.   Seamless cross-functional collaborationGone are the days of silos (hoorah!). Leading agencies are encouraging a hefty dose of collaboration across their teams to streamline processes and deliver connected strategies that adapt to market dynamics in record time.

4.   AI-driven intelligenceArtificial intelligence and machine learning are no longer nice-to-haves—they’re essentials. These technologies allow agencies to uncover patterns, predict trends, and provide their clients with actionable insights that deliver enviable results.

5.   Mobile-first researchLet’s face it: we’re all glued to our phones, and with mobile devices dominating how consumers engage, mobile-first research isn’t just smart—it’s expected. This approach will help forward-thinking agencies to connect with audiences en masse, to collect richer, more relevant data.

The Forsta advantage for research agencies

We get it: agencies need the right market research tools to thrive in this brave new agile world. That’s why our platform works hand-in-glove with automation tech, giving you everything you need to master modern market research, without the stress.

Forsta’s integrated automation tools have helped research teams to slash project turnaround times, while keeping quality top-notch. That means you’re delivering those golden insights exactly when they’re needed—and looking like a superstar while you’re at it. 

So in case you missed it, this is your sign to take FULL advantage of automation in market research, from survey design to reporting and visualizations. Also, Forsta’s extensive API cababilities allows you to establish your own integrations. Customise and level up your market research automation to suit your business strategies. Time for a free demo? We think so too.

Read more about Forsta’s take on the coming golden age for research agencies and the key strategies being implemented in our white paper.

This, dear reader, is the battle on bias—a very human problem that has invaded even the most advanced artificial systems.

If you’re a market researcher, you’re probably on the edge of your seat because you’ve seen this movie before. You’ve faced the beast of bias head-on and wrestled it into submission (most of the time). But here’s the twist—now the beast is powered by AI. So, with that in mind, let’s take a closer look at how AI is learning, why it’s picking up our bad habits, and how market research is uniquely positioned to lead the charge.

Understanding bias: the sneaky culprit in artificial intelligence

Bias. The word itself has a slightly sinister ring to it. But bias is simply the tendency to lean in a particular direction, often unfairly. In market research, bias can mean skewed survey results, inaccurate customer insights, and misguided decisions. Now take that same idea and toss it into the world of AI, and things start to get somewhat problematic.

Just like kids, Artificial Intelligence algorithms models learn by example. We feed them data—tons of data—and they absorb patterns from it. If that data has a skew, if it contains the biases of the people who made it, then congratulations: the AI has now learned to be biased, too. It’s a classic case of ‘garbage in, garbage out.’

Remember Google’s Gemini AI? Its image generator produced some questionable representations—not because the algorithm had a secret agenda, but because it learned from a pool of data tainted by years of human stereotypes and contextual slip-ups.

Bias in AI doesn’t just result in embarrassing outputs, though. It can perpetuate stereotypes, enforce social divisions, and lead to fundamentally unfair decisions. For market researchers, whose bread and butter are data accuracy and consumer insights, AI bias could turn into a nightmare—unless we stay ahead of it.

Bias awareness for market researchers

The truth is, bias isn’t new for market researchers. You folks have been dealing with it for decades, and you’ve developed both a sixth sense and rigorous approach honed through experience for it. Every time a survey respondent gives a questionable answer, or a focus group spirals off-topic because everyone is nodding in agreement—that’s bias showing its hand. Market researchers know better than anyone that what people say they do and what they actually do are often oceans apart. So, you’ve learned to anticipate, adjust, and double-check your findings.

That bias-busting instinct and scientific rigour is what makes market researchers uniquely equipped to grapple with AI bias. If you can understand how bias affects a survey, you can understand how it affects an algorithm. 

So, why does this matter?

Because bias in AI isn’t just inconvenient—it can be outright damaging. When biased data trains an AI model, it can generate outputs that reinforce harmful stereotypes. Take, for instance, image-generating AIs that overrepresent male figures in professional roles while depicting women in domestic settings. Or facial recognition software that struggles to recognize people of certain ethnicities with the same accuracy as others—the consequences of which can be deeply troubling.

