DVL Smith

DVL Smith DVL Smith is a consultancy that provides transformational skills training and coaching for insight professionals. We also offer customer insight consultancy.

www.dvlsmith.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.

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

Adam Riley and I wrote this article about the cognitive cost of AI fluency. I want to add a layer to that argument.The A...
21/05/2026

Adam Riley and I wrote this article about the cognitive cost of AI fluency. I want to add a layer to that argument.

The Alvesson and Spicer concept of “functional stupidity” – where organisations suppress challenge and substantive reasoning while still appearing effective – describes a problem that pre-dates AI.

What AI does is accelerate it.

When a first draft arrives already polished, the temptation is to move straight to refinement. Critical review becomes surface improvement. The first draft becomes the final position with better formatting.

This is how judgement erodes – not through stupidity in the ordinary sense, but through what Kant called organised immaturity. People stop being asked to think, so they stop expecting to.

In our consulting work, this shows up clearly. The teams getting most from AI aren’t the ones using it fastest. They are the ones who have deliberately designed friction back into the moments where judgement matters
most.

The cognitive muscle is built through wrestling with uncertainty. Not through reviewing what someone – or something – else has already wrestled with.

What would your organisation lose if no one had to draft anything from scratch again?

Why AI makes human judgement more important, not less

Adam Riley and I have been discussing this piece for weeks, and I think the discussion has been instructive.Adam’s insti...
21/05/2026

Adam Riley and I have been discussing this piece for weeks, and I think the discussion has been instructive.

Adam’s instinct, drawn from the consultants in his study, is that the most important shift is psychological — the discomfort, the renegotiation of professional worth, the lived experience of working with a tool that is both useful and unsettling.

My instinct, with my academic hat on, is that the most important shift is structural. Knowledge work has long been organised around a particular division of labour: juniors do the analytic graft, seniors carry the judgement. The pyramid produces both the output and the expertise. AI doesn’t disturb this gently. It dissolves the bottom of the pyramid faster than the top can adapt.

Both readings end in the same place, which is why the piece works. The psychological discomfort Adam describes and the structural pressure I describe are two views of the same problem. We are decoupling the production of expert outputs from the development of expert judgement, and we are doing it without a serious plan.

The line that has stayed with me is this: the work that looks inefficient may be the work through which people become good. That line is the one I would underline in this piece.

Read our PolyMath Mind Substack article here

Four findings from inside consultants' working lives — and what they reveal about the future of knowledge work

A confession from inside the system: higher education is not currently built to produce the people the AI economy needs....
21/05/2026

A confession from inside the system: higher education is not currently built to produce the people the AI economy needs.

I don’t mean this as provocation. I mean it structurally. We are organised around producing specialists — deep, narrow, credentialled — and we are rewarded, individually and institutionally, for going further in that direction. That model worked when the entry-level work surrounding a junior specialist gave them, by accident, the breadth they would need to become senior. The graduate trainee at the law firm wasn’t only drafting contracts; they were absorbing the firm, the client, the judgement, the politics. The work was the curriculum.

AI is now doing that work. Faster, cheaper, mostly well enough. Which means the curriculum has gone, and the specialists arriving on day one are turning up without the breadth that used to come bundled with the job.

This is the part of the AI-and-jobs debate that almost nobody is having. We argue about whether AI replaces jobs or augments them. We do not argue nearly enough about what happens to professional formation when the bottom rung disappears — and what it means that the entire architecture, from undergraduate degree to chartered qualification, was built on the assumption that the rung would be there.

Adam Riley and I have a piece on the PolymathMind Substack pulling at this thread, hung off Rishi Sunak’s two rather different positions on AI. Our provocation: AI-mediated work is collective intelligence, and the collective only performs if the human side is upgraded as deliberately as the machine side. Almost nobody is doing that deliberately yet.

Why clients need more than AI literacy, they need improved humans to get the most from AI

One of the central challenges in the AI era is knowing when human judgement, creativity and intuition should take the le...
27/04/2026

One of the central challenges in the AI era is knowing when human judgement, creativity and intuition should take the lead — and when AI can make a powerful contribution.

Over the last few months, this question has been at the heart of our work at Polymathmind.ai as we have designed three capability-building programmes focused on human–AI collaboration in true collective intelligence mode.

The first programme focuses on how humans and AI can work together to produce sharper, deeper and more critical thinking when solving complex, nuanced problems.

The second explores how humans can collaborate with AI to generate genuinely creative and imaginative solutions — going beyond what either a human or AI system might produce alone.

The third focuses on how humans and AI can combine their strengths to craft persuasive business narratives, compelling stories and inspiring communications.

We believe the key is getting the balance right: leaving enough space for the original human voice, our empathy, our understanding of the human condition, and our natural creativity to flourish — while also recognising where AI can be called onto the stage to amplify, organise, challenge and pressure-test what we are developing.

We now feel we have arrived at the sweet spot: a capability-building approach that explains the human–AI symbiotic process in practical terms — collective intelligence in action.

To find out more about our approach, please contact us at Polymathmind.ai.

Here is the latest Substack article from Adam Riley and I.

