Aug 19, 2026 | AI, Marketing
Cost reduction remains the default KPI for most manufacturing organisations. In a world where nearly every product category faces relentless commoditisation, aggressive new competitors popping up unexpectedly, and razor-thin margins, squeezing out cost feels like the only sensible survival strategy.
The alternative strategy is challenging, as it requires looking at a fundamental paradox in modern industry.
As automation has accelerated, machines have taken over repetitive tasks with unmatched reliability. Yet human labour hasn’t vanished, it is progressively relocating.
Value behaves much like the conservation of energy in Einsteins equations. It is not destroyed, it merely changes form under pressure and moves elsewhere.
If we apply this idea to manufacturing, the primary mandate of leadership is no longer just cutting costs. It is becoming the larger challenge of expanding the capabilities of employees and other stakeholders.
Value creation is migrating.
Over the last 50 years, machines mastered physical repetition, which reduces costs by replacing people, and maintaining consistency of throughput and standards. It also moves the point of value creation towards work machines cannot reliably perform: judgement, creativity, problem solving, and leadership.
As AI accelerates, we are confronted by the question of where those displaced by technology will go, what will they be doing, and what are the social and economic consequences of the changes.
In past technological shifts, manual labour to steam, then steam to electricity, horses to cars and trucks, fingers, and vacuum tubes to digital, the changes evolved over sufficient time for adjustments to evolve. There was pain associated with the changes, but it was limited albeit unevenly spread. However, each change occurred over a progressively shortening time frame. The AI driven tectonic change is happening in real time, before our eyes, so adjustment time will be very limited. Cost cutting may preserve short term margin, but it does not give customers a reason to choose you.
The scale and short-term horizon points directly toward the need to enable and enhance uniquely human skills: high-level problem solving, empathy, strategic collaboration, adaptability, and social intelligence. These are cognitive areas automation cannot easily replicate.
Despite dystopian sci-fi tropes, AI and advanced automation are simply tools waiting to be directed. Leaders who recognise where human value is moving, and actively reskill their workforce to meet it have the opportunity to redefine the ways they create customer value in their markets.
Those who remain fixated solely on cost-cutting will quickly find themselves commoditised into oblivion.
Aug 11, 2026 | AI, Marketing
In 1984, Dr. Robert Cialdini published Influence: The Psychology of Persuasion, outlining the six core drivers of human compliance:
- Reciprocity,
- Commitment & Consistency,
- Social Proof,
- Authority,
- Liking,
- Scarcity
Decades later, psychologist Daniel Kahneman expanded our understanding of human decision-making in his ground breaking book Thinking, Fast and Slow. By experimentation, with research partner Amos Tversky, he demonstrated how our fast, intuitive “System 1” brains routinely rely on these shortcuts to reach a compelling conclusion. The research on which the book explains in laymans terms earned him the Nobel prize in economics.
Both books issued clear warnings: these principles could be leveraged just as easily by bad actors as by those whose objective is positive.
Cialdini wrote for an analogue world of salesman, direct mail, and print advertising. He could not have foreseen the impact of the internet, social media, and generative AI.
In the analogue era, persuasion took time and human effort. Today, machine learning algorithms execute Cialdini’s drivers at immediate, micro-targeted scale.
The tiny safety mechanism that was present pre digital, of the opportunity to reflect on a choice has been removed by the speed of digital. Any chance rational analysis had of imposing itself on an immediate emotional reaction has been dramatically reduced.
Social proof is manufactured via engagement metrics; authority is mimicked by synthetic media; scarcity is simulated through algorithmic urgency, and consistency has built its own echo chamber.
The result is systemic fragmentation and hardening of an opinion forged by these forces. Once a person takes a position, persuading them to change becomes progressively harder when they perceive that the effort to change is an attack. Our brains evolved to be a safety mechanism, so attack hardens resolve to defend. This has generated the divisions we see around us every day.
Households, public discourse, political systems, and social licenses are splitting into opposing, and polarised positions.
The forces Cialdini identified have not changed, they are the result of millions of years of evolution. What has changed is that AI now automates persuasion on both sides of the divide, compounding the fracture every second.