Imagine an AI providing a company with skewed market insights—say, overlooking a specific demographic because it doesn’t understand the nuance of their preferences or needs. That’s not just bad business, it’s unethical. It means missed opportunities, poor representation, and potentially alienating whole communities. And, let’s face it—if AI is meant to be our super-intelligent helper, it shouldn’t be enforcing 1950s-era stereotypes.

How market research is ahead of the curve

Market researchers have a unique advantage when it comes to using AI ethically. You’ve already got a well-honed radar for bias mitigation, and you’re used to applying rigorous standards to ensure data quality. You know the importance of diverse sampling, asking the right questions, and avoiding leading questions. These principles are just as applicable when working with AI.

In the world of market research, you wouldn’t dream of putting a biased survey in front of your audience, so why feed a biased dataset to an AI? The key here is realizing that AI isn’t magical; it’s just a reflection of the data you give it. It can’t rise above the quality of the data. But with vigilance and good practice, AI can become an incredibly powerful aid, not just for streamlining research but for enhancing accuracy, avoiding blind spots, and uncovering insights that even the sharpest human eye might miss.

Strategies to avoid bias in AI: tips for market researchers

Now, let’s get practical. If AI is learning from us, how can we teach it to be better? Here’s how some of our market researchers are tackling AI bias head-on:

1. Diverse data: one of the main causes of AI bias is training on a dataset that isn’t representative. The more diverse your data, the better the AI will understand and generalize its findings. Remember, the diversity of your training data should reflect the diversity of your target population.
2. Watch for hidden bias: some biases are easy to spot—like an overrepresentation of a certain group—but others are sneakier. Think about language, context, and even cultural references. Bias can creep in from the way questions are phrased, or from unbalanced datasets that favor one particular group’s experiences. Market researchers are already familiar with rephrasing questions to eliminate bias; now it’s time to rephrase data.
3. Transparency in algorithms: AI models are notoriously black-box-like. If you’re using an AI tool, it’s important to work with providers who can explain what’s going on under the hood. Understand how an algorithm reaches its conclusions, and you’ll be better positioned to evaluate the reliability of those conclusions.
4. Human review: AI can crunch data and spot trends, but it’s the human touch that contextualizes these insights. Market researchers should always serve as the final filter, reviewing AI-generated findings to make sure they’re accurate and free from harmful bias.
5. Expectation management: AI is powerful, but it’s not infallible. Understand what it can do and, more importantly, what it can’t do. An AI can summarize mountains of data, but it might miss the subtlety of human emotion. As market researchers, part of avoiding bias is knowing when to trust your own instincts and experience over an AI’s recommendation.

Developing a framework of how to leverage AI for success can also prove hugely beneficial!

The future: can AI learn from our good side?

Here’s the good news: AI is not doomed to be forever flawed. It has the potential to be our most unbiased teammate yet, but that’s going to require us to be responsible data curators and savvy AI handlers. As market researchers, you already possess a crucial skill set—you understand people, you’re careful with data, and you know how to turn insight into action. When AI learns from the best of human practices, it’s capable of producing insights at an unimaginable scale—insights that are richer, fairer, and, ultimately, more helpful.

It’s on us all to make sure that AI’s education is a good one.

How Forsta can help

With a wealth of experience in avoiding the pitfalls of human biases, we have the power to ensure that AI remains a tool for good—not an amplifier of our worst tendencies. Bias awareness isn’t just an ethical checkbox; it’s the secret sauce that turns AI from a fancy calculator into a revolutionary force for understanding human behavior. Our advanced technology, superior data processing, and flexible reporting capabilities empower you to harness AI effectively, fueling profound human understanding while safeguarding fairness and integrity.To find out how Forsta’s industry-leading platform can banish bias to reveal more accurate insights, book your demo today.

The shiny, caffeine-fueled world of agile market research deserves another chapter. A lot has changed in recent years and only agencies swiftly adopting new strategies and technologies into their agile market research processes can stand the test of time.

What is agile market research?

