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

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

The pressure isn’t new, but the environment is

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

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

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

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

Speed only matters if it leads somewhere

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

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

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

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

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

What AI is already good at

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

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

What AI still does not replace

This is the part worth holding onto.

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

This moment is no different.

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

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

AI efficiency is not the same thing as value

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

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

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

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

What should market researchers do now?

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

From AI efficiency to impact

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

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

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

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

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

That is the AI efficiency that matters.

And the uncomfortable thought arises: When everyone has access to the same AI, what differentiates you as a researcher?  

Because speed is no longer a differentiator. Automation is no longer a differentiator. How do you prove the value of insights in a world that wants answers faster, cheaper, and yesterday?  

In a world where AI makes the mechanics of research easier for everyone, the real competitive edge is something only humans can provide. It’s where the most forward-thinking insights professionals are now investing. And it’s why Research HX has become the backbone for researchers delivering richer insight, stronger client value, and truly differentiated expertise. It’s human experience (HX). It’s not a philosophy or just a way of thinking, it’s a leadership strategy that can define whether insights gets a seat at the table.  

The human experience imperative

Behind every data point is a person – a real human being with context, history, emotions, and contradictions. But behind an insights workflow is pressure. 

Pressure to: 

• Prove ROI  
• Deliver faster with shrinking teams 
• Eliminate silos to improve workflows and access 
• Turn “interesting findings” into decision-ready insights 

AI can accelerate research, and even help relieve some of these pressures, but it can just as easily magnify these weaknesses as AI takes off some of the lift. And that’s where Human Experience becomes essential. In an AI-saturated market, HX becomes your differentiator as it focuses you on becoming more effective. 

Read more: Human Experience in the AI era: A guide for insights leaders 

When AI is everywhere, humanity becomes your strategy

AI is extraordinary at speed, structure, and scale. But it’s fundamentally limited when it comes to understanding people. Which means the competitive advantage for insights professionals shifts: 

• Where AI creates efficiency, HX creates meaning that survives the executive room 
• Where AI automates tasks, HX elevates insight into decision-ready dashboards 
• Where AI uncovers patterns, HX uncovers purpose and the strategic role of the team 

Stakeholders are choosing partners who help them make sense of the complexity – partners who interpret, not just report. Partners who bring clarity and humanity to decisions. They don’t ask for more charts alone; they ask, “What should we do?” 

That’s what HX unlocks. 

What HX looks like for modern researchers

Forsta’s HX framework embodies seven capabilities that help organizations understand people more deeply:  

1. Observe: A fuller, richer picture of people. Not just quant signals, but stories, behaviors, contradictions, and context. 
2. Converse: More natural, conversational research experiences that generate higher-quality insight. 
3. Empathize: A level of emotional understanding AI simply can’t replicate, and the reason clients trust human-led interpretation. 
4. Connect: Breaking down silos between qual, quant, CX, video, behavioral data, and unstructured text. 
5. Predict: Future-focused guidance that blends human intuition with machine-scale analysis. 
6. Inspire: Sharper storytelling that moves stakeholders, not just informs them.
7. Act: Insights that lead to concrete, commercial action – and make you indispensable. 

This is what HX looks like when Insights is a decision partner. Directions become clearer. Storytelling becomes stronger. Recommendations become more resonant. And relationships become more strategic. 

How Research HX powers the HX advantage

HX is the mindset. Research HX is the machinery that powers it. Built by researchers, for researchers, Research HX helps agencies and in-house insights teams deliver on the full promise of Human Experience without adding complexity or cost. 

Core advantages include: 

A unified platform for all your research 

Quantitative and Qualitative research in one. Surveys, focus groups, digital diaries, mixed mode (CATI and CAPI), all live in one connected environment. No more fragmented workflows. No more silos between teams or methods. 

End-to-end automation where it matters 

Research HX accelerates scripting, setup, QA, fielding, and reporting – allowing your researchers to focus on interpretation, storytelling, and client impact. 

Visualizations and storytelling tools clients love 

The AI-augmented platform turns data into clear, compelling narratives that clients can quickly interpret and act on. Because better storytelling = stronger impact.  

High-quality outputs at greater speed 

Research HX helps agencies deliver more projects, faster, without compromising quality. A crucial differentiator as timelines shrink. 

Trusted by the industry’s leaders 

Many leading global research teams rely on Forsta solutions. Not because of speed alone, but because Research HX helps them deliver truly human insight at scale. 

Services on hand when you need them most 

A team of experts with decades of experience to rely on when you need a helping hand. Research HX is more than just the technology, its about the support and expertise you get.  

How researchers can adopt the HX mindset today

Ready to dive in? Here’s a few practical steps to get you started: 

• Audit where your research is currently ‘sea-of-data-first’ rather than ‘human-first’
• Break down silos between qual, quant, CX, and analytics teams 
• Implement unified workflows using Research HX 
• Use AI to accelerate processes, not replace humans in interpreting meaning
• Invest in storytelling as a core insight capability 
• Develop an HX framework as your consistent, agency-wide operating model

The insights teams that win will be the most human

Technology will continue to level the playing field. Automation will become expected, and AI will become ubiquitous. What won’t change is the need for human judgment and context. 

The insights teams who embrace HX will deliver deeper insights, build stronger relationships, and stand apart in a market where sameness is the real threat. Not because it sounds good, but because it’s how impact happens. The future of insights is human-guided. Human-interpreted. Human-experienced. And the insights professionals who never lose sight of the human behind the data? They’re the ones who win. 

Find out more about Research HX by requesting a demo. 

Now the mystery has revealed itself, the latest development in a diverse career in insights. I have been lucky enough to have already experienced three big waves in research: The shift from CATI to digital, the shift from siloed surveys to integrated data, and now the rise of AI across the entire workflow.

Being in insights leadership throughout these waves myself has taught me something many might think is surprisingly simple: The leaders who make it through are the ones who obsess over how humans consume data, not just how we collect it.

Let me explain, starting where it all began: With a pile of SPSS files and some very honest client feedback.

“These SPSS files are great, but…”

I started my career in 1999 at a fieldwork house, scripting CATI surveys and pushing a lot of data through telephone and early web interviews.

Every time I sent the SPSS data off, I heard a version of the same line: “These SPSS files are great, but we need to consume this data in a faster, different way.”

That sentence has probably shaped my career more than any job title. Because it constantly reminded me that nobody buys raw data. They buy the ability to use data. So that was my mission. That simple complaint from clients is what led me to found Dapresy in 2003 (now Forsta’s Visualizations). The objective was very clear, we said that we are only going to be good at doing one thing: Taking any kind of market research data and efficiently visualizing it for different stakeholders, but always easy to consume.

And it worked! From one person in Sweden to more than 100 people operating in regions across the globe, that mission has scaled.

Looking at the industry today, I see the same pattern. Esomar’s latest breakdown shows roughly a third of the 142 billion dollar ecosystem is now driven by companies whose primary business is analytics platforms and software, not classic full-service research. Even now, the firms that thrive are not necessarily the most diversified. They are usually the ones with a very sharp answer to a very specific problem.

If your internal stakeholders or agency clients still feel like they are receiving data instead of answers, then the technology may have moved on, but your insights leadership has not.

For future leaders, that is lesson one: Specialize around a value outcome, avoiding buzzword. Your stakeholders will remember your ability to make data usable long after they forget which methodology you used.

Staying in the research lane while the industry fragments

Over time, Dapresy partnered with private equity, merged with Confirmit, joined forces with FocusVision and eventually became part of Forsta and was renamed Visualizations.

The product portfolio grew. The company expanded into CX and EX. The acronyms multiplied, as they always do.

But one thing didn’t change. The golden thread in my journey has always been that I’ve been focusing wholeheartedly on market research. I have always stayed in the lane in market research because creating offerings and value propositions for market research professionals really requires a strong focus, and this did not limit us.

The lesson for tomorrow’s leaders: Pick your lane, then build relentlessly for the messy reality of that lane.

The balancing act

Messy reality is the key here. Leaders can become too overprotective and inflexible. There’s a careful balance between staying in your lane, fostering a great team that excels alongside you, and focusing so hard on the straight ahead that you forget to use the wheel when the twists and turns come along.

The successful leaders, those who have stuck around alongside me, are those who can commit to their mission and evolve with the market at the same time.

The current balancing act leaders are facing is how to enhance their value proposition, keep the talent they have and add AI in a meaningful, sensible way. Given what we see in AI investment and expected productivity gains, it is obvious that AI will be deeply embedded in how we work.

I would think of research agents as junior colleagues who never sleep but still need direction. If you do not give them clear instructions, your outputs will reflect that. At the same time, your human team members can find deeper or expanded ways to add value. Use the time savings to make the most of their unique skills, their expertise that AI can’t replicate, all for the better of the overall mission.

Shifts that shaped how I think about change

I sometimes joke that I have lived through three careers in one, because the underlying technology has changed so dramatically. This has also given me perspective on what’s happening now in research. I don’t see AI jeopardizing the market research industry. It is one of the trend shifts that happens, like others, over the last 20 to 30 years.

There’s always change, and change needs to happen because that’s the way you stay modern and follow the latest technology.

For future insight leaders, the question is not “Will AI change things?” It is “Do you want to be the one steering that change, or the one explaining it after the fact?”

Research agents

At Forsta, we talk more and more about something we call “research agents.” These are AI-driven helpers that sit inside your workflow and take on specific tasks that used to be manual.

AI already supports us when it comes to repetitive tasks, finding conclusions in big data sets, and guiding us toward where to focus. In practice, I see three big changes insights leadership should be aware of.

