Jul 15, 2025 | AI, Strategy
We are so busy debating whether AI will take our jobs, we’ve missed a more dangerous question: what happens when it takes the jobs that create our leaders?
So far, the brunt of automation has fallen on blue-collar roles. Machines took over factory lines, robots handled dangerous or repetitive manual tasks. But the spotlight is shifting. White-collar work, particularly at the entry level, is squarely in the crosshairs of AI. Roles in sales, marketing, law, accounting, admin support, anything process-driven or rule-based are already being swallowed up by bots, templates, and AI agents that never sleep, strike, or slack off.
In past industrial revolutions, we saw enormous upheaval in labour markets. Steam displaced the weavers. Mass production killed off artisans. Electricity reduced manual labour but turbocharged the rise of middle management. Each wave destroyed jobs but also created new ones. That’s the comforting story we tell ourselves.
But this time, the tempo is different. AI is rolling through industries faster than we can repurpose workers. We may eventually find equilibrium, but it’s likely that the rate of job creation will lag the rate of job destruction. And this time, it’s not just jobs on the line, it’s the culture, resilience, and leadership pipelines of entire organizations.
Most of the white-collar roles under threat are entry-level. These are the proving grounds where future leaders learn the ropes, earn their scars, and get spotted by mentors. Strip away those jobs, and what are we left with? A dangerously thin layer of next-gen talent. No feeders. No bench strength. Just a void.
This matters. Organisations depend on a steady flow of energetic, irreverent, risk-taking young guns to shake things up. These outliers challenge orthodoxy, surface new ideas, and eventually rise to reshape the culture. Remove the ground floor, and over time, the whole building becomes brittle.
We don’t yet know the full consequences. But we do have some clues. History is littered with unintended consequences when change is forced onto complex systems.
Consider China’s one-child policy. Designed as a population control measure, it has led to a demographic cliff. Too few young workers. A rapidly aging population. Long-term consequences no one foresaw.
Or nature: rabbits and cane toads introduced to Australia for pest control. Wolves removed from Yellowstone to protect livestock. In each case, the ecosystem was disrupted. Only decades later did we see the cascading damage, and in the case of Yellowstone, the healing when wolves were reintroduced.
The same pattern may emerge in our workplaces. AI may be brilliant at cutting costs and boosting productivity. But if it wipes out the very roles where human potential is first tested and tempered, we could be sowing the seeds of a cultural and leadership vacuum that won’t show up in KPIs until it’s far too late to fix.
Jul 4, 2025 | AI, Leadership
Most marketers wouldn’t know John Boyd if he jumped out of a strategy deck and tackled them. However, his OODA loop brainchild leverages the power of AI to turbocharge tactical marketing effectiveness.
Boyd, a maverick US Air Force fighter pilot and strategist, understood that survival in combat came down to one thing: speed of decision-making. The OODA loop: Observe, Orient, Decide, Act, then rinse and repeat was his insight that gave him the nickname of ’40 second Boyd’ He was never beaten in flight simulator dogfight combat. He understood that whoever cycles through that loop faster reshapes the contest and forces the opponent into reactive mode. In air combat, this meant living. In business, it means winning.
OODA is a mindset. AI is changing the tempo of that mindset in ways even Boyd could not have imagined.
AI can Turbocharge tactical Tempo
Until recently, the bottleneck in decision-making wasn’t data, or insight, or even creativity. It was people. Our slow, deliberate committee meetings, our weekly WIPs, the reviews that drag on longer than a Sydney DA approval.
AI doesn’t suffer these constraints. It observes more, faster. It orients by processing billions of data points in real-time. It proposes decisions with options and probabilities baked in. And it acts immediately when allowed, not months.
What used to be a quarterly campaign development cycle can now happen in an afternoon. And that changes everything.
The limiting factor is the siloed org chart.
The challenge isn’t getting AI to do the work. It already can. The real challenge is getting organisations to leverage the power of speed AI can deliver.
Too many CMOs are caught in the headlights, stuck in outdated governance and fear of missteps. They’re playing the game like it’s 2012. Time as a constraint is rapidly being removed. AI can produce a full marketing program overnight. Then it is handed to the organisational approval processes, often as decisive as my Aunt Mimi.
Meanwhile, your competitor, the one who slashed the approval chain and taught their AI what “on-brand” means, has already launched, learned, and iterated.
Leadership Is the Bottleneck
The real AI revolution is not technical. It’s cultural, and it is leadership.
Speed has become the underrated competitive edge. Not speed for its own sake, but speed to consider, learn, adapt, execute, and then repeat the cycle. This means leaders must rethink their role. They are no longer approval gatekeepers; they act as tempo setters. The conductor of a real-time orchestra where instruments never sleep and tempo changes every hour.
Reclaim the OODA Loop
Every time a decision is delayed, it hands the advantage to the opposition.
In Boyd’s world, if you could stay inside your opponent’s OODA loop, responding to changes faster than they could comprehend, you won.
AI lets us do that not just to competitors, but to markets, media shifts, consumer moods, even cultural trends.
But only if we let it.
As AI becomes embedded in workflows, the question becomes: who trains the AI?
Who owns the “brand brain” that defines tone, style, and judgment?
Smart brands are reclaiming that brain. They are training AI on their own assets and experiences, not renting a brain from their agency. That brain learns, evolves, and becomes an unfair competitive advantage.
Marketing to succeed in this new world must become an adaptive system.
In a world moving at AI speed, Boyd’s old dictum is truer than ever:
Decide fast. Act faster. Or die slow.