First things first, let’s strip away the jargon. An agile approach is all about being adaptable, quick on your feet, and ready to change your direction based on real-time feedback. Imagine you’re in a kitchen whipping up a new dish. You taste, you adjust, you add a pinch of salt, you kick up the spice, and eventually, you serve up something spectacular. That’s the agile methodology research—testing, learning, and improving fast. Let’s be real, you’re likely already doing this.

Gone are the days of sitting through lengthy projects that take months to deliver insights. Agile means working in small bursts, collecting insights, and iterating as you go. This means you can gather feedback, analyze it, and implement changes in real-time, keeping you in sync with your customers’ ever-evolving needs.

Why is agile market research important today?

Because the world’s moving at lightning speed, and your insights need to keep up. Consumer tastes, preferences, and even entire behaviors change in the blink of an eye (the term ‘micro trends‘ isn’t trending for no reason!). In 2024, adaptability is king. Clients expect lightning-fast, personalized results to help them understand their target customer’s needs, desires, behaviours in this moment. Waiting around for an annual report isn’t going to cut it anymore.

Right now, some of the hottest trends influencing agile research can be found in generative AI tools, which are helping brands to draft surveys quick-sharp and make sense of open-ended feedback. And thanks to advancements in behavioral science, you can lap up gold-plated insights into not just what people say they do, but what they actually do. More brands are also adopting agile to test micro-campaigns on platforms like TikTok or Instagram—seeing what sticks before throwing big bucks behind a concept.

Tech, agile and AI

We’ve said it before and we’ll say it again; picking the right tech is make or break for AI quality and a lot of the methodologies you can reliably work with. With automated research platforms at your disposal, queries covered by traditional market research can be answered accurately, and without costly delays.

Speed is no longer a threat to decent research design: researchers aren’t facing a choice between time and quality; and this is only going to improve as AI takes over laborious tasks and speeds up each stage of the research process. The rapid road from design to insights, paved by automation and AI goes hand in hand with Agile. Analyzing data almost instantly or updating visualizations as the research process develops leaves space for iteration and seriously informed decision making.

Agile research in action

Let’s make it a bit more concrete. Here’s what agile approaches for market research projects look like in action:

1. Sprint surveys: instead of putting together one big, cumbersome survey, agile research uses smaller, bite-sized surveys over shorter periods. Picture this: you run a two-week campaign to test a new ad—rather than waiting months for results, you get quick hits of data that help you to adjust before it’s too late.
2. Continuous feedback loops: agile is an iterative process. Just like having someone poke their finger into your half-baked cake and tell you it’s ‘not quite ready yet’, agile research relies on continuous input from real people. It’s not about one major data drop; it’s a conversation; an ongoing back-and-forth that helps you nail it.
3. MVP approach (Minimum Viable Product): let’s say you’re developing a new snack. Instead of rolling out 50 flavors nationwide, you pick three, test them with a small group, tweak the recipe, and then go big. Agile research minimizes the risk of major flops while maximizing the chances for success. Ta-da!

Examples of agile market research done right

Who’s crushing it when it comes to agile market research methodology? So glad you asked!

LEGO

Yeah, those colorful bricks you probably stepped on a few times in your life. LEGO has mastered agile research by co-creating with their fanbase. They’ve got an online community where members give feedback on new sets and suggest ideas. It’s like agile innovation at playtime—listening, responding, and keeping those little builders (and their parents) delighted.

Monzo

This UK-based digital bank has tapped into agile research by using social media and community engagement to rapidly test new features. Got an idea for a banking app update? Throw it out to the Monzo community, collect feedback, and iterate. It’s why they’re leading in customer satisfaction: they actually listen, in real-time.

Spotify

Spotify loves a good test. Agile market research is the engine behind their playlists, UI updates, and feature rollouts. Whether it’s testing how people react to a new design or experimenting with AI DJ features (spoiler alert: it’s pretty cool), they’re constantly refining the experience by listening to their users.

The benefits of agile market research

So, why bother going agile?

