Through the Lens
Before you open that chat window, think
There is a discipline that used to be central to good consulting. It didn’t have a name – it was just what you did. You received a brief, a framework, a set of industry guidelines, and then you sat with it. You asked: does this actually apply here? What needs to change for this client, this industry, this user group? What’s the gap between the generic model and the real problem in front of me?
That discipline – the discipline of interpretation – is quietly disappearing.
I’ve caught myself nearly doing it. You open a chat window, paste in a question, get back a framework, and for a moment it feels like the thinking is done. The output looks convincing. It’s drawn from legitimate published thinking. But nobody has interpreted it. Nobody has tested it against the organisation’s constraints, culture, or users. It’s a template that feels like an answer – and the danger is how convincing it looks.
That’s the problem I want to talk about.
What the research is actually telling us
The question being asked in research circles is pointed: is habitual AI use gradually eroding the cognitive skills professionals most depend on?
MIT’s Media Lab published findings that sparked significant debate. Participants who used ChatGPT to write essays showed significantly weaker brain connectivity on EEG scans compared to those who wrote unaided. Over four months, the AI-assisted group became progressively more passive – cutting, pasting, producing generic output and many couldn’t recall what they had “written” minutes after completing it. The researchers coined the term cognitive debt: the accumulating cost of outsourcing thinking to a machine. The study is small and not yet peer-reviewed, but its direction is consistent with what larger research is finding and even AI tools themselves, when asked directly, acknowledge the risk.
The larger quantitative evidence comes from a peer-reviewed study published in Societies (2025) by a researcher at SBS Swiss Business School, drawing on 666 participants across the UK spanning students, specialists, and senior managers. The findings were striking across three relationships. AI tool use and critical thinking showed a strong negative correlation (r = -0.68). AI tool use and cognitive offloading – the tendency to delegate thinking to external systems rather than engage with it directly – showed a strong positive correlation (r = +0.72). And cognitive offloading and critical thinking showed an even stronger negative correlation (r = -0.75). In plain terms: more AI use leads to more offloading, and more offloading leads to weaker critical thinking. Younger participants showed the highest AI usage and the lowest critical thinking scores. Older participants showed the reverse. Higher education partially buffered the effect – but did not eliminate it. One interview participant in the study put it plainly: “The more I use AI, the less I feel the need to problem-solve on my own. It’s like I’m losing my ability to think critically.”
The Harvard Gazette put this question to faculty across education and philosophy, and the answers were measured but pointed. Researchers at Harvard’s Graduate School of Education noted that the risk is not AI itself – it is using AI as a substitute for thinking rather than an augmentation of it. Generative AI, one researcher observed, does not understand human context. It cannot provide wisdom about social, emotional, and contextual factors, because those are not part of its training. Another drew a parallel to GPS navigation: just as turn-by-turn directions have left many people knowing their current city’s streets in far less detail than streets they learned before smartphones, LLMs will gradually enable professionals to avoid the challenging mental work they stop practising. The consensus across the piece was careful: there are reasons to be optimistic and reasons to be concerned, but treating AI as a general reasoner for any cognitive task at all – rather than a targeted tool within a deliberate process – is where the risk concentrates.
These aren’t anti-AI findings. None of the researchers involved are calling for a return to pre-AI workflows. The concern is narrower: sequence. When AI becomes the first move rather than a later one, the thinking that used to happen upstream simply stops happening. And in enterprise delivery, that upstream thinking is often the most valuable part of the engagement.
The consulting craft that’s going missing
16+ years of enterprise delivery has taught me one consistent lesson: the generic framework is never the answer. It’s always the starting point.
A published architecture pattern for case management doesn’t account for the fact that your client’s agents work across three time zones, or that their legacy system uses non-standard status codes, or that their governance board will never approve a cloud-only solution. The pattern is valuable – but only once someone has done the interpretive work of translating it into a fit-for-purpose model for this organisation, this user group, this set of constraints.
