Thinking
Insights, Repositioned
What AI means for the FMCG Insights function, and four imperatives for leaders navigating the shift
Across FMCG, the Insights function is at a defining crossroads. AI is reshaping how consumer understanding is generated and applied. The challenge is strategic, not purely technological.
Why this matters now
Across FMCG, AI is creating new pressure on the Insights function. Consumer understanding can now be generated faster, synthesised at greater scale and connected to more data sources than ever before. But speed alone does not create better decisions, and most organisations are still struggling to move AI from isolated pilots to measurable impact.
The opportunity for Insights leaders is to reposition the function around decision intelligence: helping the business understand where to act, what to prioritise and how to move with greater confidence.
7-13
Potential EBITDA point impact from consumer insights and demand shaping in consumer goods at scale.
Source: McKinsey
Explore the four imperatives
01
From research production to decision intelligence
Shift from producing reports to shaping decisions across innovation, pricing, brand and growth.
02
The speed-depth dilemma
Anchor faster intelligence in real cAnchor faster intelligence in the commercial decisions that matter.
03
Data fragmentation is a governance challenge
Disconnected data limits AI impact. Ownership and governance determine whether it scales.
04
Close the interpretive gap
Build the capability to evaluate, challenge and translate AI outputs into action.
The risk is not AI replacing Insights.
It is Insights becoming a passive user of AI.
The opportunity is to reposition the function from research production to decision intelligence, shaping decisions, not just informing them.
Deep dive into the four imperatives
01
From research production to decision intelligence
AI can generate synthesis quickly, so the value of the Insights function shifts from producing reports to shaping decisions across innovation, pricing, brand and growth. The most advanced Insights teams are moving closer to the decisions that determine where a business plays, how brands grow and how consumer demand is shaped. The role is no longer simply to explain what consumers think. It is to help the organisation decide what to do next.
Leadership implication
Insights must move closer to the decisions that determine growth.
Question to ask now
Which business decisions should Insights directly shape, not simply inform?
02
The speed-depth dilemma
AI increases speed, but speed without rigour creates false confidence. Value comes when faster intelligence is anchored in real commercial decisions and governance. The business wants faster answers, but commercial trust still depends on depth, context and judgement. AI can shorten research cycles and surface signals earlier, but it only creates value when the outputs are connected to decisions that matter.
Leadership implication
Speed creates value only when it is tied to decisions that matter.
Question to ask now
Where does the business need faster answers, and where does it still need deeper judgement?
03
Data fragmentation is a governance challenge
Consumer, retail sell-out, market research, loyalty, social listening, DTC and first-party data often sit in disconnected systems owned by different teams. The constraint is usually governance and ownership, not the algorithm. Without governance, AI pilots stay trapped in isolated use cases. For Insights leaders, the data layer is not just a technology dependency. It is a leadership decision that determines whether AI-enabled insight can scale.
Leadership implication
The data layer is a leadership decision, not just a technical one.
Question to ask now
Who owns the data foundations that AI-enabled insight depends on?
04
Close the interpretive gap
The real capability challenge is not access to tools, but the ability to evaluate, challenge and translate AI-generated outputs into recommendations that stand up to scrutiny. Advantage will come from teams that combine AI fluency with category knowledge, commercial judgement and consumer understanding. The goal is not more automated output. It is better judgement, applied faster.
Leadership implication
Teams need interpretive capability, not just AI access.
Question to ask now
How will the team judge whether AI-generated insight is credible enough to act on?
From business decisions to measurable impact
The pilot graveyard is rarely caused by weak tools alone. More often, AI experiments stall because they are disconnected from the decisions the business needs to make, built on fragile data foundations, or launched without clear ownership.
The pilot is not the starting point. It is the test of a decision-led use case.
Business decisions
Clarify the growth, innovation, brand, pricing or demand decisions Insights needs to shape.
Priority use cases
Define the AI-enabled use cases that directly support those decisions.
Data foundations
Identify the data sources, definitions and quality requirements needed to make the use case credible.
Governance and ownership
Agree who owns the use case, the data layer, the decision process and the route to scale.
Capability building
Build the skills, review routines and judgement needed to use AI outputs with confidence.
Measurable impact
Track whether the work improves speed, decision quality, adoption and commercial outcomes.
How Pivot & Co can help
Pivot & Co helps Insights leaders move from AI experimentation to practical transformation, connecting decision-led use cases, data foundations, governance and capability into a clear route to measurable impact.
Diagnose
Assess strategy, capability and data readiness to identify the highest-value opportunities.
Prioritise
Define decision-critical use cases, quantify opportunity and agree success measures that matter.
Build
Stand up data foundations, governance and capabilities to scale with confidence.
For Insights leaders, the question is no longer whether AI will change the function. It is whether the function is ready to lead that change.
Is your Insights function ready for the next role it needs to play?
Let’s assess where your function stands, and define a practical path forward.