• Speed: the obvious one. You’re getting relevant insights as things happen. No more waiting around
• Customer-centricity: agile research keeps you closer to your audience. Instead of speaking at them, you’re talking with them—a surefire way to ensure you’re on the right track
• Flexibility: because you’re learning in real-time, you’re not chained to a singular approach. Something’s not working? Tweak it. Test something else. No fuss
• Cost-effectiveness: nobody likes a flop, especially a costly one. Agile research helps to prevent major failures by catching small missteps early on

How Forsta can help

Ready to get your agile on? We have everything you need to make your market research projects an instant hit. Our market research platform is designed to get you real-time insights faster than you can say “iterative improvement.”

• Run agile surveys: get instant feedback with agile, bite-sized surveys that help you to iterate quickly and effectively. No more waiting around for months; instead, get the actionable insights you need, as and when you need them
• Track audience sentiment: our tools let you continuously monitor how your audience feels, providing you with a constant pulse check that keeps you on track (and ahead of the competition)
• Integrated feedback loop: our platform makes it easy to create an ongoing conversation with your audience. Gather feedback, tweak your approach, and watch as your results improve—all in real time
• Qualitative & quantitative mastery: combine qualitative insights with robust quantitative data to get a 360-degree view of what your audience wants, thinks, and feels

In short? We make sure you’re not just keeping up, but staying ahead, understanding your audience, and delivering exactly what they need, when they need it.

Is agile market research right for you?

If you’re sitting there wondering if agile research is for you, the answer is (most likely) yes. If you’re in an industry where customer preferences change quickly, or if you’re trying to innovate and avoid expensive flops, agile is the way to go. And you don’t need to be a massive brand to get started. Agile research is scalable (that’s part of the joy).

Start small, see what works, and go from there. 

Ready to get started? Request a demo to discover how Forsta’s market research survey software can make your market research magical.

Let’s explore how empathy and AI can work hand in hand to produce incredible insights.  

What is human-centered design? 

It’s the secret to superb insights. It’s the power behind faster, better, more holistic data. And it starts with the right mindset. You see, human-centered design (HCD) is more than a methodology, more than a tool set, it’s an empathetic focus and a problem-solving technique that prioritizes your participants. And it’s something you already do. 

A human-centered design approach means you frame your problem or question from the perspective of your users, not technology. Traditionally, this would be thinking about user experience; “how can we help our customers or users achieve their goal”, rather than “what updates can we make to our app”.  

How does that work with market research?  

In market research, HCD means taking a step back to understand your audience. HCD goes behind numbers, it’s about telling the story of the people behind the data and developing a deep empathy with your participants. This thought process is your lifeblood, what you excel at, so what might it look like in a HCD framework?  

And at the end of the day, we’re all about action. Data itself is only as good as the resulting actions and changes that drive efficiency and improvements. HCD is a way of thinking about and handling data. The end goal is to see the human behind the data and prove the importance of your insights.  

MR human-centered design principles  

The human-centric approach enables organizations to pinpoint the right problems, understand interconnected behaviors, and implement impactful ideas. 

Let’s look at the principles of human-centered design for market research: 

• People-centric approach

Users’ needs, paint points and preferences are held in focus throughout the research process. Using a human-centered approach paves the way to more intuitive, accessible, and more impactful results. 

•  Solving the right problem

Stopping at the surface can leave you short changed. Digging down to the roots of your participants’ problems can bring to light solutions that truly resonate with your clients and their audience.  

• Understanding everything as a system 

Each experience, design choice, and question is part of a holistic human experience ecosystem. Zooming out to consider the broader context allows for more creative, and effective strategies.  

• Small, simple interventions 

The unsung hero of HCD, an iterative process. A trickle of tiny, speedy changes carves the long-term route to a waterfall of meaningful changes that improve results and experience.  

What’s AI got to do with it?  

When data points and technology reign supreme it’s easy to forget that there’s a flesh and blood being behind the results. And that you can reap rich rewards by peeling back the layers. AI tools can further this detachment, or work with you to give you time and capacity to get to the bones of your participants’ perspectives. These efficiency gains can be used to iterate and spend longer getting to know your audience. In turn, the more you can feed the AI about the personas you’re learning about, the better the analysis will be.  

