Editor’s Note: As AI makes data, analysis and content faster and more abundant, human judgment becomes a more important source of competitive advantage. For heads of insight, the opportunity is to reinvest AI-created capacity in context, critical thinking, relationships and better business decisions.
20 years ago, British mathematician Clive Humby said, “Data is the new oil.” For a time, he was right.
Data, access to it, understanding of it, the ability to manipulate it and tell stories from it, was, once upon a time, a structural advantage for insight teams. But, as I reflect on a busy H1, made up (alongside client work) of lots of time spent “outdoor catting,” a C Space-ism meaning getting out and about in the industry, I wonder if, just a quarter of the way through the 21st century, this isn’t true anymore.
At IIEX Europe and the MRS Annual Conference, the same observation surfaced repeatedly from different angles: everyone has information now. The question is what you do with it. Mike Peng, CEO of IDEO, speaking at the World Beautiful Business Forum (WBBF), put it directly: If everyone has the same tools, how do you bring back the mastery of the individual? Or, as Zoe Scaman, founder and AI strategist, puts it more provocatively, how do you differentiate when all the AI engines are programmed to give you the same answer?
This blog post doesn’t position AI against human judgment; it’s not one or the other. AI doesn’t threaten human judgment, it makes it more powerful, more valuable. When analysis and information become cheap and abundant, the scarce resource is knowing what decisions to make and having the conviction to defend them. It argues that this is the defining challenge for heads of insight now: not defending their insight function against AI but actively investing in the human judgment that AI both enables and, if left unmanaged, quietly erodes.
Why does AI make human judgment more valuable, not less?
Don’t take it from me. The MRS Research Live report called out and captured this tension before I had the words for it. Eddie O’Brien, global head of insight at Sage, argues that the teams who succeed in an AI-powered insight world will shift from being data providers to data advisers, partners in decision-making. He argues they’ll be aided by AI but “powered by human judgment”.
That framing matters. It isn’t that AI can’t replicate judgment (not yet, anyway)—it’s that, as AI makes information and analysis ever more abundant, the ability to know which questions to ask, which AI outputs to trust, what is missing and what should follow becomes the genuine differentiator. The MRS Delphi report puts it well: “when outputs become abundant, fluent and cheap, the scarce value is understanding.” I’d go a step further and add “and the ability to influence decisions.”
In some ways, this isn’t a new argument, but new tech developments have changed the context; insight teams have long had the ambition to be strategic business partners, enabling decision-making and supporting end stakeholders to make better informed, customer-centric decisions.
When AI makes analysis abundant, the competitive advantage shifts from producing more information to exercising better judgment, asking the right questions, understanding what is missing and influencing the decisions that follow.
What is human judgment in insight work, and why is it difficult to scale?
Judgment isn’t instinct. It’s context, it’s lived experience, it’s the things you learn from years of making the same observations, the same mistakes, over and over. It’s knowing your CMO won’t find this report credible because she thinks this metric is overplayed. It’s knowing that it’s all well and good that customers want a shelf-stable formula, but R&D have been trying to deliver it for six years with slow progress. It’s knowing what an upcoming restructure feels like.
It’s a kind of pattern recognition built from exposure to business realities. It’s lateral and dialectical thinking, conclusions drawn from your understanding of relationships as much as data. It’s the confidence to defend those conclusions to a room full of stakeholders when they push back.
None of that lives in a dataset, and none of it scales easily. It’s also, by its very nature, something that lives disproportionately in senior practitioners, which creates a structural problem for organizations investing heavily in AI-driven efficiency without thinking about where the next generation of senior practitioners is going to come from.
The value of human judgment comes from what a dataset cannot capture: accumulated context, lived experience, relationships and pattern recognition. Those qualities make judgment a powerful differentiator and an inherently difficult capability to scale.
The AI dividend: what is it, and what are insight teams doing with it?
The MRS’ CEO, Jane Frost, makes a pointed observation in the Research Live report: “We need to model how we utilize the capacity that is created by the use of AI, so the ‘dividend’ isn’t frittered away or scraped into budget efficiencies.” At WBBF, Mike Peng also talked about the AI dividend; AI creates human surplus. What will you do with it? As Jane alludes to it, there’s a risk it enables the commodification of insight, a race to the bottom, where teams are pressured to cut, cut, cut until there’s nothing left.
Another risk is that teams use the time saved by AI to do more of the same work, faster, getting to the same outputs and outcomes. What if we slowed down? What if we invested the surplus in the deeper, slower, judgment-intensive work that creates the most value? In-person ethnography, customer immersion, stakeholder workshopping, or even just focused thinking time to really sketch out the downstream consequences of the insight.
Insight leaders who are using AI to free up time to invest in building deeper and wider stakeholder relationships, explore strategic challenges and furthering team development are making fundamentally different bets than those using AI primarily to compress timelines and cut headcount.
I’m a head of insight. What questions should I be asking myself and my team?
Like all change, especially the big, earth-shaking, big C change, the shift to AI-powered insight brings opportunities as well as challenges. Some heads of insight are already operating at the coalface of this change. Virgin Media O2’s head of insight, Claire Rainey, talks about a team that’s operating at a more strategic level, with more exposure to senior leaders across the business, acknowledging the trade-offs; investing in these stakeholder relationships inevitably means there’s less time to get into the detail.
Eddie at Sage is bringing together research, analytics and customer experience to build communities of best practice. He’s looking for partners who are ready and willing to “integrate methods to create a richer, more rounded view of the customer.” And for the BBC’s Director of Audiences, Nick North, public engagement exercises have resulted in greater insight than ever, but they’ve also highlighted the role of compelling storytelling to influence decisions and drive change.
If I were a client-side head of insight, here are the questions I’d be asking myself:
- Where are we reinvesting the AI dividend in our insight team?
- Are my junior researchers, who came of age in a post-COVID hybrid working world, with less exposure to senior colleagues they could learn from—developing human judgment or just reviewing AI-generated outputs?
- What would it take to deliberately design for the development of human judgment and critical thinking in our insight team?
- Do we have an AI governance strategy for our insight function in place? What kind of thinking and work does it presuppose or enable?
- Are we spending enough time with stakeholders to be genuinely context-fluent?
- What has been deprioritized as AI makes the production of insight faster?