How we design surveys

For more than 20 years, we have been scripting surveys manually by dragging and dropping, copying and pasting Word text into survey tools.

I expect that to change: We will complement that process with a more automated process where you collaborate in a Word document, then feed it to a tool that automatically scripts the survey questions.

We will also use AI to be inspired about what questions to ask and which target groups to reach.

How we prepare and process data

Once the data is collected, there is a familiar checklist of tasks: Cleaning, coding, weighting, structuring, building derived variables, and preparing outputs for reporting.

There will be AI helping us to do the repetitive tasks when it comes to preparing data and being ready for reporting.

That aligns with what we see outside research. Thomson Reuters’ Future of Professionals report finds that knowledge workers expect AI to save them up to 12 hours per week by 2029, with four hours freed up in the next year alone.

Think about what your team could do with an extra working day every week, without hiring anyone.

How we create value

Most important of all, research agents should give you back the one resource you never have enough of: Time to think.

That does not mean your job becomes easier. It means your excuses become weaker. If AI is clearing the undergrowth, you are expected to build the next level and come up with new ways to provide value.

What I would do if I were starting again in 2026

I disagree with those worrying about research dying, research roles disappearing. Some things will be different or more challenging, but overall, if I were to start over, I would still choose market research as my lane.

The tools will change. They already have, several times. The acronyms will change. They always do. The constant, for me, is the responsibility: To help people understand.

The future of insights leadership is not about predicting the next technology wave. It is about using every wave, old and new, to move clients and stakeholders from data to decisions with more clarity, more speed, and more humanity.

Find out more about the result of Forsta’s mission by requesting a demo.

Listen to more from Tobi: The Founders & Leaders Series, Insight Platforms.

Why ‘humans in the loop’ is non-negotiable

When AI is used in small, contained ways, errors are easier to spot. But as reliance grows, mistakes become harder to detect and easier to propagate. Hallucinations, bias, and over-generalization can move through workflows unnoticed if teams aren’t actively supervising outputs.

Crucially, accountability doesn’t shift to the machine. Whether research is conducted for a client or used to inform internal decisions, responsibility still sits with people. Research workflows augmented by AI will need clear checkpoints where human judgment is applied.

This has spurred the concept of “AI high performers,” a very small subset of people whose bold ambitions have resulted in higher success rates in integrating AI into their research workflows, roles and workplaces. Their ambition is focused on using AI to drive growth, innovation, and reduce costs, and it’s working. So far, the difference between winning with AI and not seeing tangible results is a reimagined workflow to make the most of the benefits of the AI’s toolset, a clearly defined process, and proactive risk mitigation by specifying how and when model outputs need human validation. To put numbers to this, 64% of those classed as AI high performers have a rigorously designed process, compared to just 23% of the general workforce using AI.

AI supervision clearly isn’t an optional layer; it’s where setting clear objectives and taking responsibility for thoroughly thought-out implementation can set you apart. You can then further differentiate yourself and bring research expertise to enhance the quality made possible by time clawed back by automation.

What changes when teams rely on AI day to day

When AI becomes part of everyday research operations, subtle but significant shifts arise. One of these shifts is increased pressure of quality validation as research cycles get faster. When outputs are produced more quickly, there’s a desire to move through the next steps at the same speed. This creates a real risk: Confidence in the speed of AI outputs can outpace confidence in its raw usability.

Volume also increases, adding to this pressure pot. With speed comes space for more. More studies, more cuts of data, more summaries, more outputs. Without stronger prioritization, teams and stakeholders can find themselves overwhelmed by insight rather than empowered by it. The human research workflows shift to contextual interpretation and picking what’s useful for each stakeholder out of the data ocean.

We can learn a lot from looking at the wider impact of AI on the workforce outside of market research. A recent McKinsey study showed that AI is expected to reshape headcount in roles adjacent to insights work. For example, in knowledge management, expected employee reduction continues from 16% (observed last year) to 27% (expected this year). In marketing and sales, the trend continues from 18% (observed, last year) to 32% (expected, this year).

However, this doesn’t necessarily mean a talent drain, but perhaps a shift in a new direction. McKinsey also expects client-facing roles to grow by 25%, while non-client-facing roles to shrink. With this in mind, there are some skills a researcher can brush up on and leaders can encourage to meet new stakeholder expectations.

This matches the behavioral shift required by my new expectations. Automation bias (the tendency to trust machine output over human judgment) becomes more pronounced as AI feels increasingly competent. Teams must actively counter this by building habits of challenge, sense-checking, and contextual review into their research workflows. In short, less manual effort means the same level of responsibility shows up in different places.

Where synthetic data fits (and where it doesn’t)

Synthetic data is one of the more talked-about developments in AI-enabled research, and also one of the most misunderstood. Used carefully, synthetic approaches can extend analysis, fill gaps, and support early-stage exploration. But they’re not a replacement for real human input.

Today, a pragmatic guideline shared at Esomar Congress that complements their recent AI Code is that no more than 30% of a research project should rely on synthetic data. And the reason is simple: Synthetic data is only as good as the data used to generate it. Models extrapolate from the past, but the past isn’t always a reliable predictor of the future. Particularly in fast-moving markets or when cultural context matters.

How agentic AI may reshape research workflows over time

Looking further ahead, agentic AI has the potential to reshape research workflows more fundamentally.

Rather than a single system doing everything autonomously, agentic AI points toward multiple specialized agents supporting different stages of the research process. One agent might assist with survey design, another with data preparation, another with analysis, and another with reporting.

These agents still need direction, supervision, and integration into existing research workflows. They also need to be ready to plug into broader research systems without introducing fragmentation or risk. This is a recurring theme with new tools; integration is essential to mitigate risk and maintain a smooth workflow.

Explore more by listening to the Founders & Leaders podcast.

For most teams, agentic AI will arrive gradually. Early use cases will focus on well-defined, low-risk tasks, with wider adoption depending on whether these systems can integrate cleanly, support quality standards, and earn trust.

Read more: The hidden dangers of non-integrated AI

From hype to maturity: What the wider AI landscape tells us

Across industries, AI adoption has followed the same arc as any new technology: Early over-estimation of short-term impact, followed by more measured, value-driven implementation. Then initial experimentation gives way to consolidation, as organizations move from trying everything in an excited rush to focusing on what is delivering measurable results.

Adoption is also pretty uneven. Some regions and sectors are moving faster than others, and what works in one context doesn’t automatically translate to another. This pattern is already visible across industries. Recent research shows that while 88% of organizations report experimenting with AI, fewer than a third say they’ve achieved measurable business impact at scale.

What research teams should focus on next

In 2026, the research teams that succeed won’t be the ones chasing the most automation. They’ll be the ones enhancing their tried and tested research workflows with increased speed and layers of human intervention that build trust for stakeholders.

That means:

• Building clear supervision points into AI-enabled processes
• Strengthening data foundations and quality controls
• Maintaining data security during experiments (keep an integrated workflow and try external tools with dummy data)
• Exploring and preparing for future AI systems, but only when they’re ready to add value

Above all, it means recognizing that research doesn’t become better by removing humans from the process. It becomes better when human judgment is supported, not replaced by AI.

Want to find out more? Visit our solution page to book a demo with one of our experts to see how these tools could help you and your market research. 

But being AI-ready isn’t about mastering every new platform in preparation for the future it might bring.

It’s about understanding how the role of the researcher is evolving today, and where human expertise creates the most value in an AI-augmented world.

Across both market research agencies serving clients and in-house teams supporting functions like marketing, product, and innovation, the same shift is underway. AI is taking on more of the production work (the setup, processing, summarizing) and that’s changing what people then expect from researchers in return.

The researchers who thrive in 2026 won’t be defined by how quickly they execute technical tasks. They’ll be defined by their interpersonal skills and by the impact their data communication has.

From production to influence

Historically, much of a researcher’s value was tied to execution. Writing questionnaires, managing fieldwork, cleaning data, building charts, and manually creating reports were core parts of the job. Currently, the AI shake-up is starting as tremors, but the expectation is that this will become more pronounced as the year goes on.

On the agency side, automation is reducing the time spent on manual production, which raises client expectations elsewhere. Clients increasingly look to agencies for deeper insight interpretation. Turnaround time is highly competitive and what differentiates agencies is their ability to help clients understand what the findings mean and what to do next.

In-house, a similar shift is taking place. Insights professionals are moving beyond a service model (‘run this study’, ‘pull this data’), toward a more strategic partnership with teams like marketing, product, and CX. As AI accelerates access to data, internal stakeholders expect researchers to help connect insights to decisions, priorities, and outcomes.

In both cases, AI doesn’t remove tasks. It can take care of some and redistribute the rest. Time saved on production is reinvested in higher-value activities: Framing the right questions, applying insight to real decisions, and influencing action.

The skills that matter most going forward

As research roles evolve, the skills that matter most are changing too. We’re expecting to see a higher priority put on things like judgment, communication, and impact.

Business thinking

AI can surface patterns very well, but it can’t decide what matters to a specific stakeholder at a specific moment. Researchersincreasingly need to understand commercial context, organizational priorities, and decision trade-offs to position insights accordingly.

Communication and storytelling

As Research becomes faster and more accessible, the real challenge is no longer getting data but making sense of it. Researchers need to translate complexity into clarity, tailoring narratives for all stakeholders.

