AI unlocks a new role for insights teams

Back in 2023, AI was mostly viewed as a promising future companion for insights teams. There was cautious optimism about its potential to automate repetitive tasks, generate faster reports and maybe even help spot trends in large datasets. It was a conversation centered around “augmentation,” not “disruption.”

Fast forward to 2025, and that future has arrived, with all the velocity and complexity we didn’t fully anticipate. AI is no longer just a tool to boost productivity; it’s become a disruptive force that’s fundamentally reshaping how insights teams operate, collaborate and deliver value.

Today, AI touches nearly every part of the research process. It drafts surveys, recommends methodologies, synthesizes open-ended responses at scale and surfaces insights faster than traditional workflows ever allowed. More advanced systems now proactively suggest hypotheses or flag anomalies in real time, allowing teams to respond to consumer behavior as it unfolds (not weeks later).

This shift hasn’t eliminated the need for human researchers — it’s elevated their role. 

The human edge now lies in judgment, creativity and strategic storytelling. Insights professionals are increasingly acting as orchestrators: guiding AI systems, interpreting nuanced results, and translating outputs into business-impacting actions. It’s no longer about running a project, it’s about designing a system that continually learns and adapts.

Of course, this disruption brings new challenges, too. Teams are reevaluating their tech stacks, retraining on AI tools and rewriting their roles. Questions about data ethics, bias and transparency are more pressing than ever. But these disruptions are necessary to build more responsive, intelligent and scalable insights functions.

Ultimately, the conversation has shifted. In 2022, we wondered how AI might help. In 2025, we’re redefining what research even looks like — with AI at the center. 

In this article, I’ll share my perspective on how AI has shifted how market researchers work and how you can upskill your insights team in 2025.

In their 2015 book, The Future of Professions, Richard Susskind and Daniel Susskind predict the decline of professional work: 

The end of the professional era is characterized by four trends: the move from bespoke service; the bypassing of traditional gatekeepers; a shift from a reactive to a proactive approach to professional work; and the more-for-less challenge.

Richard Susskind & Daniel Susskind, The Future of Professions

These four trends apply to the consumer insights profession as well. AI will: 

1. Turn consumer insights from a craft to a product, one that anyone in the organization can leverage. 

2. Facilitate the democratization of insights, which will remove traditional gatekeeping of consumer data. Insights teams will no longer “own” the consumer.

3. Help companies be more proactive, rather than reactive, by serving up useful information whenever it is needed. Research will no longer be produced solely from specific business questions.  

4. Cut many of the costs associated with consumer research. 

AI can make consumer insights easier to acquire, access, understand and act upon. As a result, organizations will have rich consumer insights at their fingertips — faster and cheaper than ever before — with nothing to stand in their way of making better decisions based on consumer truth. 

In short, AI is an amazing advancement for consumer centricity. That future should be thrilling to you as an insights leader. But you are undoubtedly looking ahead to what it will mean for you and your team.

Let's take a deeper look into what this shift means for insights teams.

AI hasn’t just accelerated the research process — it’s transformed the role of the researcher. From designing studies to delivering insights, today’s workflows look very different than they did just a few years ago.

In 2023, a typical research project might have involved weeks of manual programming, survey fielding, data cleaning, analysis and reporting. Today, much of that heavy lifting is automated. AI handles survey logic, flags quality issues in real time and generates topline summaries within hours of field close. What used to take weeks now takes days or even hours.

But speed isn’t the only shift. AI has also changed how researchers think. Rather than focusing on a single static study, teams are now working in cycles of continuous learning. Tools powered by generative AI and machine learning can identify evolving consumer themes, compare new results to historical norms and recommend next steps without waiting for a debrief meeting.

Real-world examples

McDonald's

McDonald’s has used AI-driven insights platforms to test creative faster and adapt global campaigns to local markets with greater precision. By combining historical data with real-time feedback loops, their teams have been able to localize messaging and visuals more effectively than traditional ad testing cycles would allow.

Reckitt

Reckitt has also leaned into AI to automate concept testing across product categories. By integrating generative AI into their innovation workflow, they’ve moved from asking “Which idea wins?” to “How can we optimize this idea in market?” A mindset shift that’s unlocked faster, smarter product decisions.

Unilever 

Unilever has applied AI to help create limited edition lines for brands like Dove based on what's trending with consumers today. What once required months of desk research and agency support can now be explored in days, giving brand teams a first-mover advantage in spotting emerging themes.

These examples show how AI isn’t replacing researchers; it’s repositioning them. The new skill set is less about manual analysis and more about framing the right questions, overseeing AI tools responsibly, and turning complex outputs into sharp, business-ready insights.

In this new reality, researchers are becoming strategic consultants within their organizations. They’re not just delivering answers, they’re architecting systems that continuously generate them.

To stay competitive in 2025, insights teams need to shift how they work and the skills they bring to the table. Here's my take on how to upskill your team and where to focus: 

1. Think beyond projects and start designing systems

In the past, researchers focused on delivering individual studies. Today, it's about building systems that learn and improve over time. AI can run continuous testing, flag emerging trends, and connect the dots faster than humans ever could — but only if it’s set up and guided correctly.

Where to focus: Help your team develop a systems mindset. That means understanding how platforms connect, how data flows and how to build research programs that get smarter as they go.

2. Make AI fluency part of the job

No, your team doesn’t need to become data scientists. But they do need to understand how AI works, where it adds value and when it needs a human hand on the wheel. Fluency means being able to use AI tools confidently and spot when something feels off.

Where to focus: Offer training on generative AI tools, prompt writing and how LLMs like ChatGPT or Claude actually work. Encourage experimentation so your team gets comfortable using these tools day-to-day.

3. Double down on strategic thinking & storytelling

As AI takes over the more repetitive parts of research, humans are free to focus on the work that really matters: interpreting results, shaping strategy and influencing decisions. This is where your team can shine.

Where to focus: Build skills around storytelling frameworks, business context and turning data into recommendations. It's not just about finding the insight — it’s about making it land.

4. Stay grounded in ethics and responsibility

With AI in the mix, it's easy to move fast. But that makes it even more important to stay thoughtful. Responsible AI use isn't optional. Researchers need to know how to spot bias, protect consumer data, and make sure automated outputs are fair and accurate.

Where to focus: Run training sessions on bias detection, data ethics and AI governance. Make these topics part of your team’s regular conversations, not just a compliance box to check.

5. Help your team work across functions

Insights doesn’t live in a silo anymore. AI-enabled research often touches product, creative, media and analytics. Researchers need to know how to plug into those teams and add value at the right moments.

Where to focus: Create opportunities for cross-functional learning. Whether it’s shadowing a sprint, joining a campaign kickoff or co-building a dashboard, helping researchers step into other workflows can make a huge difference.

TLDR: What does a future-ready insights team look like?

The best insights teams in 2025 are:

• Comfortable using AI and guiding it to get better results

• Designing flexible systems instead of running one-off studies

• Strong storytellers who can influence business decisions

• Ethically grounded, always checking for fairness and quality

• Deeply embedded in the business, collaborating across teams

AI has moved from the sidelines to the center of the insights world. And it’s not slowing down. 

What started as a promise of speed and automation has become a full-scale shift in how research gets done, how teams operate and what skills matter most. Rather than replacing researchers, AI is creating space for deeper thinking, sharper storytelling and more strategic impact. 

But to unlock that value, teams need to adapt, building fluency with new tools, embracing continuous learning and staying grounded in ethics and empathy. 

The future of insights isn’t just about data or technology — it’s about people who know how to use both wisely.