DVL Smith

DVL Smith DVL Smith Limited (dvlsmith.com) is a business consultancy, and Polymathmind.ai focuses on building the human skills needed to work alongside AI.

Visit the PolymathMind Substack here: polymathmind.substack.com DVL Smith provides training, coaching and consultancy in business and customer insight. We have been conducting insight training and major global insight projects for more than 25 years for some of the world's biggest companies. We would be delighted to respond any to challenges where you feel we could add value. We are always keen to tailor what we do to the precise needs of our clients. Please contact David on LinkedIn or at [email protected]. INSIGHT CONSULTANCY
We are totally committed to providing innovative solutions for our clients We achieve this by always seeing the big business picture, going deep into what is driving customer attitudes and through our experience in turning insights into action. INSIGHT TRAINING & COACHING
We focus on building insight professionals’ ability to make sense of today’s complex consumer evidence, to construct compelling insight narratives, and to work effectively with stakeholders in applying insights. The platform for our training is my book - 'The High performance Customer Insight Professional' (https://amzn.to/2GOzC8j)
For insight professionals who would like to enhance the way they communicate their insights to stakeholders we have our online programme - 'Tell the Insight Story: The Seven Story Tools System'. (https://bit.ly/2Q3zuGI)

EXPERIENCE & AWARDS
David is a Fellow of the UK Market Research Society, the Chartered Institute of Marketing and the Institute of Consulting. He is a Certified Management Consultant. He has a PhD in Organisational Psychology from the University of London. He is a Visiting Professor at the University of Hertfordshire Business School. He is a former Vice President of ESOMAR and a former Chairman of the UK MRS. We have multiple MRS Awards including its Silver Medal. We have multiple Awards from ESOMAR including the Award for Excellence in Marketing Intelligence. David also holds AURA’s Alan Hawks Award for Driving the Insight Industry Forward.

Do consultants face a sort of existential 'I exist, and nobody cares' question?If AI can research the market, analyze th...
12/08/2026

Do consultants face a sort of existential 'I exist, and nobody cares' question?

If AI can research the market, analyze the evidence, generate strategic options and turn the argument into a polished slide deck in minutes, what exactly is the client paying for?

Robert Armstrong raised this in the FT last week. His argument is that much of the analytical work of strategy consulting is becoming commoditized, leaving the harder human work of trust, persuasion, organizational politics and change. That’s right as far as it goes but (we would argue) misses something important.

Before you can persuade a client to act on a recommendation, someone still has to decide whether the recommendation is any good. In other words: exercise experience based judgement. Sound judgement is the point of differentiation between humans and AI.

So the challenge isn’t simply how to use AI to make consulting faster and/or cheaper. It is how to integrate the power of AI without hollowing out the human capabilities on which the profession increasingly depends.

Adam Riley and I consider the challenge in our latest PolymathMind Substack.
https://polymathmind.substack.com/p/consultant-heal-thyself?

How AI Is Changing Management Consulting — and Making Human Judgement More Valuable

The central theme of the article and posts Adam Riley and I at Polymath Mind have published this week is that the Govern...
03/08/2026

The central theme of the article and posts Adam Riley and I at Polymath Mind have published this week is that the Government’s welcome investment in AI skills training must be underpinned by sound strategic planning.

We are mindful, of course, that there is considerable fatigue around yet more calls for AI strategy sessions. A more popular line might have been simply to congratulate the Government for doing something concrete: committing to upskill and reskill 10 million workers by 2030.

And, to be fair, we have congratulated them.

But the clue to why we keep emphasising strategic thinking lies in the initiative’s target date: 2030. Who knows were we will be with AI by then. AI is evolving very fast.

The announcement therefore raises a fundamental question: has sufficient strategic thinking been done to ensure that the skills developed between now and 2030 will remain relevant and genuinely drive productivity and growth?

Answering that takes us firmly into strategic territory. It is not enough to say, “We will lead the way in AI skills training.” We need to address the grown-up questions:

What problem are we trying to solve?

Which human cognitive capabilities must we strengthen to achieve our goals?

How do we retain human judgement within decision-making while taking full advantage of AI?

The answers to these and other critical questions will determine the shape of the training and, ultimately, whether it succeeds.

This analysis may lead us to conclude that, because AI is evolving so rapidly, training should focus less on particular tools and more on helping people improve the quality of their thinking when working alongside AI: the metacognition piece.

This is not fancy jargon. It is central to successful AI training.

The goal should be to amplify people’s thinking capabilities, not merely give them a quick introduction to the latest AI gizmo. We need to strengthen core cognitive skills so that people can continually adapt as new tools arrive.

Strategic thinking will guide us towards teaching people how to think in the AI era: when to offload tasks intelligently to AI and how to avoid lazy cognitive surrender.

That means moving beyond AI skills training as a box-ticking exercise centred on an application that could be obsolete almost before the course is completed.

It means building a workforce with the cognitive mindset and capabilities required to thrive in the emerging era of collective intelligence.

Read our latest Substack article here.