Judgment

AI produces output confidently, but not always correctly or appropriately. Knowing what to trust, what to challenge, and what to treat with caution is becoming a defining skill. This includes ethical awareness, and an experienced instinct for when something doesn’t quite add up and needs double checking. Ultimately, a human is always going to be on the line for every decision made, even when augmented with AI and automation so confidence in your judgement needs to be concrete.

These skills aren’t new, but they’re moving from ‘nice to have’ to ‘core requirements’ as AI takes on more of the mechanical work.

The new hats market researchers will wear

Many insights professionals already find themselves wearing new ‘hats’ alongside their existing responsibilities as AI reshapes some roles. These aren’t new or rigid job titles, but ways of working that are becoming increasingly common.

The AI Orchestrator

As AI becomes embedded across research workflows, someone must decide how it’s used (and where it shouldn’t be used at all). In this role, researchers act as orchestrators rather than operators. They decide which parts of the process can or should be AI-accelerated, where human review is essential, and how different tools and models work together, all in the name of better insights.

The Story Architect

When AI can summarize data in seconds, the human challenge shifts to contextually shaping it. Story architects focus on turning AI-assisted analysis into insights narratives that land. They decide what to emphasize, what to leave out, and how to structure insights so they resonate with different audiences, whether that’s a client, a marketing team, or a product group.

Story Architects think about visuals, structure, and format, using narrative to move people from information to understanding and ultimately empowering them to act.

The Creative Strategist

In this role, researchers help translate human understanding into practical ideas. AI can expand the inputs available (qual at the scale of quant, multiple data streams in one), but the creative leap remains human.

Creative Strategists work closely with innovation, design, marketing, and creative teams to make sure their insights can actively influence how organizations react.

The Coach

As access to research and data becomes more democratized, guidance becomes just as important as delivery. The Coach enables others to engage with insights effectively. They help stakeholders ask better questions, interpret findings responsibly, and avoid common pitfalls (like overconfidence in automated output).

Coaches also play a critical role in challenging groupthink and advocating for evidence-based decision-making.

The Product Manager

As research becomes more platform-driven and AI-enabled, someone needs to take ownership of the insight system itself. The Product Manager role focuses on how tools, platforms, and workflows are designed, adopted, and improved over time.

This role sits at the intersection of market research, technology, and internal enablement, and becomes increasingly important as insight systems grow more complex.

Read more: Human Experience in the AI era: A guide for insights leaders

Human-led market research with strong AI support

Today, most teams aren’t aiming for fully automated, human-free workflows. Nor are they rejecting AI. What many teams are moving toward is a middle ground: Human-led research, amplified by AI.

AI brings speed, scale, and efficiency. Humans bring context, judgment, empathy, and meaning. Together, they create better outcomes than either could alone.

What this means for researchers today

Becoming AI-ready doesn’t mean reinventing yourself overnight. You don’t need to become an engineer. You don’t need to chase every new tool. And you don’t need to have all the answers right now.

What does matter is leaning into the parts of the role that AI can’t replace: Interpretation, influence, and decision support. It means developing confidence in your judgment, sharpening how you communicate insight, and understanding how your work connects to real outcomes.

In that sense, AI-readiness is less about speed and more about clarity. Less about doing more, and more about doing what matters. The future market researcher isn’t defined by the tools they use, but by the value they create.

Want to find out more? Visit our solution page to book a demo with one of our experts to see how these tools could help you and your market research. 

AI isn’t progressing in a smooth, predictable line. It’s advancing unevenly, excelling at some tasks while remaining fragile in others. Market researchers are encountering this, and it’s what many technologists describe as the ‘jagged frontier of AI’. 

Understanding this frontier isn’t about resisting AI or surrendering to it. It’s about using it well. Because when market researchers understand what AI can and can’t do, they can work with more confidence and deliver insights more efficiently.

Read more: Human Experience in the AI era: A guide for insights leaders

What the jagged frontier means

The jagged frontier describes a simple but important truth: AI performance varies dramatically depending on the task. 

The same system that can summarize hundreds of interviews in seconds may struggle with irony, cultural nuance, or emotionally complex feedback. It may accurately surface patterns one moment, but it may struggle with concluding an array of correlations. This is true outside of market research, too. ChatGPT can provide impressive Deep Research answers on pretty much any topic, but it can’t count. This inconsistency isn’t exactly a flaw, but a defining feature of today’s AI systems. 

For market research teams, this explains why AI can feel tremendously disruptive, and also not at the same time. It would be a mistake to assume that success with a tool in one area applies to the rest of the process, and that it implies the redundancy of a human to operate, moderate, and expertly interpret. 

Where AI performs well

AI brings real, tangible value to market research. 

It excels at processing large volumes of information quickly, summarizing lengthy transcripts and open-ended responses, and detecting recurring patterns across datasets that would overwhelm human teams. It handles repetitive tasks like scripting, routing checks, transcription, translation, and basic QA with speed and consistency that humans would struggle to replicate, reducing friction across global projects. 

Used appropriately, these capabilities remove long-standing bottlenecks. They accelerate delivery, reduce manual effort, and help insights teams keep pace with rising expectations and shrinking timelines. 

But speed alone does not guarantee insight quality. And this is where understanding the jagged frontier becomes critical. 

Where AI struggles

AI’s limitations matter most in the areas where market research creates its greatest value. 

It struggles with ambiguity, like questions that are exploratory, emotionally layered, or open to interpretation. It lacks true contextual understanding, making it difficult to assess why a finding matters within your stakeholders’ setting. It performs poorly when topics are genuinely novel or when historical data is limited, as it is restricted to its training data. And it cannot reliably make ethical judgments about sensitivity, appropriateness, or unintended consequences. 

This is why AI can be confidently wrong. It produces fluent output without understanding significance. It identifies signals without knowing which ones deserve attention. Without human judgment, AI-generated output risks looking authoritative while missing the point entirely. 

But those building AI for insights understand these limitations too. In market research, you’re generally not exporting your data sets to LLM chatbots. You’re looking at tools that automate the things AI is fantastic at, leaving more time for humans to do the emotional and contextually important analysis.  

Read more: Why AI built for research is built different 

Common myths that distort expectations 

Much of the anxiety surrounding AI in market research doesn’t come from the technology itself: It comes from misconceptions about what it can realistically do. When expectations are inflated, fear and disappointment tend to follow. 

Several myths appear again and again in conversations with insights teams:

Discover more: Adapt to thrive: AI and the market researcher

How market researchers can work confidently with AI

Understanding the jagged frontier changes how insights professionals relate to AI. It’s something to understand as AI continues to evolve unevenly.  

Market researchers should be confident and competent with new AI technologies and adopt them appropriately to stay up to speed and competitive as this landscape evolves. Success comes from knowing when to trust AI with automation for efficiency gains and when to slow down and apply human judgment.

When approached this way, AI becomes less a source of uncertainty and more a collaborator. It handles speed and scale, while humans handle sense-making. The teams that thrive won’t be those who hand the whole workflow over to machines (or those who reject AI outright) but those who know exactly where the balance lies. 

But you’re not throwing participant data into ChatGPT to generate your charts or asking Gemini to find your sample. AI built for research is a little more specific, and secure. AI thrives when it’s given a specific task, reducing the chance of hallucinations or external information leaking into your results. This is just one thing that differentiates AI built for research.  

So, what does it look like? Forget the future, what’s available now? Let’s explore the practicalities of AI built for research and the features that will give you a leg up with increased speed to insight at every stage of the research process. 

Discover more: Register for our webinar, Adapt to thrive: AI and the market researcher

Research HX

Before we jump to the AI tools, it’s useful to know where they live. Each of these powerful features is part of Research HX, our solution built specifically for research, by researchers who’ve all ‘been there, done that’ and get what you need from your tech.  

• End-to-end integration across tools and systems streamlines processes, reduces complexity, and accelerates delivery. 
• Automation saves your workflows from time-consuming tasks like data preparation and analysis, cutting traditional research time by more than half. 
• AI power to unlock new possibilities for your research. Which is what we’re going to explore now. 

Find out more: Explore Research HX 

Getting started with your research: AI Survey Scripting

We all know how it usually goes, you draft a survey in Word, you circulate it, get conflicting feedback, you update it, circulate it again. Finally, you get to a point where you can script the survey. Depending on the complexity, this can take a while. By the time it’s built, it’s probably changed again, and so on it goes. 

However, we can flip the script (pun intended) with “AI Survey Scripting”. Once your Word document is final-ish you can upload it directly to the tool to create a structured survey draft in seconds, with the tool recognizing different question types and layouts. 

Now you’ve gone from document to consistent framework in hours, not days. Scripting is streamlined with fewer errors and a cleaner, more consistent framework for your research.  

This tool is continuously evolving to meet researchers’ needs, with the published tool in early stages of development and expanded versions available from next year.  

AI in data collection

AI Open Assist 

AI built for research and enrich your surveys to draw out deeper open-ended feedback. 

Open assist is built into Decipher (our survey tool and part of Research HX). When you need to get richer answers for certain questions, build this in and it will probe respondents to provide more information based on the answer they’ve started with. 

In short, you get an answer you can actually work with. Like peeling back the layers of an onion, AI Open Assist gets you past the dry outer layer into the useful flesh of… well… onion… If the results make you cry, we take no responsibility. 

Video and audio questions 

You can now make it really easy for participants to answer open-ended questions. In their own words, using a simple in-built video or audio only question type. That means at any point in your survey where you need rich human narratives; you can now insert a video question and go deeper.  

