What does Adaptive Edge do?

Adaptive Edge is a UK strategy and innovation consultancy. We help organisations decide what to change, then make the change real: strategy built with the people who must carry it, AI adoption inside real work, and behaviour change that sticks.

How is Adaptive Edge different from a traditional strategy consultancy?

We do not hand over a deck and leave. Strategy is made in working sessions with your own teams, so the people who must deliver it own it from day one. We prove an approach in one team before asking the whole organisation to change.

Who does Adaptive Edge work with?

Global organisations across healthcare, industrials, media, retail technology and financial services, including ITAB Group, Freudenberg, Sky and Sandvik Coromant. Engagements run across Europe and beyond, remote and on site.

How does Adaptive Edge approach AI adoption?

We put AI to work inside real workflows rather than running abstract training. Teams learn on their own live work, we measure what actually changes, and we scale only what proves useful.

What does a typical engagement look like?

Most engagements combine focused research, co-created working sessions, and hands-on tools built for your context. They range from a single workshop to a multi-phase transformation programme.

How do we start a conversation?

Use the contact form on the site. We usually begin with a short call to understand the decision you are facing, and we will tell you plainly if we are not the right fit.

The Oracle Effect: Finding Answers vs. Finding Understanding in the Age of AI

By Nathan Waterhouse · 2025-08-12

I've started having a worrying thought. It came from watching my mum use ChatGPT to solve a tech problem. She had an issue with her phone – the kind that would normally result in screenshots sent to me or my brother, usually blurry photos of whatever interface was giving her trouble. But this time, she just asked ChatGPT and solved it herself. Didn't even tell us. It was done.

For a moment I basked in the incredulity of this. I imagined a life where all my mum's tech problems are solved by AI. But as that feeling faded, I noticed something else: her trust had shifted from me and my brother to ChatGPT. And there's probably nothing wrong with that. But a sense of unease still remained.

I heard recently about how many teenagers are using ChatGPT to fix their relationship issues (33%), like a sort of life counsellor, and that 31% of teen AI users say those conversations are as or more satisfying than with real friends. Maybe there's nothing wrong with that either. But it got me thinking: is ChatGPT becoming our oracle? The thing we trust to solve all our problems?

The Journey We've Lost

I thought back to myself as a teenager, wondering about things like "why is the sky blue?" or "why does it turn red at sunset?" Usually these questions came to me late at night, and I'd ask my dad. He's a physicist, so he'd give pretty good answers. But for other questions, I might ask a friend. When they didn't know either, I'd head to the library.

There was no book that would just answer all my questions. I'd start with an encyclopaedia, looking things up from A to Z. Maybe find a specialist book, ask the librarian for advice. I'd triangulate all these different sources until I had an answer. This would take hours, sometimes days.

When the internet came along, I found it fascinating that all these sources were suddenly available. I think one of the first things I searched for on my brother's engineering department Unix terminal was, ironically, a library: Bibliotheca Alexandrina, a recreation of the ancient Library of Alexandria. Then came Google, giving us tens of thousands of answers. Good luck figuring out which one was right.

Fast forward to today. We have AI models that give us condensed, personalised, articulate answers. A single source of truth.

What Have We Lost in the Search?

So what's wrong with that? Isn't it more convenient? Faster?

The itch I'm trying to scratch here is this: what have we lost in the search for answers? Is the journey itself of intrinsic value?

I guess the answer is: it depends. When my mum solves her IT issue instantly, that's brilliant. Why should she spend hours on the phone with me or some call centre? I welcome that change.

We might call that kind of knowledge 'transient': we probably won't need it that often, and who knows, maybe we will not even be using sim cards in a few years.

But when it comes to core knowledge, like which foods help lower cholesterol or how to resolve team dynamics at work, these are things we want to understand and remember deeply. Finding the answer instantly risks losing the context, connections and judgement that come from the search itself. I enjoy that journey. My hunch is that I remember these answers more durably because of it.