An open letter to Andy Burnham on the missing half of Britain’s AI strategy

“AI makes you smarter but none the wiser” is the phrase from the Fernandez et al paper that stood out.Their point that A...
09/07/2026

“AI makes you smarter but none the wiser” is the phrase from the Fernandez et al paper that stood out.

Their point that AI can improve task performance while leaving people poorly calibrated about their own ability matters. This is because a great deal of AI adoption is currently being judged primarily through output.

And, of course, questions such as: did we produce more? Did it take less time? Was the draft better? Was the analysis cleaner? are important.

But these questions alone are not enough. They tell us what the human -AI system produced. But they do not tell us what the human learned at a deeper level about the challenge under investigation.

And this gap is where the trouble starts. Better outputs generate confidence. But confidence can reduce scrutiny and become dependence. And dependence can start to look suspiciously like competence – especially from the outside.

This is why I’m wary of AI capability programmes that focus primarily on tools, prompts and productivity gains.

Prompting matters of course, but it is not the same as the deeper capability of knowing how to think with the tool without letting the tool think for you.

The challenge is that most AI interfaces are built for smoothness. The user asks and the system answers.

But in this way the friction that often promotes learning disappears. We lose the edgy creative friction where deep learning and true understanding takes place.

We want to encourage and reward processes that ask: what did you think before you asked the machine? Why do you believe that? What evidence would change your mind? What might be missing? Where could this answer fail?

Recent work on AI “provocations” is useful here: systems that challenge assumptions, surface counter arguments and force a pause for thinking before acceptance are important.

If AI is going to build capability it has to make us think harder at the right moments.

The aim should not be to use AI less. It should be to use it in ways that make our human thinking stronger.

AI may make us perform better before it makes us better.

At PolymathMind.ai we have been examining the threat that comes with the AI medium luring us into accepting outputs that...
02/07/2026

At PolymathMind.ai we have been examining the threat that comes with the AI medium luring us into accepting outputs that should have been more thoroughly checked, double-checked and rigorously evaluated.

But the bigger threat is the almost sinister way in which this deference to AI authority sneaks up on us, slides under the door and by stealth becomes the norm.

The worry is how easily we slip into accepting AI’s outputs without the clear, deep, forensic evaluation to which we would normally have subjected such information.

So the big risk is that people stop noticing that they have stopped checking. It is akin to the death of human thinking by a thousand cuts, because we have not noticed what is happening: the steady erosion of our questioning muscle.

One minute we are rigorously and intellectually checking everything. But, over time, we lazily drift into accepting AI’s convenient first draft in an unquestioning way. The opening gambit from this new medium that is AI sneaks up on us until it becomes “the way we do things around here now”.
So, the speed with which humans may be handing over authority to AI is surely cause for concern.

The practical question is not simply, “Should we trust AI?”

It is, “What would AI dependence look like if we stopped noticing, if we stopped checking, if we stopped asking, ‘Are you sure this is the best you can do?’

If you cannot answer that for your team, then you could be in trouble!

https://polymathmind.substack.com/p/please-do-not-feed-the-machine

What Plato, Forster and McLuhan can teach us about AI, cognition and the future of knowledge work

Picking up on framing issues in a binary way being a trap when it comes to unravelling the complexity of likely impact o...
25/06/2026

Picking up on framing issues in a binary way being a trap when it comes to unravelling the complexity of likely impact of AI, I think binary thinking survives because choosing a side is less effort than holding four positions , each with an element of truth , and admitting none of them cancels the others out.

Polanyi’s observation was that we know more than we can tell — that real competence, the kind a skilled clinician or engineer has, doesn’t reduce neatly to a procedure you could hand someone in writing. I think the same is true of judgement about AI. You can train someone to use the tools in an afternoon. You cannot train them, in an afternoon, to notice when the speed of the answer has quietly become a substitute for the quality of the thinking behind it.

That’s the actual shortage. Not AI skills — there’s no shortage of those, and there will be even less of one by Christmas. The shortage is in people who can sit with an AI-generated answer and ask whether it’s good, rather than whether it was fast.

Read the latest Polymathmind.ai Substack article from Adam Riley and I below.
https://polymathmind.substack.com/p/a-golden-age-for-whom

AI ‘Golden Age’ or Power Grab? What do the latest interventions by Bezos, Khosla and Trump Actually Reveal

This week's Polymathmind.ai Substack article from Adam Riley and myself points to Malone's finding that AI-human teams o...
18/06/2026

This week's Polymathmind.ai Substack article from Adam Riley and myself points to Malone's finding that AI-human teams often underperform the best of either alone. The question this raises is a useful one: when does AI augmentation actually work, and when does it degrade thinking?

We reference research from Lebovitz, Lifsh*tz-Assaf and Levina, that distinguishes between engaged and unengaged augmentation.

In engaged augmentation, the professional interrogates the AI's output. They relate the AI's claim to their own knowledge, judgement and context. They may overrule it, reflectively agree with it, or synthesise something new. The AI sharpens the thinking; the human stays in charge of it.