With the advancement of AI for data analysis (including AI-powered transcription and summarizing open-ended feedback), open-ended questions can be used in large, structured quant studies. AI built for research can help transform these responses into scalable insights that combine the depth of qualitative with the rigor of quantitative research. 

Automated sampling: Sample marketplace 

Researchers can spend days juggling vendors to buy survey samples. And just when you think you’re done dealing with vendors, you throw a bunch of bad data out and start all over again. 

With Sample Marketplace built into Research HX, you can source, compare, and genuinely launch in minutes​. Setup, linking, and billing are all in one place, reducing the headache of procurement. Sample Marketplace is filled with only trusted vendors to improve data quality and server-to-server integration to battle the bots you’re saving time and reducing errors with automated sample sourcing. Leaving you with plenty of time to tackle bigger, and bigger projects. 

AI powered analysis 

AI Summaries, Video and Audio Summaries 

Automatically analyzing open-ended responses has breathed new life into qualitative research. Using predefined prompts to surface topics and insights in Visualizations, tedious manual work is reduced, and the application of qualitative collection methods becomes viable for ad hoc and small projects.  

Unlike manual coding, AI can process responses instantly to extract topics or create a summary from verbatims to help teams surface insights faster.​ Explore the data with different selection types. 

And remember, this can be done with video and audio questions too. The decadence of qualitative is in your hands. 

Read more: Qualitative research, upgraded: Quantitative scale with AI  

Text analytics with Narrative HX 

We’ve covered AI analysis at the respondent level, so let’s zoom out and look at the whole study. With Narrative HX you can maximize your analysis by turning unstructured data into ​actionable insights. 

Narrative HX uses AI to analyze unstructured text, in over 50 languages (like verbatims or media transcripts) without lengthy setup or pre-defining categories. Surface themes, sentiment, and trends in a fraction of the time to relieve the burden of manual model building.​ This AI tool helps you build complex text analysis models in minutes, without special training or the requirement to be a hard-core coder or engineer. 

AI powered quality assurance and analysis: AI Computes 

Back to the respondents for a moment. Available in both Visualizations (analysis and data visualization within Research HX) and Decipher, AI Computes can be used to extract information from open ended questions.

Process respondent verbatims, flag PII for removal, translate most languages, and assign sentiment and topics. Use AI Computes to enhance verbatims to make them searchable, usable, and then actionable. 

Again, reduce your manual work, keep all your data within one secure solution and empower your teams to expand possibilities with AI built for research.  

Reports that drive impact

Getting your research in front of key stakeholders is one thing. Getting them to remember the impact of your insights is another. And this is what you want, so that you become an indispensable resource. This is the part that every researcher cares about. How to show RIO, drive impact, and democratize your data. 

One research agency speaks about how they used dashboards to empower a client to keep finding the answers they needed. Classic working smarter, not harder. Your data keeps giving without the need for a researcher to always be hands-on. Plus, stakeholders feel engaged in the process, embedding research further in business decisions. 

Automated report generation: Visualizations 

Automated PowerPoint reports. Yes, we’ll say that again. Automated PowerPoint reports. 

Streamline report creation, going from hours to minutes, and customize slides by question type and tags. Create and save customized templates for each stakeholder and benefit from integration with data collection, meaning reports can be generated in a few easy steps with less chance for human error and disjointed data when transferring between platforms. Spend less time on repetitive tasks and more time on analysis and storytelling for your stakeholders. This is one of the human skills that even AI built for research can’t replace, and where talent can differentiate you from your competitors.  

Agentic researchers: Research Agent in Visualizations 

We saved the best for last. A data visualization assistant in your pocket (or in this case, in the Visualizations solution) to help you nail this critical step. 

Research Agent is an AI-powered assistant that uses visual analysis to review and summarize research report slides. It helps you get information on how to refine design, layout, and insights with natural language input and outputs. 

​How does this work, you might ask? Select a slide and ask a series of questions, using pre-determined prompts, or your own, and the agent processes the content and gives you answers. This could help you work in industry best practices or evaluate how effectively your dashboard is communicating a clear narrative. The agent can even conduct a quality assurance review by identifying typos, missing elements, and layout issues.  

This gives you a personalized assistant to make your work better, your story stronger and your reporting more compelling. All withing a secure environment, no feeding external LLMs your precious data.  

Currently, Research Agent is only available to admin users as we gather feedback. But, watch this space. 

But this is just the beginning. Agentic AI built for research is on the horizon. We believe that integrated, task-specific agents will revolutionize the researcher’s role, and that this is starting to be realized. With true agentic power in the future, researchers will be able to tap into agents to automate even more of their processes, saving hours. 

Final word

Researchers are gold, and like how other precious metals are also important, they can’t be replaced, and nothing is quite the same; however, researchers will have more time for strategic tasks and human connection.

These same aspects have been vital to human survival for hundreds of thousands of years (300,000 if we want to be specific). Understanding how humans interact with the world is the reason market research was invented in the first place and until machines can feel, they won’t be able to do this better than humans. This foundation isn’t going anywhere, but a little help along the way and fewer tedious tasks are always welcome. 

Long before “no code” was cool, a small team asked a big question: “What if we could understand more about our gamers online?” The early spark was a web survey for a Mattel title, built by engineers who cut their teeth on text adventures and multi-user dungeons. That origin story matters. It set the tone for a tool engineered for power users first, then polished for everyone else. 

The earliest iterations ran as a scripting engine with a text interface that felt closer to the Oregon Trail than a modern wizard. Over time, it matured into a full UI without losing the skeleton key underneath: A text-based language, XML access, and the ability to script advanced logic. 

Decipher grew the old-fashioned way: Via word of mouth among serious researchers. The product was built by practitioners for practitioners, so efficiency beats flash. As part of Research HX which covers the whole of the research process, on the HX platform, many veterans still describe it as a tool you can’t outgrow.  

Today, Decipher is a professional-grade survey engine built for people who run complex, repeatable research at scale. It combines UI with an XML editor for power users, and it plays nicely with the rest of your stack via open APIs. Decipher powers 40,000+ studies a year and supports more than 1B surveys annually across 80+ languages. Eight out of 10 of the top MR agencies use our technology as the backbone of their research. Scale is not a tagline here; it’s in its DNA.  

Why researchers love Decipher: The secret weapon in plain sight 

Full-stack control when you need it 

The XML editor lets programmers see and shape the true skeleton of a survey, not just the skin. That unlocks Python for automation, complex logic, and serious data processing inside your build. On trackers, the payoff is real. 

Private servers for the heavy hitters 

Hundreds of clients run private servers for security, throughput, and back-end programmability. That is how teams templatize custom question types, enforce house standards, and deliver at enterprise cadence.  

An open tool that plays nicely 

The API library is broad and battle tested. Wherever you route cleaned data to, the plumbing is there. Bonus: All data exports include native Power BI/Tableau-ready structures when you want zero-touch handoffs. However, a new layer of speed, research-focused analytics and data visualizations can be applied when using Visualizations, the perfect partner to Decipher for all your analytical and PowerPoint needs.  

Methodology depth built in 

MaxDiff is a first-class question type with built-in analysis for 8–20 attributes inside crosstabs. Conjoint has been rolled out, with reporting in Visualizations for downstream storytelling. For teams who live on trade-offs, this is a big lift off your plate.  

The bigger picture: Research HX in action 

Surveys are the heartbeat, but the body is bigger. Decipher helps you collect clean, structured data that you can feed into the Research HX workflow, so you can move from collection to comprehension without friction. That means automated PowerPoint output when you need static deliverables, live dashboards in Visualizations for stakeholders, and case management to close the loop when “insight” becomes “action.” 

Industry context: Quality is the battleground. Grit 2025 calls out persistent uncertainty around sample quality, while ESOMAR’s guidance continues to push proactive prevention, validation, and layered defenses. One of the best defenses against fraud prevention we have released recently has been our S2S (server-to-server) integration. This allows our surveys to connect directly with panel providers, eliminating the chance for survey skippers and ghost completes.

Where Decipher is not the whole answer, by design 

Decipher’s crosstabs are a favorite for quick-cut analysis with strong statistical testing. For deeper work like variable creation, benchmarking, ranking and correlation at scale, using Visualizations is your best power-move. 

Features that matter 

• Word importer: Turn structured docs into live surveys in minutes. Ideal for repeated studies and rapid prototyping.  
• Sample marketplace in-Research HX: Source respondents up to 80% faster or bring your own sample with server-to-server connections for quality.  
• MaxDiff, automated analysis: Native item scaling for 8–20 attributes in crosstabs, plus export for extended modeling.  
• Open API: Add custom features throughout your data collection process. 
• Integration: Seamless connectivity with Visualizations for a smooth, fast collection to analytics process that gets you insights in half the time.  
• Enterprise trackers at the speed of business: Consolidating multi-market trackers into single projects, then deploying changes once, is the difference between missed moments and market moves. The six-month-to-six-day story is not a metaphor; it is a metric.  
• No-surprises packaging: Researchers get all the features included in Decipher, not a maze of add-ons. In a world of creeping line items, clarity is a competitive advantage.  
• Fit for the fraud era: Integrated quality scoring, S2S checks, and layered defenses reflect how the industry is really operating, not how we wish it worked.  

Why we are excited about the future 

Because Decipher still feels like a box of LEGO for researchers. You can click and go, or you can build a cathedral. Now it’s getting even smarter. Open Assist helps teams move from brief to build with guided setup, Word-to-survey building, and code suggestions in the XML editor. AI analysis throughout the Research HX process accelerates the “so what,” turning crosstab cuts into clear narratives, anomaly alerts, and next-best-question prompts you can act on.

