Insight After AI

More and more brand and insight leaders are asking me the same essential question:

“How should we be thinking about our data and insight in the age of AI?”

It sounds like a technical question. But what they’re really getting at is strategic: how can we use these new tools to fuel more valuable decisions?

There’s a paradox here: as tools become more powerful, human ingenuity is more vital than ever. Not despite AI – but because of it.

From Questions to Strategy

Teams and brands that are winning aren’t just adapting to AI, they’re actively rethinking the entire insight value chain. What is its purpose in a world of instant answers? And where does human expertise matter most?

These aren’t cautionary questions. They’re competitive ones. The opportunity for AI is real: greater accuracy, deeper understanding, sharper foresight, almost instant, and all equally available to data scientists and marketing interns alike. The challenge is orchestration.

From Data Abundance to Human Orchestration 

At its core, research exists to uncover opportunity and manage risk. This won’t change.

What has changed is the operating environment. We’ve moved from constraint to abundance. From data scarcity to data overload. Today, insight professionals must choose which inputs to trust, how to combine them, and why they’re right for the task. With AI, insight teams now have more instruments, more players, and more signals than ever before. The opportunity is richer, yet the challenge is more complex. 

Far from being automated out of existence, human-led research is now more valuable. These teams don’t just produce answers, they shape complexity into something that drives meaning, momentum, and relevance for brands. 

And the best teams aren’t just reacting. They’re designing systems that learn, adapt, and deliver over time.

From Inputs to Intelligence

The shift we’re seeing is from one-off answers to evolving systems.

One-off insights lose relevance fast. That’s why leading teams are building frameworks with built-in feedback loops – systems that grow in accuracy and value over time.

This requires more than tactical rigour. It requires design, empathy, methodology, and above all: strategic discernment.

We call this capability Data Fluency: the ability to connect better insight up front to sharper strategy and more effective execution.

We don’t see high-performing teams using AI to replace analysts with prompts. We see them empowering analysts with more inputs, better tooling, and greater responsibility. Because ironically, in a world of data abundance, researchers are making more decisions, not fewer.

That’s why the next competitive advantage isn’t just access – it’s the ability to separate confidence from quality. It’s judgement.

From Confidence to Quality

Decision-makers feed on confidence, but succeed on quality. And those aren’t always the same.

We see this often in our work on the say–do gap. We’ve found a consistent pattern: what people say and what they do can differ by 40–70%. That variance matters.

For example, survey data might show 70% of respondents say they’re spending more on holidays. Actionable, right? But when matched with actual purchase behaviour, only 10% may truly be spending more. The story changes and the associated decisions need to change too.

In an AI-powered world, these traps scale fast. Generative models offer confident answers in just seconds, however, confidence doesn’t equal correctness. The illusion of certainty becomes a serious risk in high-stakes decisions: from brand strategy to product launches and policy development.

This is why quality insight will, for the time being, still depend on human intelligence and creativity. The best teams are building their capacity to challenge assumptions, triangulate inputs, and stay alert to what’s almost right but not quite. Because in this landscape, discernment is not optional – it’s decisive.

The discernment also extends to staying up to date with the AI tools that are available and using the right ones to solve the right challenges. Not all tools perform equally. We’ve seen significant variation between leading AI providers, depending on the challenge at hand. At some point soon the tools will critique one another – but until then, domain expertise is vital. Knowing which model to trust, in which context, is crucial for outcomes. 

And of course, even the major players are clear: their tools are experimental. The risk and the responsibility still belong to the user.

As tech philosopher Tom Chatfield puts it, “In an age of artificial intelligence, success more than ever depends upon distinctively human capacities.”

In the case of data and insight functions that means:

• Holding the brief
• Holding domain expertise 
• Knowing the cultural context
• Bringing the taste, the instinct, the memory

This kind of fluency doesn’t just sharpen internal decisions, it creates new ways for brands to shape culture itself.

It’s the thinking behind the M+C Saatchi Cultural Power Index, a first-of-its-kind, AI-powered diagnostic tool that can help brands harness the power of culture to drive growth. 

In a fragmented landscape, that kind of clarity is a competitive edge. It turns cultural relevance from a guess into a strategy, and insight from static answers into ongoing advantage.

When we can see, measure and even model this clearly, we can act more decisively. It’s this kind of insight that turns complexity into competitive advantage.

From Insight to Advantage 

So, if you’re leading a brand or an in-house insight team, ask yourself:

Am I optimising for answers — or for action?

The most effective organisations aren’t the ones with the most data, they’re the ones with the clearest intent, the sharpest judgement, and the ability to turn AI-powered insight into strategic progress.

We help teams evolve their data and insight capabilities to meet the demands of this new environment by blending cultural intelligence, advanced analytics, and strategic consulting to build functions that are fast, discerning, and human-led.

If you’re reviewing how your organisation uses insight — or what capabilities your team will need next — we’d love to explore that with you.