The insights function as we know it is ending. But what’s emerging in its place is far more powerful than what’s being left behind.
For decades, insights professionals have been anthropologists of commerce; studying consumers, interpreting behaviors, reporting findings. You’ve been the voice of the customer, the keepers of truth about what people want and why they want it. This was noble work. Important work.
But it’s no longer enough.
Three fundamental shifts are reshaping the landscape of consumer understanding, and together they’re not just changing what you study; they’re changing what insights means. Your function is moving from understanding consumers to enabling intelligence itself.
The three transformative shifts changing insights
Shift 1: The AI agent ecosystem emerges
AI agents are becoming a new class of consumer that doesn’t think, feel, or decide like humans do.
By 2028, these digital entities, from shopping assistants to financial advisors, will influence 60% of purchase decisions:
- They operate on optimization functions, not emotions.
- They process thousands of options simultaneously, not the handful humans consider.
- They learn from patterns invisible to human perception.
Why tradition insights won’t cut it: Your traditional research methods can’t interview an algorithm or run a focus group with software. Understanding these non-human decision makers requires entirely new approaches to consumer research.
Shift 2: System X redefines consumer decision-making
Humans aren’t making decisions alone anymore. Every choice increasingly involves an invisible partnership with AI; what researchers call System X.
What is System X? It’s neither purely human nor purely artificial, but a hybrid intelligence where AI augments both our quick, intuitive decisions (System 1) and our slow, analytical ones (System 2).
For example: When someone uses ChatGPT to compare products or lets an AI assistant reorder groceries, the decision emerges from a complex interplay of human preference and machine processing.
Why tradition insights won’t cut it: Traditional attribution models can’t capture this collaborative decision-making.
Shift 3: Context overtakes identity
The era of personalization (understanding who someone is) is giving way to the era of context (understanding who someone is right now).
For example: The same person who orders salad at lunch might crave pizza after a stressful meeting. Their identity hasn’t changed; their context has.
Real-time contextual intelligence draws on emotional indicators, environmental factors, physical state, and social dynamics to understand not just the consumer but their immediate reality.
Why tradition insights won’t cut it: This shift from static identity-based profiles to dynamic context requires insights delivered in milliseconds, not quarters, and insights of a fundamentally different kind.
The new role of insights: orchestrate intelligence
These three shifts point to a shift in the role of an insights organization. The role of insights isn’t to study intelligence but to orchestrate it and enable better decision making as a result of it.
Consider what’s converging:
- Data streams are multiplying: From traditional surveys to biometric sensors, from transaction logs to conversation transcripts, from behavioral clicks to emotional states – the raw material of insights is exploding exponentially.
- Processing power is democratizing: What once required a data science team now runs on a smartphone. What once took weeks to analyze happens in milliseconds.
- Decision-making is accelerating: The time between insight and action is collapsing. By the time you’ve written a traditional insights report, the moment has passed.
- Boundaries are dissolving: Between human and AI, between company and customer, between prediction and creation.
This convergence demands a fundamental reimagining of the insights function.
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From archaeologists to orchestrators
Traditional insights work has been archaeological, digging through data to uncover hidden truths about consumer behavior. You’ve excavated preferences, unearthed motivations, and reconstructed decision journeys. This careful, methodical work revealed patterns that shaped billion-dollar strategies.
But in a real-time, AI-augmented world, archaeology is too slow. By the time you’ve excavated an insight, the landscape has shifted. The consumers you studied have evolved. The contexts you mapped have changed. The agents you analyzed have updated their algorithms.
The new insights function must be architectural; designing systems that generate intelligence continuously:
- Instead of finding insights, you build insight engines.
- Instead of delivering reports, you create intelligence platforms
- Instead of answering questions, you enable question-answering at scale.
This shift from archaeology to architecture changes everything:
The new capabilities the insights function needs to thrive
To thrive in this transformed landscape, insights functions need to develop four core capabilities:
1. Agent fluency
Understanding how AI agents perceive, decide, and learn. This means studying algorithms with the same rigor once applied to humans; their biases, their boundaries, their evolutionary patterns. It means developing “agent personas” and “algorithmic journey maps.” It means learning to speak the language of machines while preserving human wisdom.
2. Contextual sensing
Building networks that can understand not just who someone is but who they are right now. This demands new data sources, new analytical methods, and new ethical frameworks. It means moving from periodic measurement to persistent awareness, from historical analysis to predictive presence.
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3. Intelligence orchestration
Creating systems that don’t just generate insights but enable others to generate their own insights and make better decisions. This means building tools that democratize analysis, automate interpretation, and accelerate from question to decision. The insights team becomes a platform team, enabling intelligence and better decision making across the organization.
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4. Ethical navigation
As intelligence systems become more powerful, questions of privacy, agency, and fairness become central to insights work. This means developing frameworks for responsible data use, designing for human flourishing, and ensuring that intelligence systems enhance rather than exploit human vulnerability.
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What your future insights team will look like
These new capabilities demand new talent profiles. The insights team of the future will look radically different from today, including:
- Computational anthropologists who study AI behavior with the rigor once reserved for human cultures.
- Decision architects who design systems that blend human judgment with machine intelligence.
- Context engineers who build sensing networks that understand situational dynamics.
- Intelligence developers who create platforms that democratize insights generation.
- Ethics operators who ensure intelligence systems respect human dignity and agency.
This isn’t about replacing traditional researchers with data scientists. It’s about creating hybrid professionals who combine humanistic understanding with computational capability, who can code but also care, who can build algorithms but also appreciate anthropology.
And it’s not just about adding new people to the mix. As capabilities and talent transform, so must organizational structures.
The insights function can no longer sit in a silo, delivering reports to other departments. It must become a neural network that connects and enables intelligence across the enterprise.
What it requires from insights leaders
For insights leaders, this transformation requires courage. It means abandoning comfortable competencies for uncertain capabilities. It means rebuilding teams while delivering results. It means explaining new value models to executives who still think in terms of focus groups and survey scores.
But the opportunity is unprecedented.
The insights function that successfully transforms becomes the intelligence backbone of the enterprise.
While other functions struggle to make sense of AI’s impact, you can become the sense-maker. While others wonder how to compete in an algorithm-driven market, you become the algorithm whisperer.
Key questions to ask yourself
Readiness isn’t about having all the answers. It’s about asking better questions. Start to think about the following questions as you plan out your next few months:
- How do you study intelligence that isn’t human?
- How do you measure decisions that aren’t conscious?
- How do you deliver insights at the speed of experience?
- How do you preserve human agency in an automated world?
- How do you create value when intelligence is abundant?
These questions don’t have simple answers. But they point toward a future where insights professionals don’t just understand the market; they help to create it and orchestrate decisions in a new way.
Looking to build an AI solution that delivers real business value at scale? Let’s talk.
Managing Director, AI Strategy and Innovation
For more than 15 years, Geoff has worked at the intersection of strategy, emerging technology and innovation and now leads our Americas team to lead AI strategy and build innovative AI-powered solutions. Geoff brings equal amounts of optimism and skepticism to every problem – and there’s nothing he loves more than a new problem to solve. Over the years, he has built and launched new products and businesses in consumer goods, financial services and wellness.
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