Integration stays open by design: Bring your own model through APIs, keep data governed on private servers, and push outputs straight into Visualizations for same-day storytelling. In short: Decipher is constantly evolving, pairing trustworthy automation with human judgment, so insights hit harder, participants’ experiences are better, you complete clients’ requests faster, and the industry is stronger. 

Selling it internally is where real progress is made. Corporate buy-in is not a one-time pitch, and getting the adoption process off to a good start is half of the war. It is a campaign of influence, insight, and impeccable timing. If you want the green light, the budget, and the cultural muscle to make it stick, here are the five moves you must master. 

Stride beyond the surface-level sale 

No executive dreams about better dashboards and making sure new research technology can export to PowerPoint in a 16:9 format. Actually, we’re talking about research here, so they probably do. But anyway, the problem on their mind is market share. They dream about margins. They dream about speed. Often, the people buying aren’t the same ones who will be approving and aren’t the ones using the solution daily. When the sales process is too focused on vision and not enough on execution, things can start to stutter.  

Buyers might validate the business case, but they haven’t fully thought through what it will take to make it work. But loop in the frontline users and watch what happens. They can spot gaps a mile away. They’re the ones who know your technical requirements, your reporting quirks, your weird-but-critical processes. 

Miss those practical details, and your research tech rollouts will hit resistance fast. The smartest teams bring implementation into the conversation early. They align leadership’s big ideas with user-level reality. You need champions. Internal influencers. Voices from sales, marketing, finance, and product who will wave the flag with you.  

“There can be a real emotional disconnect. The leadership team is excited about everything AI. But the people doing the work? They don’t see AI solving problems that pen and paper can’t.” – Jilliane Martin, VP Global Customer Success, Forsta.  

More to explore: Everything you need to know about buying market research software webinar.  

Plan enough time. Period.  

Adopting a new tool or making major changes isn’t the best time to test how quickly everyone can scramble. Most process maps and project plans assume the ideal scenario. And let’s be real, the ideal scenario almost never happens. 

You need to account for the real world. Annual leave. Budget freezes. Internal politics. Shadow stacks. Every one of these can slow your momentum. And if your timeline doesn’t flex, you can lose confidence in both the solution and your own teams. 

Build in buffer. Overlap solutions where needed, as the cost of doing this is much cheaper than a failed implementation. And communicate that reality back to your stakeholders. The cost of doing it twice is higher than the cost of doing it right. 

More to explore: The complete guide to market research software ebook. 

Pilot with purpose and prove with power 

Ambition is great. But during research tech rollouts, it can backfire. The excitement of a new solution that’s going to solve all your problems and increase research speeds by 50% can be infectious. But remember, you’re wading into uncharted territory and you need to get everyone using the new tools up to speed.  

To start, make sure you’ve got your use case defined. Then, pick a smaller, simpler project you can take your time with. We recommend you don’t jump straight in with your most sensitive client. Start softer and build up over time to avoid any teething issues. 

Your first deployment should be a proof point. Not a pressure cooker. 

Start with a clean, contained project. Build internal confidence. Let your team learn the ropes without the fear of messing up a critical deliverable. And remember the point above – this isn’t the time to rush, you’re getting it right.  

Change is a challenge and a reward 

Even when you’ve made a great decision, your users might fight it. That’s human nature, we’re not the biggest fans of change.  

So, what do you do when user start to death-grip their old processes and tools? You can’t bulldoze that resistance. You need to guide people through it. Again, remember the first point here. Getting buy-in from all users early on is a great way to ease the transition, build confidence in the new solution, and get people really excited. Who isn’t going to love the change if they feel like they’re a part of it?! 

Success also comes from focusing on continuity. Show users how they’ll still get the same (or, ideally, better) outcomes. Get internal champions on board early. Celebrate quick wins during that first project. Communicate constantly, with clarity. Because left unchecked, that resistance turns into rogue workflows and wasted spend. 

Stay skeptical, but balanced 

One thing that rarely gets mentioned is the inherent tension between the five priorities buyers juggle during research tech rollouts. You can’t have all the benefits without paying the price, whether that’s in budget, timeline, or internal capacity. The most successful teams don’t try to do everything at once. They understand how to prioritize. They work through all the steps, but they put the most weight behind the one or two that will differentiate their process and drive success. Real success isn’t about perfection. It’s about priorities. 

“The salesperson who tells you, ‘We do these three things brilliantly, these two decently, and here’s what we’re not the best at. That’s the person you should trust,” explains Jilliane Martin. Every vendor has a balance between solutions and priorities. So, you should too. Build your research tech rollouts around what matters most, and don’t get distracted by the bells and whistles. 

More to explore: The smarter way to buy market research software ebook. 

Final word 

There’s no shortage of good market research tech out there. (If you are looking, we think you should check out Research HX.) But if you want your choices to deliver, you have to do more than buy. You must implement like a pro or find a company, like Forsta, who have service teams on hand to help you get up and running. From reprogramming existing surveys to migrating massive global brand tracking studies, we are here to support you.  

We also never throw you out of the nest. Once you are a client we are here with you with 24-hour technical support and a dedicated customer success manager assigned to your account, so no matter what you’re doing, you can soar. 

Success means aligning strategy with execution. Giving your team the time and space to succeed. And treating change like a journey, not a checkbox. The teams that win are the ones who lead with intention and plan for the potholes. 

With businesses demanding deeper insights and more comprehensive data to improve their decision-making, could diversifying help you satisfy their needs? While traditional methodologies remain the backbone of research, agencies must innovate to stay ahead of the competition. Incorporating Customer Experience (CX) programs offers a lucrative diversification opportunity to improve client retention and open new revenue streams. With the right technology, market research agencies can seamlessly expand their offerings and provide clients with additional end-to-end solutions that drive business success. 

What is customer experience market research?  

Customer experience research is about more than just tracking shopper behavior. It sets up the binoculars to show how real people interact with brands in the wild. It digs into what customers want, what drives them, and where brands are falling flat. From first click to post-purchase, CX research reveals the moments that matter and the gaps that need closing. 

The point? Brands want to get closer to their customers. A solid CX program helps them line up what they think they’re delivering with what customers are actually experiencing, and how they feel about it. Get that alignment right, and loyalty soars. ROI follows. And over time, this isn’t just useful, it’s do or die. CX insights become the blueprint for future product launches, service updates, and brand evolution. 

Here’s the kicker: Market researchers are already built for this. You know how to decode human behavior from data. CX is a natural pairing for market research prowess. And with the right platform and strategy, you can own it.  

The growing demand for CX 

CX has become a key differentiator for businesses and brands across all industries. Companies are increasingly investing in ways to understand, measure, and improve customer satisfaction and loyalty. CX programs offer a structured approach to collecting, analyzing, and acting on customer feedback. But businesses often can’t scale this mountainous task alone.  

Market research agencies are uniquely positioned to offer these programs with your long-standing command of data collection and analysis. However, launching a CX initiative requires the right technology and CX partner to ensure scalability, efficiency, and accuracy. Let’s talk about how to make it worth your while.  

What market research can do for CX 

Data storytelling 

Simple, yet stunning visualizations produced at lightning speed go a long way in democratizing data for consumption beyond your usual audience. Already adept at communicating complex streams of data, market research is poised to open the eyes of businesses to the power and potential hiding in their customer’s data. Literally. A picture speaks a thousand words, and an expertly developed PowerPoint yells with the strength of a hundred corporate workers trying to prove the value of customer sentiment.  

Faster implementation 

When flying solo, businesses may struggle with differing priorities and siloed operations. Market research agencies can bridge this gap by providing skillful strategies and superior tech partnerships. This allows you to offer a strong service and gets brands acting on data and seeing RIO faster.  

Continue to integrate data streams 

CX doesn’t exist in isolation. Forward-thinking agencies can differentiate themselves by offering integrated market research, CX and EX (employee experience). By expanding their scope beyond traditional customer research, agencies can offer holistic insights that align employee engagement, brand perception, and customer satisfaction. 

Forsta = MR and CX, hand in hand 

Our platform empowers market research agencies to adapt, expand and seamlessly integrate CX programs into what you already do. Here’s how: 

1. Seamless data collection 

We already support a substantial suite of data collection methods, including online surveys, mobile feedback, CATI, CAPI, offline data collection and real-time sentiment analysis. This allows you to gather rich insights at every touchpoint and plug in where businesses need it most. 

2. Advanced analytics and AI-driven insights 

Market researchers already know how to uncover insights. But with AI-powered advanced analytics, sentiment analysis, and customer journey mapping, agencies can elevate their value proposition. 

3. White-labeling capabilities 

Offer a fully customized CX solution under your own brand. This means market research agencies can expand their services without the overhead of developing their own technology. 

4. Automated reporting and real-time dashboards 

Static reports are so last century and we’re well beyond that now. Clients demand real-time insights, and our dynamic dashboards provide instant access to actionable data. This allows you to both communicate in a language they understand, and grants clients the opportunity to dive deeper and uncover their own insights. Market research agencies can differentiate themselves by delivering always-on reporting that fuels faster, smarter decision-making. 

5. Seamless integration with existing tools 

Our platform integrates with popular CRM, ERP, and marketing automation tools, allowing businesses to apply CX insights within their existing systems and workflows. This makes the prospect of a whole new program less intimidating for businesses, and you can offer implementation support for even more added value.  

Qualitative research tools also provide the opportunity to jump straight into discussion and focus groups with unhappy customers or get feedback on upcoming product launches from brand ambassadors.  