Research on the "levels of processing" in cognitive psychology supports this: deeper, more effortful engagement tends to make memories stick.

I can still remember the evening my father told me about Rayleigh Scattering: refraction and the colour spectrum. How the sky was blue in the day and at night, the angle changes and because of the thicker layer of atmosphere, the refracted light is more red. I can vividly remember where we were sitting. I'll never forget that knowledge.

The Science of Forgetting

It turns out my hunch about memorable learning has science behind it. A 2025 MIT study showed that people are much more likely to forget things they learn through AI. EEG readings revealed that students using ChatGPT had weaker brain connectivity in areas associated with creativity and memory. Nearly 80% couldn't recall what they'd "written" later.

Another study found something even more troubling. When writers from the US and India used AI assistance, their cultural differences disappeared. Indians stopped mentioning specific spices in biryani. Everyone's essays converged on pizza and Christmas. The AI was flattening their unique perspectives into a bland middle ground.

The Oracle Effect

This is what I call the Oracle Effect. Search engines show you thousands of results and leave you to figure it out. AI presents itself as the final authority. One prompt. One answer. And we accept it more readily than we might expect.

A 2025 KPMG survey found that 66% of professionals now rely on AI output without verification. But what that statistic does not capture is how this is rewiring our research instincts. We are losing the habit of exploring multiple perspectives, weighing conflicting evidence, and arriving at our own judgement.

And because AI draws from its training corpus – largely the internet – its answers tend to converge on the average view. We get the middle of the bell curve, not the edges. The extremes, the outliers, the rare perspectives that often spark real breakthroughs, are flattened or filtered out.

The problem is not accuracy. It is that innovation thrives when we explore widely. If everyone gets the same answer, we risk narrowing our thinking and producing the same ideas.

Why This Matters

There's an important implication for leaders and organisations here. Innovation research shows that the broader our horizons when we search for information, the better our innovations are. If we all end up with the same answers, we're going to converge on a bland future where nothing's very different and nothing's particularly competitive.

I think that future is not one I want to live in.

Keeping Our Horizons Broad

So how can we keep the search for information memorable and enjoyable, but also efficient and effective? Here are some approaches I've been experimenting with:

The Twenty-Minute Rule - Before I ask AI, I spend twenty minutes with the problem myself. I note what I think I know and what I am unsure about. I usually go for a walk, record my thoughts, then feed the transcript into AI and have it help me clarify the question into a research plan. Only then do I start looking for answers.

Diverge Before You Converge - When researching customer behaviour for a global client, we first imagined what different cultures might value. Japanese customers might prize ritual and patience. Brazilian customers might prioritise relationships and celebration. Only after mapping these lenses did we use AI to explore each one. The results were richer and more distinctive.

Create AI-Free Zones - The Wharton School calls this avoiding the "AI efficiency trap". A venture capital firm I know has AI-free Fridays. They research in the old way and say these sessions often produce their most original insights.

Practice Peripheral Vision - Innovation often comes from unexpected connections. Post-it Notes came from a failed adhesive. Microwave ovens from radar research. In one retail project, we deliberately looked for "extreme users" with unusual behaviours to spot future trends.

A Question for You

I know this might feel overwhelming if, like me, AI has become integral to your workflow. But this isn't about going backwards, it's about going forward more thoughtfully. Think of it as adding a wide-angle lens to your toolkit, not throwing away the telephoto.

In a world where everyone turns to the same oracle, your peripheral vision is not just an advantage. It is your only defence against irrelevance.

If my mum's phone can be fixed in seconds, that is progress. But if all our questions are answered in seconds, without the joy of searching, we risk losing something far harder to recover.

How might we build more moments of exploration and discovery into our work with AI? I would love to hear what you are trying.

Finding the balance between AI's efficiency and our human need to wander and wonder might be one of the most important skills we develop.