Market research design teams are under pressure to churn out results. Taking advantage of AI tools is a natural, and highly beneficial development. Picking a tech platform that allows you to grow, to work with them, unlocks the potential to pull the picture together so you can make sense of the human behind the data.  

Data collection and analysis 

The teeter towards trouble can start with AI tools that only work with single sources of data or single analytic capabilities. Comprehensive understanding comes from collecting across qual, quant and third-party data sources. This holistic collection and analysis helps you identify patterns and trends that would slip the net if you rely on only one type of data. Things like combining sentiment analysis at scale, querying your own data and digesting qualitative data are all techniques AI can use to get you closer to your audience.  

Using a tool with narrow requirements negates any work done to understand the audience at a holistic level. The choice of champions is instead a flexible tool that can ingest data from multiple sources and apply a multitude of analytic packages. This allows human experience to seep through, driving holistic and unique insights.  

Intelligent iteration 

AI speeding up the analysis process means you have time to iterate. Get closer, and closer each time to the ultimate goal, and to understand the why behind participant behavior and thoughts. Involving stakeholders or even participants themselves in the research design process can inspire iteration while keeping the research grounded in the human experience.  

Storytelling 

Your clients are human too and the final part of the process is driving action. The way to truly engage your audience is through stellar storytelling. The first chapter is always understanding your audience, and pictures speak much louder than words. 

Persona development is a central part of this. Deeply understanding your audience from their pain points to desires, across a swath of sources proves you have seen beyond the tech and met them eye to eye.  

If collating all these data sources to present highly customized deep dives gives you palpitations, have no fear. Automating these outputs saves you hours of manual work. Again, the right tech amalgamates your myriad of sources to produce slides, images, or other desired deliverables. The beauty of a product like Forsta Visualizations is role-based dashboards. See global results or click into the micro-details that make up the bigger picture.  

Final thoughts  

HCD is an essential mindset. It’s the benchmark between data and impactful insights. As the collaboration between humans and technology becomes more tightly woven, keeping a beating heart as the center focus of your method and tech choices will open the doors to increased efficiency and dazzlingly rich pictures of your audience.  

Evolve into the next generation of market research agencies by harnessing the power of AI. Join us as we touch on the world of AI for market research and how you can fast-track your expert results for clients.  

Make AI tools work for you 

Global AI is growing at a compound annual growth rate (CAGR) of almost 40% and shows no sign of slowing down. In fact, global adoption by organizations is set to expand at a CAGR of 37% through 2030. On the Market research side, AI revolutionizes analysis. Faster, more accurate insights can be gleaned alongside automating time-consuming repetitive tasks. Integrating AI into your agency is critical to avoid being left in the wake of modern thinkers.  

Superspeed data crunching 

AI processes vast amounts of data quickly, revealing trends and patterns in real-time. Automating routine, manual data analysis can be a secret weapon to increase efficiency.  

AI can analyze large data sets almost instantly. Also accurately, with the right tools. Humans might love patterns, but AI adores them. Complex connections or trends in data that are overlooked by human eyes can be detected by AI analysis. By automating large data analysis, you can save huge amounts of time and start working with quality results that matter most to your clients.   

Data cleaning 

Similarly, automated cleaning and removal of personally identifiable data is an easy-to-adopt case. Especially for cases where bias may be a concern, having this removed before humans get to work carefully navigates this issue.  

Query your own data 

Possibly the biggest industry-shaking potential comes from insight synthesis and democratization. AI’s ability to stitch together summaries and even new results is truly game-changing stuff. With a brain built on your research, any user would be able to query the model and get new insights in a format that suits them best. Self-service and persona-specific results don’t have to be a slog to produce. 