And your AI tools can lend an additional spark of power to CX programs. AI probing presses pertinent info out of respondents, allowing you to deliver deeper data to brands.  

Benefits of adding CX programs to your agency’s services 

By incorporating CX programs, market research agencies can: 

• Expand revenue streams: Offering CX services creates new business opportunities and diversifies income sources. 
• Strengthen client relationships: Providing end-to-end solutions positions agencies as strategic partners, not just data vendors. 
• Enhance competitive advantage: Agencies that offer CX programs differentiate themselves from competitors relying solely on traditional research methods. 
• Improve operational efficiency: With automation and AI-driven insights, agencies can deliver faster, more accurate results to clients. 
• Tap into $72billion (and growing) in revenue: While market research is a strong, stable pillar of insights, the CX industry is exploding dramatically. 

Market research is constantly evolving. Is CX part of your evolution? 

With the right partner, the road into, through and beyond market research is infinite. Adopting new programs, new data sources and new ways to appeal in a very noisy market gives you and your agency the opportunity to become indispensable partners to your client’s success.  If you’re ready to expand your service offerings and elevate your agency’s impact, contact us today to learn how our platform can help you integrate CX programs effortlessly.

Every insight begins with a single data point. But when that data is scattered, inconsistent, or locked in outdated tools, the road to said insight is long, slow, and full of potholes.  

Data collection is the foundation of market research, yet inefficiencies cost researchers up to 80% of their time on preparation instead of analysis, potentially leaving valuable insights untapped in the original answers​. It’s time to flip the script with Research HX, an integrated, AI-powered engine designed to accelerate insights, enhance quality, and eliminate friction. 

More data, less delay 

Time is the enemy of insights. Traditional data collection methods often mean juggling multiple platforms, manually cleaning responses, and waiting for data to be formatted before analysis can begin. Research HX removes these barriers, enabling a more agile and responsive approach to research. 

• Forsta Surveys: The powerhouse of data collection. Script or WYSIWYG build, craft your own or work with customizable templates. The limit? It does not exist.  
• Mixed mode: Capture feedback seamlessly across modes, whether online, mobile, or telephone. 
• Qualitative: AI-powered dynamic probing, multi-modal inputs and summarization gives you the power to unlock deeper, more meaningful insights.  
• AI-powered automation: Reduce manual scripting time with the AI-driven Word Importer, converting surveys from Word to live studies in minutes. 

This isn’t just speed; it’s precision at scale. A faster pipeline means insights flow freely, keeping decision-makers ahead of the curve. 

Quality data, no compromises 

Fast data is useless if it’s flawed. Data quality is a major concern, with 95% of researchers grappling with issues like inconsistent responses, dropouts, and unreliable panelists​. Research HX tackles this head-on with: 

• Intelligent probing: Discover more about the human behind the answers with personalized AI prompts that react to your participants’ open-ended responses. 
• Automated quality controls: Integrated tools detect fraud, improve response validity, and reduce bias. 
• Server-to-server (S2S) connections: Eliminates ghost completes, preventing fraudsters from muddying the waters, and reducing sample costs for the industry.  
• Multi-layered security: Multi-factor authentication (MFA) and integration for a secure connection and research process, every time.  
• Automated language translation: (80+ languages) ensures secure, globally scalable research without the need to export data into external tools.  

80+ question types 

Yes, over 80! Forsta Surveys supports just about every option for survey building. Researching products? We have a tool for that. Brand tracking? We have a tool for that too. MaxDiff, conjoint, Data Monitoring Committees? You betcha!  

Just a tiny selection of some of the question types we have:  

• Rank sort 
• Sliders 
• Image maps 
• Autosum 
• Images and videos 
• Tinder style 
• Open end prompting 
• Concept testing 
• Video and audio testimonials 

We have the question type for any of your use cases. And our unparalleled support is on hand to help guide or build customized surveys for you, so no matter what you’re investigating, we have the tech to get to the heart of the human. 

Your style, your way 

In addition to every question type under the sun, we also empower you to add your own flair. With custom design options through the build process, to full on white labelling, you can craft an experience that looks and feels as unique as it is. 

AI computes: Smarter, faster, more connected 

With AI-enhanced analysis, qualitative research can now be analyzed as easily as quantitative. Making insights deeper and more personal. This means you can add qualitative options to your data collection tech stack.  

Visualizations’ AI computes automatically flags PII, nonsensical, or irrelevant data for faster, cleaner analysis. It doesn’t just summarize open-ended responses, it transforms them into structured insights, identifying key themes, sentiment, and response patterns based on your criteria. 

And here’s the real game-changer. Open-end analysis happens with the same speed and efficiency as quant, meaning researchers no longer have to choose between depth and speed. With integration, you can rethink your data collection strategy based on the new tools at your disposal throughout the whole process. 

What if you could get richer, more human insights without slowing down? Parallel workflows and AI-powered automation make qualitative collection just as seamless as quant. Whether it’s digital diaries, live discussions, or AI-enhanced probing, Research HX allows you to collect data differently. Without adding complexity. 

Conjoint analysis: Power and precision 

Making the right choices starts with understanding how people make theirs. Conjoint analysis has long been the gold standard for unpacking decision-making, but it’s also been a logistical headache. Until now. 

With Research HX, discrete choice modeling is built right in. No more outsourcing, no more back-and-forth, no more wrangling data into the right format. Now, you can design, execute, and analyze conjoint studies seamlessly. Start capturing realistic decision-making (see, data collection) faster, with less manual processing and see results flow straight into Visualizations.  

Instant insights, immediate impact 

With Research HX, data earns its keep. As responses flow in, insights automatically sync to visualization tools, allowing researchers to spot trends in real-time. 

• Auto-generated reports: Forsta Surveys feeds directly into dashboards and AI-enhanced PowerPoint generation. 
• Seamless integration: Data collection connects instantly to analytics and reporting. No more manual exports. 

This isn’t just about efficiency. It’s about making every second—and every response—count. 

Redefining the research process 

Data collection isn’t just a step in the process. It’s the foundation for everything that follows. Research HX turns fragmented workflows into seamless pipelines, empowering researchers to do more, faster, and with greater confidence. 

Ready to transform the way you collect data? Book a demo to learn more, or explore the rest of Research HX to delve into the future of research with our dedicated ebook. 

When done well, AI can accelerate analysis, boost productivity, and help research teams uncover deeper insights faster than you can say “machine learning.”

But of course, there is a catch: Not all AI tools are created equal. Integrated AI solutions are suping-up expert teams, slashing speed to insight and offering a new battleground for bias, quality and agency diversification. Non-integrated AI tools on the other hand, while appealing, can open a Pandora’s box. Think security nightmares, data headaches, inconsistent quality, and a whole lot of copy-paste chaos.

Read more: Adapt to thrive: Innovation, AI and speed to insight

So, let’s take a deep breath, step away from the chatbot, and talk about what can go wrong when AI tools go rogue. When they don’t have integration to keep them in line.

Security matters: Together is better

Let’s start with the big one: Security.

Many non-integrated AI tools are hosted on third-party platforms, outside of your organization’s secure environment. Which means that when you upload your shiny new survey data to a flashy AI tool, you could well be handing it over to a black hole of unknown storage protocols, limited encryption standards, and zero oversight.

According to Cisco’s 2023 Data Privacy Benchmark Study, 92% of organizations say data privacy is a competitive advantage. However, a recent Axios report indicated that companies typically have an average of 67 generative AI tools in use, with 90% lacking proper licensing or approval, underscoring potential security vulnerabilities. Yikes.

What’s more, many AI tools store data for training purposes unless you explicitly opt out (assuming you can). That means your confidential data could be repurposed without your knowledge.

Oh, and don’t forget the humble copy-paste. If your team is lifting sensitive data from a secure system and plonking it into an online AI tool? You’ve just created a massive security risk in under 10 seconds.

The integrated solution: An integrated AI system lives within your research platform. Secure, encrypted, and governed by the same privacy protocols as the rest of your data. No copy-pasting, no dodgy data transfers, and no “oops, did we just leak our client’s data?” moments.

Data quality: Let AI be your lifesaver

Non-integrated AI tools may be fast, but they’re not always smart.

Without access to the full research context or a holistic data set, standalone AI tools often misinterpret nuance, lose vital sentiment, and make shallow assumptions. For example, generative AI models might summarize a set of open-ends beautifully, but if they’re not trained on your specific audience, brand tone, or research context, you risk delivering a summary that’s way off the mark.

And this raises the question of bias. Market researchers are highly trained to sniff out and mitigate bias, but can AI do the same? Now, AI isn’t necessarily doomed to replicate the errors of our past, but it does require some wrangling and the correct tool selection to ensure it provides the best base for you to work with.

Read more: Battle on bias: AI is learning from our mistakes

As of the latest GRIT industry data, adoption of AI tools is steadily climbing, yet still far from universal. While 69% of technology providers and 56% of full-service research firms describe themselves as users of generative AI, many organizations remain hesitant to embed these tools into their core research strategy. Why? Concerns around data quality, consistency, and integration are still holding teams back from fully embracing the tech. Without a seamless, integrated approach, AI can quickly shift from asset to obstacle; undermining trust in insights rather than enhancing them.

The integrated solution: Integrated AI is part of a closed-loop system. It sees the full picture from data collection to reporting. In this way, it can apply AI capabilities with contextual understanding, consistent methodology, and much higher quality control. That means smarter summaries, better sentiment analysis, and AI that actually supports your researchers, instead of confusing them.