Why Philosophy Matters More Than Ever (Part Two of Two)

Philosophy can sound abstract, even a little wishy-washy, until you translate it into what it really is: clear, deep, cr...
21/04/2026

Philosophy can sound abstract, even a little wishy-washy, until you translate it into what it really is: clear, deep, critical, creative and ethically grounded thinking. At that point, it starts to look far less like a luxury subject and far more like something that should be compulsory in every secondary school. So when we hear that the big AI companies are beginning to employ philosophers to help them navigate the challenges ahead, that surely has to be good news. Maybe it reflects a growing recognition that, as AI becomes more powerful, human values need to stay firmly in the loop. And I suspect Douglas Adams would approve of any effort to ensure we have more friendly, imaginative Ford Prefects in the mix than gloomy, robotic Marvins.

Here is the latest Polymathmind.ai article from Adam Riley and I...

Part one of two

What we’re finding especially exciting at present at PolymathMind.ai is designing training programmes that spell out pre...
13/04/2026

What we’re finding especially exciting at present at PolymathMind.ai is designing training programmes that spell out precisely how humans and AI can collaborate in a true collective intelligence mode. Our focus is on the three critical fields referred to in this post.

First, helping us sharpen our thinking - specifying where human judgement remains firmly in the ascendancy, while also acknowledging where AI can help us question our critical assumptions.

Second, boosting our creativity - retaining human ingenuity and empathy in teasing out insights, while learning how to use AI to unlock new angles and perspectives.

Third, helping us craft persuasive communications that are firmly anchored in the human voice and our understanding of what constitutes an authentic narrative, while also opening ourselves up to fantastic storytelling ideas that help our message land.

Read this latest article from Adam Riley and I.

The rise of collective intelligence at work

In discussions about whether AI will lead humans to delegate their thinking, rather than learn how to think better by wo...
13/04/2026

In discussions about whether AI will lead humans to delegate their thinking, rather than learn how to think better by working with it collaboratively, I am reminded of Germaine Greer, the celebrated intellectual and author. When asked to list her hobbies for her entry in Who’s Who — the esteemed directory of influential people — she simply replied: “thinking".

She embraced the agonising frustration, exhilaration, inspiration, challenge, and enjoyment of thinking through complex problems and coming up with creative ideas.

But we have to wonder, with AI becoming more powerful every day, whether the temptation simply to accept its outputs will prove too great. Will we lazily resist clear, deep thinking and instead slavishly accept the output from AI?

So we would all do well to follow Germaine Greer’s lead and start enjoying thinking — and now do so with our new collaborative thinking partner: AI.

Here is the new article from Adam Riley and I...

AI, judgement, and the ancient danger of mistaking tools for gods

I’ve always liked the "panorama principle" - the idea that context explains everything when we’re trying to understand c...
27/03/2026

I’ve always liked the "panorama principle" - the idea that context explains everything when we’re trying to understand complex change.

When faced with the choice between digging for ever more detail or stepping back to see the bigger picture, the wiser move is usually to widen the lens.

This feels especially relevant to the debate about where AI will take us.

It’s very tempting to cling to neat, reassuring visions of the future - tidy narratives that imply technological progress automatically leads to predictable social outcomes. These stories reduce uncertainty. They make the future feel manageable.

But history suggests it rarely works like that.

Where AI actually lands will be shaped by a messy interaction of forces: institutional choices, political agendas, economic realities, cultural priorities - and ultimately how strongly we as humans choose to define and defend our values.

The next decade is unlikely to unfold in a smooth, linear way. AI will create extraordinary opportunities - but also disruption, uneven progress and unintended consequences. There will be clear winners. There will also be losers.

The real skill will be learning to operate without the comfort of certainty: staying pragmatic, adapting quickly and moving forward milestone by milestone, rather than relying on grand predictions.

If there is a genuine source of reassurance, it lies in our agency.

The future is not simply something technology delivers to us.

It is something we shape - by ensuring AI reflects the human values we care about, and by placing those values at the centre of how we redesign work, organisations and communities in the years ahead.

Read the latest article from Adam Riley and I.

Why Tech Utopias Keep Appearing — and Why They Rarely Arrive

It is reassuring to see that when it comes to implementing AI most organisations are now recognising the importance of r...
20/03/2026

It is reassuring to see that when it comes to implementing AI most organisations are now recognising the importance of retaining strong human judgement within the final decision-making processes.

We are increasingly aware of the power of cultivating ‘collective intelligence’ - a genuinely symbiotic human-AI approach that enables us to get the very best from AI technologies.
The real challenge however emerges when time is short and the pressure to deliver is intense. If your AI companion has served you well in previous situations and its latest output appears professional, plausible, cogent and polished, it can be all too easy to abdicate that final human responsibility to double check and critically evaluate what it has produced.

This temptation towards intellectual complacency - or shallow thinking under organisational pressure - shines a spotlight on the growing importance of developing individuals’ 'metacognitive skills.'

In essence, this is our capability to think about the quality of our own thinking. It requires us to pause and ask whether we have stayed with the AI’s output long enough, wrestled with its implications deeply enough, and applied sufficient human judgement and instinct before concluding, ‘Yes - this is good to go.’
In the AI era we must build the habit of protecting that vital space and time for reflective scrutiny - a deliberate internal conversation with ourselves about what is really going on beneath the surface.
It is this rigourous human sense making, judgement and thoughtful interrogation of AI’s internal outputs that we must continue to safeguard and value as we move forward.

Here it the latest article from Adam Riley and I...

The human operating model is now playing catch-up to AI

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