Jul 24, 2026 | AI, Marketing
There was a time when advertising followed intent.
People searched for information, and businesses met them there with relevant answers. A second key attribute of the ‘old’ system was that advertising created mental availability. This resulted in a brand presence and positioning being already established when someone ventured towards a market. In other words, ‘Brand building’, a set of disciplines too often ignored by those injected with digital as a panacea to all marketing challenges. The exchange was simple: attention for information, need for solution.
That model has been inverted.
Accuracy and relevance are up for auction. Today, marketers rely on platforms like Google and Facebook to push ads towards loosely defined behavioural categories.
Whether the recipient is a genuine prospect or a bot farm is often beside the point. The system optimises for delivery, not relevance. In some cases, it seems that simply being human is no longer the qualifying criterion.
What used to be information-led discovery has become auction-driven visibility.
“Information” is now whatever wins the bid inside an opaque algorithm. The bidder willing to pay enough, while satisfying the platform’s quality formula gets the privileged slot, whether or not they offer the best answer.
It begins to resemble a protection racket worthy of the Corleone family more than a marketplace.
If you do not pay, you do not appear. Visibility is no longer earned through relevance or quality, but purchased through participation.
If we reverse the model, and are prepared to pay for clarity and priority for the needs of the information seeker rather than the advertiser, the logic shifts quickly towards paid access.
AI search sits awkwardly in a new middle ground we have yet to adequately define. It promises a clean, single ‘best’ answer without the clutter of ads. However, the definition of what constitutes a ‘best’ answer is unclear.
What it delivers is not truth., it is probability. A compressed synthesis of what has been said before, shaped into what is most likely to satisfy the query.
That works well for established facts. Ask for a public figure’s birth year, and you will probably get an accurate answer.
Move into ambiguity or historical uncertainty, however, and the cracks appear. The system does not “know” in any meaningful sense. It predicts.
Its goal is coherence, not correctness.
This creates a subtle but important risk: the illusion of authority without the guarantee of accuracy.
Meanwhile, alternatives are available, and more are emerging, where the user once again, becomes the customer.
Smaller search platforms are experimenting with models that shift incentives away from attention and towards trust. Some rely on subscriptions. Others allow limited, user-controlled advertising or blend in affiliate models.
What unites them is a simple premise: you are the customer, not the product.
Today, their market share is marginal, but the underlying value proposition is strong.
As digital environments grow noisier and more manipulative, the ability to control what reaches you becomes a premium feature.
That control will not be free.
But as with any system where incentives shape outcomes, which is most of them, paying for alignment rather than being monetised through distraction may prove to be a far better deal most of the time.
Jun 17, 2026 | AI
Most recognise the danger of AI. It delivers a slick, formatted persuasive response to a prompt. It has become way too easy to just accept the veracity of the response and move from the drivers seat to the back seat. This removes yourself from the sweat of doing the work, and the responsibility for the outcomes.
We see it all the time, often in spots where we accept that there has been a level of scrutiny that should deliver reliable outcomes. For example, the crapola pie Deloitte delivered to the government in June 2025, which then published the deeply flawed AI generated report without any review.
The answer is in the transparency of the process of assembling, analysing, and preparing the output. What is included, what has been ‘AI imagined’ to fill the gaps in the prompting and resulting workflow, and easiest to miss, what has been left out.
The challenge is rapidly compounding as we move into the ‘AI Agent’ world, where we expect a whole multi-step process to be executed on our behalf by a machine.
Following a few sensible steps can dramatically improve the quality of the output.
Set hard boundaries.
- Define in explicit terms what the process will and will not do. Is the workflow restricted to your own files, or can it go outside?
- If it is instructed to use outside sources, what are the boundaries?
- Explicitly instruct that there be no generation of conclusions before a human review of the sources and for/against arguments.
- Insert a series of ‘stop’ points beyond which the tool will not proceed until instructed to do so. Instruct the tool to act as a devil’s advocate at each stop/go point.
Transparent provenance.