Qualitative summaries 

With the increased need to bring non-survey experiences into market research, AI tools can keep unstructured text from open-ended questions and video/audio analysis up to speed. Older, existing approaches require training data to start which won’t be available for new research. Generative AI can be ready to mine at the touch of a button. Automated transcripts of interviews and instant summaries of swaths of qualitative data can unlock options where existing or manual methods would have been prohibitively time-consuming. 

Generative AI enables researchers to explore reviews, feedback, behavior, and sentiment data in addition to audio and visual. The insight options are potentially limitless. Just a few ways to take advantage today include:  

• Predicting future behavior 
• Addressing changing markets by identifying new product needs 
• Determine response to ad messaging in advance 

Personalized results 

Clients are desperate for personalization recommendations, and AI makes it easier than ever to segment audiences and deliver actionable insights. With these data-driven blueprints, you can empower your clients to quickly personalize their campaigns, ensuring they stay ahead in the competitive landscape. They’ll appreciate your strategic guidance and the value these tailored recommendations bring to their success 

Limitations 

Before this all sounds too good to be true, we need to stay realistic about the limitations of AI. Firstly, they’re tools. In the same way a blacksmith uses a forge, a chef uses a knife and an archer uses a bow, AI tools would be useless or even harmful without the proper subject expertise.  

Human oversight is still required to check the results, and AI isn’t quite ready to make strategic decisions.  

Bias in data and algorithms 

Ensuring AI doesn’t perpetuate existing biases is one of the biggest current concerns. These systems are based on the data they’re trained on, and the data they’re being fed. The source of algorithmic bias is often in these, as well as historical and social contexts that weren’t picked up. For example, the unfortunate case of Apple’s AI rejecting female credit card applicants due to male-dominated training data, not credentials.  

AI doesn’t understand bias without context training and any biases already present are likely to be amplified. If the input is flawed, the output will be too. To prevent this, choosing a platform that’s been trained on diverse datasets and contexts is required as well as bias-preventing research practices. Also, using Retrieval-Augmented Generation (RAG) can be helpful here, which only queries the data you’ve fed into it. Of course, a knowledgeable human touch to consistently monitor is also indispensable.  

Integration with traditional methods 

Combining AI with traditional research methods requires careful planning and execution. No leader wants to bring forth disruption, yet many agree that AI will put jobs at risk. Some argue that roles will flex around AI, after all the tech still needs expert guidance for optimal analysis. They are task-bots that need to be appropriately slotted into the whole research process, to specifically fit the needs of you and your clients.  

There are few widely accepted standards for introducing AI into your research. Existing processes like the Cross-Industry Standard Process for Data Mining (CRISP-DM) is a comprehensive guide but has limitations in itself. If your use-case doesn’t fit an existing framework you must dig for best practices and pave the way yourself.  

Data privacy and security 

Safeguarding customer data is paramount in the age of AI. Systems store vast amounts of data to function and new inputs can be shared and re-worked in connected spaces. It’s essential to ensure data is stored safely and participants’ privacy is protected.  

These are not new concerns in the world of market research. We’ve discussed survey fraud and how to tackle it on many occasions and we can learn from these existing methods. With wider access, something like implementing an S2S integration to battle ghost completes for AI systems will be necessary to maintain security. AI can’t do this themselves, so get ready to welcome AI privacy specialists.  

Forgetting humans 

It’s easy to get swept up in the excitement of AI, especially for those who see it as a replacement rather than a tool. While AI offers incredible possibilities, it cannot replace the unique human touch that sets your business apart. AI is imperfect and still requires knowledgeable oversight to ensure proper application. It enhances, but doesn’t replace, the human experience, which is the beating heart of any successful organization. Ultimately, it’s the specialists, with their intuition and empathy, who turn AI’s raw data into insights that truly resonate. 

The opportunities to enhance market research analysis with AI are undeniable. It’s clear these tools do work to save time, money and give your clients what they are after, much faster. The explosion of options, sensational news and interesting takes on application may add fuel to the fear fire so employ a level head and fully organic guidance. The cost of new tech can be high, but with the benefits charging your results the long-term costs of not doing so may invoke disaster. Adding AI tools is about a careful balance of human expertise and finding the right fit for the job.