The insights industry is resetting: AI is now the baseline, and impact is the bar.
Advantage comes from AI proficiency, balancing speed with human judgment and trust.
Insights is shifting from static research to decision support and tradeoff navigation.
Human-centered insight, backed by the right AI and teams, is critical for confident decisions
At Insights Lighthouse Davos, the same themes kept coming up again and again.
Across a few intense days of panels and conversations, there was a shared sense that the insights industry is in the middle of a reset. People were being honest about what’s working, what isn’t, and what needs to change if insights teams want to stay relevant and influential in the years ahead.
Here are the ideas that stayed with us after we got home.
One of the strongest themes came up early and often: the scale of the industry doesn’t protect it from pressure.
Consumer and market insights is a roughly $150B global industry, and it’s still growing. But that hasn’t protected teams from layoffs, leadership churn, or tougher questions from the business about impact and ROI.
The message from multiple speakers and attendees was consistent, even if it wasn’t always comfortable: AI is no longer a differentiator. It’s the baseline.
What matters now isn’t being an “AI expert”. It’s having AI proficiency that helps you to use these tools to move faster and reduce bias, while still navigating the very human parts of decision-making: influence, uncertainty, emotion, and internal politics.
For Stravito, this means helping Insights leaders connect insights to real business choices. As expectations rise, insights teams need support in telling clearer stories, making recommendations even when the data isn’t perfect, and showing how insight translates into growth. That’s where decision confidence becomes the real outcome, not just more information.
Another set of sessions that caught our eye looked at how insights work itself is changing, especially through virtual shoppers and predictive market simulation.
Instead of running one study at a time on one product or price, these approaches model entire portfolios. You can simulate what happens when prices change, distribution shifts, or advertising increases, and see the effects across related products in real time.
Although the math was impressive in its own right, it was the shift in mindset that was truly compelling.
This includes tradeoffs, competition, and growth goals that aren’t just about margin or volume in isolation.
For us at Stravito, it was a good reminder that the future is focusing increasingly on supporting decisions, especially when those decisions involve complexity and competing objectives.
It also sparked some interesting conversations about where decision simulation is headed and where collaboration across the ecosystem could make sense.
One of the most grounded discussions came from sessions on how organizations are actually training people to work with AI.
The honest takeaway: AI speeds up execution, but it doesn’t replace thinking or judgment. And over-reliance can do real damage, especially for people early in their careers.
An example shared in one of the sessions was very strict: people still learn the fundamentals and do manual analysis before using AI to accelerate, not substitute.
Speed mattered, but only if teams still understood what they were doing and why.
We also heard warnings (and complaints) about tool sprawl. Too many overlapping AI tools, too little clarity about which ones matter, and growing risk as people look for “side doors” to get work done.
That sparked a lot of nodding in the room. Governance panels and clearer ownership help teams focus on what actually drives decisions, not restricting teams.
From our perspective, this reinforced something we see with customers already: consolidation and clarity are becoming just as important as innovation.
Insights don’t have to be flashy to make an impact. Being thoughtful can make the same splash.
One session shared how they are mapping an enormous and often confusing health and wellness landscape using a blend of AI and human expertise. A big emphasis was placed on credible sources and avoiding hype.
One highlight was a simple, three-minute gut health assessment with very high completion rates. Instead of overwhelming people, it translated complexity into clear, practical routines.
Even their product strategy reflected this approach. Rather than framing protein purely around fitness, they’re repositioning it around metabolism and everyday energy.
What stood out was the role trust played, rather than the health tech itself.
When people are faced with complexity, the winners are the ones who help people understand enough to act with confidence.
At Stravito, we know value comes from making complexity usable. Whether it’s consumer health or enterprise research, trust is built when insights are translated into clear, decision-ready guidance. Our focus on human-centered AI and transparent sourcing reflects that same principle: people need to understand why an answer exists before they can act on it.
Scaling AI across tens of thousands of employees doesn’t happen organically. It requires structure. The advice we heard was practical:
An “AI-first” operating model is quickly becoming the backdrop for how large organizations think about work. Assistants for every employee, agents for every process, fewer steps, and clearer handoffs.
Scaling AI successfully is less about experimentation alone and more about structure, clarity, and confidence. As enterprises move toward AI-first operating models, insights platforms need to fit naturally into governed, trusted workflows, helping teams adopt new capabilities without creating risk or fragmentation.
Taken together, what Davos really underscored is this: AI only creates value when it helps people think better.
As insights teams are asked to move faster, prove impact, and guide growth decisions, the human side of the work matters more, not less. Judgment, context, storytelling, and trust are still what turn insight into action. AI’s role is to support that work, not overwhelm it.
That’s why having the right AI tools and the right partners behind them has become so important. Tools that are grounded in real insight, designed around how teams actually make decisions, and built to help people find clarity instead of noise.
This is the direction the insights industry is heading. And it’s exactly where we’re focused: helping teams turn knowledge into confident, human-centered decisions when it matters most.