A governance model that lands well in a UK public sector body won’t work the same way in a Japanese enterprise. An integration pattern standard across Europe may be a non-starter for a Singapore financial institution with different regulatory requirements. That translation from generic framework to fit-for-purpose solution is where the real value sits – and no AI tool can do it, because no AI tool has sat in those rooms, read those stakeholders, or navigated those cultural dynamics.
That interpretive work used to be the craft of consulting. It required experience, contextual judgement, and a willingness to sit with uncertainty before reaching for a solution. It was slow, sometimes uncomfortable, and not always billable in an obvious way. But it separated good consultants from people who had read the same published articles as the client.
What I’m observing now – and the research backs this up – is that AI is quietly taking that discomfort away. And when the discomfort goes, so does the thinking.
Ryan Roslansky’s inconvenient reminder
I picked up Open to Work recently – it was recommended by someone from Microsoft at an AI event, and it’s been sitting with me since. Co-authored by LinkedIn’s CEO Ryan Roslansky and Aneesh Raman, the book’s core argument is not a technological one. It’s a human one.
Roslansky identifies five attributes that will define professional value in an AI-augmented world: the 5 C’s. None of them are prompt-engineering skills.
His central argument, as I took it: the future of work will be driven by human creativity and ingenuity, not technology alone. When humans stay at the centre, AI amplifies what people do best.
Reading it, what struck me was how rare genuine clarity has become – not an AI-generated summary, but a real point of view formed by someone who has sat with a problem. That capacity to form an original view before the chat window opens is the professional skill under the most pressure right now.
The sequence is everything
I’m not making an anti-AI argument. In my line of work, I use AI tools to do things that used to take days in a fraction of the time – and I’ve built agents that do the same for others. That leverage is real. But the operative phrase is used well. And used well means used in the right sequence.
Think first. Then use AI.
That’s it. That’s the principle. Before you open a chat window:
- Form your own view. What do you actually think the problem is? What’s your instinct about the solution? Even a rough, imperfect hypothesis is more valuable than a clean AI-generated one – because it’s yours, grounded in your direct experience of this client and context.
- Use AI to pressure-test, not to originate. Once you have a view, use AI to challenge it. Ask it to identify gaps in your thinking, surface alternatives you haven’t considered, find the research that supports or challenges your hypothesis.
- Build on the synthesis. The output you deliver is the product of your judgement augmented by AI – not an AI output with your name on it.
This is what Human-in-the-Loop means in practice. Not a governance checkbox. Not a compliance requirement. A design philosophy: humans originate the thinking; AI extends and accelerates it. The sequence runs human → AI → human, not AI → human → copy-paste.
The cost of getting the sequence wrong
When the sequence inverts – when AI originates and humans rubber-stamp – several things break quietly.
Accountability becomes diffuse. When something goes wrong in a programme built on an AI-generated framework that no one interrogated, who owns the failure? The person who presented it? The tool that produced it? The client who accepted it?
Contextual fit degrades. AI tools train on broad, published knowledge. Your client’s culture, history, constraints, and failure modes don’t appear in that training data. Only the person in the room holds that context. When that knowledge doesn’t inform the AI output before adoption, it never gets incorporated.
Professional credibility erodes. Slowly, and then all at once. Clients are getting better at recognising generic AI output. The consultant who arrives with a genuinely interpreted, contextualised recommendation is becoming rarer – and, as a result, more valuable.
What good looks like now
The professionals who will differentiate themselves in the next five years are not the ones who use AI most fluently. They’re the ones who combine AI fluency with something AI cannot replicate: the discipline to think before they prompt.
Roslansky’s 5 C’s – courage, curiosity, creativity, communication, compassion – are not just soft skills. These are the cognitive and relational capabilities that let a professional do the interpretive work that sits upstream of any AI tool. Curiosity drives the right questions. Creativity shapes the interpretation. Courage means sitting with uncertainty rather than reaching for the nearest framework.
The question worth sitting with – before you open the next chat window – is simple: what do I actually think about this?
Start there. Then use AI to go further.
I’m a 3× Microsoft FastTrack Recognised Solution Architect, specialising in AI governance and delivery, programme recovery, and enterprise architecture. If this resonated, connect with me on LinkedIn: linkedin.com/in/mike-richard