The data sharing drama: When tools refuse to play nicely

Let’s say your team is using one tool to clean data, another to summarize open-ends, and a third to create reports. Sounds fine, right?

Now picture this:

• Data gets exported from Tool A
• It’s manually uploaded to Tool B
• Then it’s restructured for Tool C
• Then someone realises Tool C isn’t compatible with Tool A’s format…

Cue: Chaos, duplication, lost data, and a few choice words from your research team.

 Disjointed AI tools are notorious for breaking the flow of research. They don’t speak the same language. They don’t share metadata cleanly. They often strip out vital context during exports. And when it comes to tracking provenance or conducting audits? Good luck.

The integrated solution: An integrated AI solution works as part of a unified ecosystem. Where data flows automatically between stages, without human error, formatting issues, or compatibility meltdowns. This not only improves efficiency but also ensures data integrity from start to finish.

The hidden cost of going rogue

On the surface, non-integrated AI tools may seem like a cheap, convenient fix. But when you look a little closer, you’ll find that they often introduce:

• Hidden security vulnerabilities
• Workflow inefficiencies
• Poorer quality outputs
• Data silos
• Compliance headaches
• Duplicated efforts
• And most importantly… wasted time

Inefficiencies, errors, and duplicated efforts from poor data practices (often caused or exacerbated by non-integrated AI tools) result in real and significant costs for businesses. According to Gartner, bad data costs organizations $12.9 million a year, while IBM reports that in the United States alone, businesses lose approximately $3.1 trillion annually due to poor data quality.

Integration isn’t just a technical upgrade then; it’s a competitive advantage.

The human impact: It’s not just tech. It’s teamwork

Let’s not forget the people behind the platforms.

When teams rely on disconnected tools, they end up working in disconnected ways. Insights get missed. Context gets lost. Collaboration suffers. But with integrated AI built into your research workflows, your team spends less time firefighting and more time strategizing, storytelling, and doing the work that lights them up.

That’s why integration isn’t just a tech decision, it’s a people-first move. It’s about giving your team tools they can trust, use easily, and get excited about.

So, what should you do next?

1. Audit your current tech stack. Are there rogue AI tools floating around?
2. Talk to your team. What’s slowing them down? Where is data getting lost?
3. Choose an integrated platform. Look for one that combines research, AI, analysis, and reporting in one place. (We might know a good one. Hint: it starts with F.)

Read more: Five guiding principles for integrating AI in market research

AI is powerful, but only if it’s done right

AI in research shouldn’t be a patchwork. It should be a connected, secure, and intelligent part of your overall insight strategy. Otherwise, you risk turning your high-potential tech into a high-risk liability.

So, if you’re still juggling standalone AI tools, constantly exporting and reformatting, and praying that your tools don’t leak sensitive data, there’s a better way.

Let Forsta help you integrate your AI, secure your workflows, and take your research to the next level. Learn more about our integrated research solutions.

As a market research pro you’re making sense of the data mine. But when it’s messy, incomplete, or just plain unreliable, time can be a significant roadblock. Enter artificial intelligence (AI). Your companion when transforming chaos into clarity and delivering insights that drive action, hassle-free. From catching fraudulent responses to automating quality checks, AI is reshaping how we handle data quality.

In this blog, we’ll take a deep dive into how AI is supporting your fight for data quality and why it might be the friend you didn’t know you needed.

Fraudulent responses and bots: AI to the rescue

Imagine pouring your time, energy, and resources into a thoughtfully crafted survey only to find that half the responses were from bots with names like “SurveyKing123.” Not cool. This is where AI swoops for a pre-human check. Real-time fraud detection can spot bad actors (whether they’re bots, inattentive respondents, or professional survey spammers) and flag them faster than you can say “bad data.”

AI-driven sample quality scoring plays a key role here. At Forsta, we enable robust data quality measures through integrations with leading solutions from Dynata, PureSpectrum, and Research Defender. These integrations help you to filter out gibberish responses, low-quality inputs, and potential fraud automatically.

But it doesn’t stop there. AI doesn’t only weed out the bad apples; it’s also keeping an eye on patterns. Repeated behavior across surveys, like speeding through questions or copy-pasting answers gets flagged. This helps not only in real-time but also as a learning mechanism to improve future data collection processes.

And AI isn’t working alone. Server-to-server (S2S) integrations with sample providers add another layer of security and validation. By keeping a secure connection in place between sample buyers and suppliers, these integrations help ensure a higher level of data quality from the get-go.

So, why does this matter? Well, because clean data isn’t just nice to have. It’s the difference between actionable insights and “uh, what do we do with this?” High-quality datasets lead to better decision-making, more accurate predictions, and happier clients.

Data validation: Strengthening accuracy at every step

Ensuring high-quality data isn’t just about filtering out bad actors, it’s about validating every response with precision. Forsta is enhancing data validation through strategic integrations with leading data quality vendors and S2S connections.

All this helps to identify fraudulent activity, assess response quality, and improve research reliability. These tools work behind the scenes to validate respondents and secure your survey before they even start, ensuring that the data collected is both meaningful and actionable.

But we’re not stopping there. We’re continually exploring new technologies and partnerships to push data validation even further. Because when it comes to quality, good enough is never enough.

Automated quality control: The AI checkmate

Quality control isn’t sexy, but AI makes it feel like it is. Forget spending hours poring over responses, wondering if they’re accurate or relevant. AI can do the heavy lifting by screening responses for consistency, relevance, and accuracy.

Whether it’s identifying nonsensical verbatim answers or flagging overly enthusiastic respondents clicking “Strongly Agree” for every question, there’s an AI out there to ensure every piece of data meets your gold standard.

The bottom line? The more reliable your data, the easier it is to wow your clients with insights that actually drive decisions. And let’s not forget the efficiency factor: Fewer human hours spent combing through data means more time for creativity and strategy.

AI as your data sidekick: Here’s what it brings

AI doesn’t just clean your data; it elevates your entire research process. Here’s how:

1.   Consistency: Whether it’s text analytics or verbatim coding, AI brings structure to unstructured data. Gone are the days of wading through pages of survey feedback. AI summarizes, categorizes, and delivers insights in record time.

2.   Accuracy: AI doesn’t suffer from coffee crashes or bias. It applies the same level of scrutiny to every response, making sure that nothing slips through the cracks. AI also identifies outliers and anomalies that might otherwise go unnoticed.

3.   Efficiency: Time is money, and AI saves you both. With tools that automate quality checks and fraud detection, your team can focus on high-impact tasks like strategy and storytelling.

4.   Real-time adaptation: AI isn’t just reactive; it’s proactive. During live surveys, conversational AI tools can probe deeper based on initial responses. This dynamic approach ensures richer, more meaningful data that’s tailored to the research objectives.

What’s in it for you?

Let’s not sugarcoat it: data quality is a pain point for most research agencies. But AI transforms it from a headache into a seamless process. By enhancing accuracy and reliability, AI doesn’t just clean up your data. It elevates your insights.

And here’s the kicker: it’s not about replacing human expertise. AI is your constant companion, enhancing what your team already does best. It’s about working smarter, not harder. By automating repetitive tasks, AI frees up your team to focus on interpreting results, crafting narratives, and building strategies that truly resonate with clients.

AI in action: Forsta’s secret sauce

At Forsta, we’re not just fans of AI; we’re practitioners. Our tools integrate cutting-edge AI capabilities to ensure your data isn’t just clean, it’s pristine. Whether it’s real-time fraud detection or automated quality checks, we’re all about empowering you to deliver insights that shine.

Here’s how we’re making it happen:

• Integration: Our AI tools seamlessly connect with your existing platforms, making adoption a breeze.
• Customization: Tailored algorithms ensure your specific needs, whether it’s niche industries or unique research goals, are met with precision.
• Scalability: From small-scale surveys to massive data collection efforts, our AI grows with you, ensuring consistent quality at every stage.

Ready to revolutionize your data quality?

Whether you’re battling bots or tackling gibberish responses, AI and integration are your allies in ensuring insights that are as sharp as they are actionable.

Explore more about how Forsta’s solutions can elevate your research game. Because when it comes to data quality, good enough is never enough.

Sound familiar?

Welcome to the chaos of fragmented research workflows.

But what if there was a better way? One where your team actually has time to think, innovate, and uncover game-changing insights (instead of just trying to piece together a never-ending data puzzle)? We hate to be predictable, but you guessed it… there absolutely is!

In this blog, we’re going to talk about why unified research platforms are the future. And why it’s time to say goodbye to siloed, disconnected, and downright frustrating workflows.

The silo problem: Why disconnected research is holding you back

Here’s the ugly truth:

• Researchers spend 80% of their time preparing data instead of actually analyzing it.
• Only 1 in 5 market research professionals use AI tools in their core strategy.
• Fragmented tech stacks slow teams down, leading to longer project timelines, higher costs, and frustrated clients.

So, what’s actually causing this?

• Disconnected platforms: Your data collection tool doesn’t talk to your analytics tool, which doesn’t talk to your reporting system. Endless back and forth ensues.
• Manual work overload: Copying, pasting, cleaning, reformatting. Rinse and repeat until your sanity wears thin.
• Lack of collaboration: When tools and teams operate in silos, knowledge gets lost, opportunities slip through the cracks, and inefficiencies skyrocket.

The result? A whole lot of wasted time, duplicated efforts, and research teams that are too busy fighting fires to focus on what really matters: Delivering insights that drive impact. So, what can you do about it?