AI tools extract information from the sources it finds or are directed to. Those sources define the potential of the output to deliver useful value of some sort to the user. Curating the sources the tool examines is therefore a fundamental step in the generation of that value.
Remain curious.
Just because a tool can repeat a workflow accurately every time does not mean that the workflow is perfect. It takes human curiosity and experimentation to test and retest a process that is designed to deliver an optimised outcome. The AI cannot do that optimising; it requires a curious human to be in the drivers seat asking that key ‘what if’ question. So, turn off the process from time to time, and go back to the old way, manual execution.
An exercise I did many times pre-AI to improve a record-keeping process was to imagine myself as a paperclip, attached to relevant documentation. I would follow the document through the process, documenting every point at which the document was delayed, added to, moved, and authorised, and the time lapse of each of those points. Map it out, and inevitably you will see improvement opportunities. AI cannot see those opportunities.
These steps will stop the tool trying to please you by being agreeable and synthesising conclusions.
May 8, 2026 | AI, Marketing
When you see something unlikely, from out on the fringes, don’t just dismiss it as some sort of anomaly. Dig deeper, look for the cause, every now and again that search will deliver a nugget of innovation
The header photograph of goats in trees is typical
There is a particular evolution of flexible hooves and an enhanced sense of balance that has enabled local goats to climb the Agania tree, native to southern Morocco. The motivation for the goats is the fruit of the tree, an important source of nutrition. The goats eat the fruit and spit the pip providing another one of nature’s wonderful collaborative relationships.
When you see something unexpected, unlikely, or just plain bonkers out on the fringes, it makes sense to have a close look to understand why. Sometimes there will be a nugget of truth hiding in the ‘bonkers’.
In this case there is the nugget of truth, but the photograph does not reflect that truth. An enterprising local goatherd ties young goats to the tree, and then hits up the tourists for a tip when they take photos.
Word of the unusual engaging, unlikely, and interesting stuff like a tree adorned with goats tends to spread. Often the unreality of it hides the nugget of truth.
Apr 23, 2026 | AI
The cognitive capacity of our brains can become lazy. Evolution has delivered an amazing ‘machine’ that seeks to organise and optimise the use of the cognitive resources available. Like any muscle, when unexercised, it shrinks.
I stumbled across AI a week after ChatGPT was launched and was fascinated by the potential to inform, improve, and be provocative in the construction of blog posts, and critical writing generally.
At that point, I had written about 2300 posts over 12 or so years.
Initially, AI did deliver some of the hoped for benefits. When asked, it pointed out things I had missed, glossed over, or required checking. The writing remained mine.
Rapidly, the tool (at that time still exclusively Chat) started to exert its algorithmic power, subtly altering my ‘voice’. Almost unnoticeable at first, but progressively intrusive.
I built a customer ‘voice’ GPT at about a year, constantly updated, which slowed but did not stop the encroaching impact of the probability engine I was trying to leverage and tame.
Net result: far fewer posts, as finding that perspective and point of view that differs sufficiently from the AI generated slop to make it both interesting, and worthy of attention is so much harder than it was pre-AI.
I worry that we are encouraging what Daniel Kahneman would call ‘Systems 1 thinking’ and increasingly ignoring systems 2, from which springs all that is new, different, and ultimately what makes our lives better.
‘Cognitive debt’ is a term used by psychologist Daniel Pink to describe the impact on brains that use AI tools as a substitute for thinking.
When we substitute AI for thinking, curiosity, scepticism, we are softening and removing the instinctive frameworks we use to manage and respond to what is around us. We are removing the opportunity to exercise choice, apply experience and wisdom, to challenge and improve.
Just using tools to do tasks trains our brains to be lazy, creating cognitive debt.
I am 74, so the impact will not disrupt me. Arguably if you asked my wife, cognitive debt has already overtaken me. However, I have children in their most productive years, and young grandchildren, and the impact on them does bother me, greatly.
As a community, we need to ‘lean into’ AI, as it is not going away. Little kids ‘get it’ so give them access to the tools from the time they are learning to express themselves. Teach them how to ‘think slow’ to leverage the capability of AI tools, rather than just accept the slop delivered by ‘AI fast’