If you are not already building your AI-accelerated OODA loop, your competitors are. By the time you notice, they’ll be on to the next loop, and you may be headed for oblivion.
Jun 25, 2025 | AI, Customers, Lean, Operations
The idea of the OODA loop is to get inside the decision cycle of your opposition. Once inside, you control the outcome in the absence of some externality.
Toyota used this idea to destroy Detroit.
The Andon cord placed the power of tactical decision making about quality right at the point where it was needed, with the workers on the production line.
By this means, quality problems were identified and fixed before they moved a further step towards the customer.
It also did something else.
By identifying and fixing problems at the source, the cycle of problem fixing was accelerated greatly. Not every problem can be fixed immediately at the line, but there are processes for escalation, from the front lines to the lowest level that is empowered to address the problem. That escalation involved suppliers when the problem was caused by a supplied part that was substandard.
By contrast, Detroit was driven from the top down, being run by spreadsheets (handwritten until the 90’s) by executives who may never have seen the inside of the factory.
A problem as it escalates up a chain of command has many opportunities to be buried, forgotten, miscommunicated, all of which will happen, driven by all sorts of human frailties and power games. The end result, the little problem in the factory compounds and becomes a big problem with customers, which costs a lot to address, and ruins reputations.
Toyota got well inside the time it took Detroit to respond to problems. While Detroit was escalating or hiding quality problems, Toyota was fixing them and moving on the next improvement.
They were inside the OODA loop of Detroit, and it destroyed the American car industry.
AI is now giving users an easy tool to get inside the decision cycle of their competition, while seeing the productivity benefits drop to their bottom line.
How are you going to deal with that?
Jun 2, 2025 | AI, Communication, Governance
Analysis implies the intelligent interrogation of data, the use of differing ‘frames’ through which to see the data, and to enable those non obvious connections to be made.
First you need ‘clean’ data, without which, nothing that follows will be worth much.
Thoughtful, critical analysis of data leads to insight, from which comes that elusive lightbulb moment.
Reporting is the opposite, it simply requires the cleaning, summarising and posting of the data without the critical thought from which real insight evolves.
No lightbulb.
AI is good at that, while not being good at generating insight.
Being data rich but insight poor is now a very common problem.
AI will not solve it for you, people are needed. Not just any people, seat warmers, but the right people with the curiosity and ‘why not’ attitude of youth, combined with the wisdom of experience and domain knowledge.
Unfortunately, these people do not grow on trees, ready for the picking. You have to grow and nurture them yourself, while recognising that many will move on at some point. The old adage of a rising tide lifts all boats is nowhere more relevant.
May 30, 2025 | AI, Marketing
The market research industry turns over big dollars by providing reassurance to marketers that when they are wrong, they have an acceptable excuse.
The recent election campaign and associated polls demonstrate comprehensively how broken market research can be. It should have been simple. Is Labor going to win, will the conservatives take the lollies, or will it be a split result? It was almost a binary choice, but no poll I saw was even close.
Given that failure, how in heaven’s name can we reasonably expect such a broken system to deliver reliable answers to challenging questions about the future behaviour of customers and potential customers in a competitive and volatile environment? Add into the mix the inability of most marketers to understand the competing forces in their market sufficiently well to ask good questions and therefore write quality research briefs. That delivers a perfect recipe for pissing money against the wall in pursuit of reassurance.
Over the years as a much younger marketer, I spent a lot of money on market research. It took a while, but I did come to realise the data was only a tiny proportion of the game. The real challenge was building the wisdom, insight, and market gutfeel to be able to ask those really good questions. Then, when a surprising response emerged, have the curiosity to further interrogate it from a positive and genuinely inquisitive perspective to get an answer to the eternal question: Why is it so?
In the last year or so, and accelerating at an astonishing rate, is the ability delivered by AI to gather and process market and behavioural information that can be used to ask those challenging; why is it so’ questions.
The process of market research has been totally up-ended. No longer do you need to spend tens or even hundreds of thousands of dollars over months to get shallow and often dated results. Now you can replicate the task quickly and cheaply with superior results using AI, and what is emerging as ‘synthetic research’.
Traditional research is good at telling you what has happened. It is good at counting. However, if you need to know what will happen in the future, and you use yesterdays tools, good luck!
May 26, 2025 | AI, Governance, Strategy
Innovation using physics is forging ahead at an accelerating rate.
Remember the speed at which a covid vaccine was brought to the market after the first identification of the virus. Instead of the usual 10 to 15 years we suddenly had that process compressed into 18 months.
And yet there remained those who refused to accept the vaccination for a range of personal and behavioural reasons which many would say are irrational.
Somewhere the line between the technical innovation involved in the hyper-rapid final stage development of the vaccine and the humanities driving behaviour crashed into each other.
As the rate of technical innovation across every domain accelerates it is likely we will continue to stumble across this barrier to adoption, and a fragmentation of adoption across a range of behavioural parameters.
Simply another social tension driven by the speed at which the modern world is evolving. It is way beyond the speed at which our DNA allows behaviour and attitudes to evolve.
The situation in front of us right now is the degree and manner in which AI is accepted and adopted by organisations and by individuals.
We managed this dilemma in the motor industry as it became obvious that it was profoundly important to incorporate safety into the vehicles as a means to save lives. As a result, it became mandatory to design crumple zones into cars, and install seat belts. Regulatory intervention and oversight 60 years after it became obvious that a car could kill its occupants.
Where will the equivalent crumple zone emerge in the arena of AI, and will it be in time?