In unengaged augmentation, the AI's output is accepted without that interrogation. The human is in the loop on paper. In practice, the AI has done the thinking.

What separates the two is not the technology. It is the human capability being applied.

The capabilities that produce engaged augmentation are the ones we have been describing as Power Skills for three years: contextual sensemaking, abductive reasoning, the discipline to challenge a fluent answer that looks finished. They are the difference between AI as scaffold and AI as substitute.

Most organisations are investing significantly in the technology. Far fewer are investing in the capabilities that determine whether the technology pays off.

Where in your organisation is augmentation engaged - and where has it become something else?

Human-AI collective intelligence: why most organizations are getting it wrong.

The Substack from Adam Riley and I this week makes an argument I want to push one step further. The data shows that huma...
11/06/2026

The Substack from Adam Riley and I this week makes an argument I want to push one step further.

The data shows that human involvement in AI-assisted decisions remains high - the UK Government’s AI Adoption Research found 84% of businesses using AI report at least some human input or checking, with 67%
reporting significant input.

On the face of it, this is reassuring. Humans are in the loop. But “in the loop” is not the same as “meaningfully engaged.”

A person can be technically present at a decision while lacking the time, training, authority or confidence to actually challenge an AI output. A manager can remain accountable for a recommendation without understanding how it was produced. A team can check an AI-generated
answer superficially - reading it for fluency rather than interrogating the assumptions beneath it.

This is the gap that worries me most. Not whether humans are present, but whether their presence is actually doing anything. The risk is what we might call ‘decorative or performative accountability’ … the appearance of
human oversight without the substance of human judgement. It satisfies governance frameworks. It survives audits. It is also exactly the condition under which serious errors get waved through, because the people who could/should have caught them were structurally unable to do so.

Closing this gap is not a technology problem. It is a capability problem. It requires teaching people to challenge fluent, confident systems - which is harder than teaching them to use those systems in the first place. Most organisations have invested heavily in the second and barely at all in the first.

If we want human judgement to remain real rather than ornamental, that imbalance has to change.

AI adoption is real but maturity is uneven. What the latest evidence says about jobs, judgement and the entry-level squeeze

Most AI adoption conversations focus on speed. More drafts, more options, more outputs, faster (and cheaper!).But the or...
11/06/2026

Most AI adoption conversations focus on speed. More drafts, more options, more outputs, faster (and cheaper!).

But the organisations that will win with AI aren't necessarily those that automate the most. They're those that invest in the human capability that determines what AI is asked to do, and whether the answer it gives is any good. Sharp framing going in produces sharper output. Shallow framing produces shallow output - just faster and at greater scale than before.

We talk about three things at Polymathmind.ai: sharper thinking, bolder creativity, inspiring communication. Not because they're nice to have. But because in a world where AI can produce competent work cheaply, these are the capabilities that create distinctiveness. The human advantage in a world of human-AI collective intelligence.

The premium moves to the people who can frame problems well before the machine is involved. Who can sense when the plausible answer is the wrong one. Who can turn AI output into something that actually means something to the people it's intended for.

Adam Riley and I wrote about this in the context of Bill Winters' "lower-value human capital" remark. Why not read our article (in comments) — worth reading if you're thinking seriously about where to invest in your people.

What is your organisation actually doing to build higher-value human capital? Not just tools training but real capability building!
Read the Substack below...

The real AI challenge is not replacing low value human capital but instead building higher-value human capability

Here is a question worth addressing. If AI compresses twelve hours of junior work into fifteen minutes, who pays for the...
28/05/2026

Here is a question worth addressing.

If AI compresses twelve hours of junior work into fifteen minutes, who pays for the apprenticeship?

Professional services were built on a quiet bargain. Clients paid for some inefficiency — the second-year analyst rebuilding the model, the junior associate redrafting the memo — because that was how the next generation of expertise got made. The inefficiency was the training budget. It just sat on the invoice.

That bargain is under pressure on both sides. AI strips out the hours. Clients see the new price and reasonably ask why they should pay for the old one. The firm captures the margin in the short term. The training budget quietly disappears.

Nobody decided to defund the apprenticeship. It is being defunded by a thousand efficiency decisions, each individually sensible.

Intuit’s May announcement - 17% of the workforce gone, refocused around AI -illustrates the pressure operating at two levels. AI changes how firms organise work internally. It also threatens the revenue models that sustained those roles in the first place. The juniors who survive will be climbing a steeper, sharper-elbowed pyramid. Fewer rungs. Less time at each. More expected at every level.

The economics of “learning on the job” only worked when the job contained the learning. Strip the learning out of the job and you have a workforce of senior people with no successors and junior people with no path.

This is not an AI problem. It is a governance problem that AI is making urgent. The essay this week from Adam Riley and I sets out what business leaders should do. The honest first step is to admit that the bargain has broken — and that no one has yet decided who refinances it.

Why AI may not destroy every entry-level job, but could still break the master-apprentice model of knowledge work

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