Enter the unified research platform: A researcher’s best friend

Imagine a world where your research tools actually work together; one where data flows smoothly from collection to analysis to reporting without needing 27 different logins and the patience of a proverbial saint. That’s the magic of a unified research platform.

Instead of constantly exporting and importing data between disconnected systems, everything happens in one place, creating a seamless flow that eliminates inefficiencies. Automation and AI step in to handle the tedious tasks, freeing your team from the grind of manual data wrangling so they can focus on high-value analysis and strategic thinking. And with fewer hours lost to admin, researchers finally have the space to explore new opportunities, experiment with fresh approaches, and push the boundaries of innovation.

But here’s the real kicker: It’s not just about efficiency, it’s about growth.

New opportunities: What happens when research teams have more time?

When you take away the data chaos, amazing things start to happen. Amazing things like new services, bigger projects, greater billing potential, and wider reach.

• More client offerings: With extra time, your agency can expand into new services. Think CX programs, social listening, eye-tracking, or AI-driven qualitative research.
• Bigger & faster projects: Unified research platforms speed up data collection, analysis, and reporting, allowing agencies to handle more studies without adding headcount.
• Higher-value work: When researchers aren’t bogged down with admin, they can focus on storytelling, consulting, and strategic insight delivery (which, after all, is what clients actually pay for).
• Global reach: Automated language translation opens the doors to international audiences, bringing in richer, more diverse insights.

Translation: More efficiency = More revenue streams.

(And yes, that means bigger budgets for research conferences, team retreats, or maybe just a fancy coffee machine to keep you out of Starbucks.)

So, what exactly is holding us back?

The fear factor: Is AI & integration killing research jobs?

Let’s address the elephant in the room: The fear that AI and integration will replace human researchers. It’s a concern that abounds in many industries, but here’s the reality: AI isn’t here to take jobs; it’s here to make them better. Think about it: Spellcheck didn’t replace editors, Google Analytics didn’t make marketers obsolete, and Excel didn’t put accountants out of work. Instead, these tools enhanced efficiency, eliminated repetitive tasks, and allowed professionals to focus on more valuable, strategic work.

The same applies to AI in research. Instead of drowning in manual data cleaning and endless report formatting, researchers now have the freedom to focus on the parts of their job that actually matter. Things like strategic analysis, creative problem-solving, and human intuition. AI serves as a powerful collaborator, like a workplace ride-or-die. Used to full effect, it can uncover deeper insights, faster. This means you can take on more complex, high-value projects that may not have been possible before.

Rather than being replaced then, research professionals are being empowered with better tools, streamlined workflows, and the time to do what truly excites them, delivering insights that drive real impact. The future of research isn’t about machines taking over; it’s about humans and technology working together to push the boundaries of what’s possible.

Breaking down silos: A real-world scenario

We’ve talked about the problems with fragmented research workflows, but let’s bring it to life. Imagine two research agencies: One stuck in the past, juggling disconnected tools and endless manual processes, while the other fully embraces a unified research platform. Which one do you think is thriving?

Scenario 1: The siloed nightmare

 Your team conducts surveys in one platform

• Data is exported, cleaned, and reformatted manually.
• Analytics happen in another tool (one that requires more manual setup).
• Reporting involves another manual export into PowerPoint, where someone spends hours formatting slides.

By the time the final report is ready, your client’s competitors have already acted on fresher insights.

Scenario 2: The unified research dream

• Surveys, analytics, and reporting happen in one connected platform.
• AI handles data cleaning and visualization automatically.
• Reports are generated instantly, complete with interactive dashboards.
• Your team spends more time advising clients on strategic next steps, which keeps them coming back for more.

Which agency do you think wins more business and grows faster? (Spoiler alert: It’s the one that’s not stuck exporting SPSS files at 2:00 AM.)

The future of research is unified

If your research team is still dealing with fragmented tools, duplicated work, and data silos, it’s time for an upgrade. A unified research platform isn’t just about speeding up processes. It’s about freeing up time for what truly matters:

More strategic, high-value insights Expanded revenue streams Better collaboration and teamwork Happier clients (and happier research teams!)

It’s not about replacing people; it’s about equipping them with the right tools to thrive. So, think about it; would you rather spend your days wrestling with disconnected systems, manually moving data from one place to another, and struggling to keep up with the competition? Or would you rather be at the forefront of the research revolution, delivering insights faster, smarter, and more effectively than ever before?

The choice is yours. But if you ask us, the future belongs to the agencies that break down silos, embrace innovation, and adopt a smarter way of working.

Ready to make the shift? 

Ditch the silos, unify your research, and unlock new opportunities.

Learn how Forsta’s integrated research solutions can help your team work smarter, deliver faster, and grow bigger and can help you ditch the inefficiencies and thrive in the new era of research. 

If your agency is still wrangling disconnected platforms, manual data entry, and a dozen software subscriptions that refuse to talk to each other, this blog is for you. Now, let’s get into it! 

The reality is that many researchers are still buried under manual tasks:  

• Researchers spend 80% of their time preparing data and only 20% finding insights 
• Professionals not using AI or integrated solutions complete tasks 25% slower than those who do 
• Only 1 in 5 market research professionals have fully embraced AI as part of their core strategy. Crazy, eh?  

That’s a whole lot of wasted time, untapped potential, and stress-fueled coffee consumption. 

But the researchers that get integration, automation and AI right? They’re the ones delivering insights at lightning speed, reducing errors, and outpacing the competition. 

So, how do you build a future-ready research agency with integrated solutions? Let’s break it down. 

Step 1: Audit your current research toolkit (and find out what’s slowing you down) 

First things first—take a good, hard look at your tech stack. Ask yourself:  

• How many different tools are you using for data collection, analytics, reporting, and visualization? 
• Are your platforms seamlessly integrated, or are you exporting, importing, and copy-pasting like it’s 2008? 
• Are there redundant systems that simply aren’t pulling their weight? 

If your tools aren’t saving time, improving accuracy, and making your life easier, it’s time for an upgrade. 

Step 2: Choose an integrated research platform (because patchwork solutions are holding you back) 

Not all platforms are created equal. The goal here is to centralize everything from data collection to reporting into one powerful, automated and AI-driven ecosystem. 

With an integrated research solution, you get: 

End-to-end efficiency: No more hopping between a dozen tools just to get a simple answer.  Real-time Insights: AI-enhanced automation speeds up analysis, letting you focus on what matters.  Error reduction: Fewer manual steps mean fewer mistakes (and fewer late-night data cleanups). 

Take Forsta’s HX Platform, for example. It seamlessly blends customer experience, employee experience, and market research. Breaking down silos and delivering insights in record time. (Yes, we’re biased, but also… it works.) 

Step 3: Automate the boring stuff (so you can focus on the fun, brainy bits) 

Let’s be real, nobody got into market research because they love data entry, formatting spreadsheets, or manually coding open-ended responses. Unless you did, and in which case, no judgement.  

But the beauty of integration? AI and automation handle the grunt work so your team can focus on the fun stuff: AKA delivering impactful insights that change the world (or at the very least, change your clients’ businesses for the better).  

What this looks like in action: 

• Perfectly formatted data from the start: No more hours fixing messy datasets
• Automated reporting & visualization: Watch your PowerPoint decks generate themselves in real time 
• Intelligent probing in surveys: Adaptive AI can give respondents prompts to enrich their answers, giving you richer qualitative insights 

The result? More time spent uncovering the ‘why’ behind the data. Not just processing it. 

Step 4: Avoid these common mistakes when integrating research solutions 

Even with the best of intentions, agencies often stumble when trying to integrate new tools. To help you get started, here’s what not to do: 

Mistake #1: Choosing tools that don’t talk to each other Some platforms look shiny and exciting, but if they don’t integrate smoothly with your existing systems, they’re more trouble than they’re worth. 

Mistake #2: Underestimating training needs Integration only works if your team knows how to use it. Investing in training makes sure that automation doesn’t lead to confusion and inefficiency. 

Mistake #3: Not thinking about scalability You might only need simple survey tools now, but what about in five years time? Choose a platform that can grow with you, not one you’ll outgrow in a year. 

Step 5: What happens if you don’t integrate? (spoiler: It’s not pretty) 

So, what if you decide not to embrace integration? 

Longer project timelines: Expect slow, manual data wrangling that eats up precious time  Frustrated clients: If they can’t get quick, actionable insights, they’ll take their business elsewhere  Higher operational costs: More manual work = more billable hours spent on admin instead of real insights  Missed opportunities: Without real-time insights, you’ll always be a step behind the competition 

Integration isn’t just about making life easier, it’s about future-proofing your agency and keeping your clients happy. 

What’s in it for you? (besides a lot less stress) 

So, what’s the payoff for embracing integrated research solutions? 

• Faster, smarter research: We’re talking less time on grunt work, and more time on strategy
• Higher-quality insights: AI-enhanced analysis = deeper, more accurate findings
• Better ROI: Less wasted time = more billable hours doing high-value work
• Stronger competitive edge: Clients want agencies that deliver fast, actionable insights

In short: Integration is no longer optional. It’s the key to future-proofing your agency. 

Don’t just keep up. Lead the charge 

The research world is only getting faster and more complex. Not to mention more competitive as the months pass us by. With that in mind, we’re confidently predicting that the agencies who embrace integration and automation today will drive innovation tomorrow. 

So, if you’re still cobbling together fragmented tools and manually crunching numbers, now’s the time to level up. 

Ready to supercharge your insights? Learn how Forsta’s integrated solutions can help you ditch the inefficiencies and thrive in the new era of research. 

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.