Jul 30, 2026 | Analytics, Management
Assembling and leveraging the complex mix of information and capabilities required to ‘win’ is a challenge similar to climbing a slippery ladder.
Most rely on data, spreadsheets, and extrapolations, blended in with optimism.
At the bottom of this ladder is data. Raw, unfiltered, and almost useless on its own. Just numbers, words, events. Noise without context. Like a kids jigsaw puzzle upended in a pile on the floor.
The ‘ladder’ has five steps.
Data. The pile of numbers, littered around files, outside sources of information, and peoples brains, without context relevant to the questions being posed.
Information. You start sorting. Cleaning. Labelling. Structure begins to emerge. It’s janitorial, unglamorous, tedious, and essential. This is where many people mistake motion for progress.
Knowledge. Connecting dots delivers knowledge. You see cause and effect, frequency, variation, and importantly the outliers. You can answer questions like “What’s happening?” and “How often?” Knowledge is a map, but it is still two-dimensional.
Analysis. This requires you test the knowledge, isolate variables, run comparisons, identify the outcome drivers, and develop ‘what if’ models. You don’t just have answers; you know how strong the answers are, and where they might fall apart. It gives you that added dimension.
Insight. Here’s where the real game begins. Insight is not just about seeing patterns, it’s about seeing meaning. It’s the inflection point, where a thousand observations converge into a single, clarifying idea.
But insight alone is not enough.
Running parallel to this clean, rational ladder is a tangled, intuitive, and very slippery pole: wisdom. Hard to see, impossible to teach, and absolutely critical to success.
Wisdom doesn’t follow steps. It grows from pattern recognition forged from lived experience. Mistakes made, ‘hunches’ that turned out to be right, and often spectacularly wrong, opportunities misread or missed completely. It takes time, perseverance, an ability to learn, curiosity, and determination.
It’s what lets a seasoned operator say, “That’s not going to work,” without needing a single chart, and be right. It’s instinct layered over time, a mental shortcut engine built on scars, sweat, and sleepless nights. It’s experience distilled into reflex.
The irony is that the higher you climb the corporate hierarchy, the more you need that slippery pole of wisdom.
If you only trust the data ladder, you might build the wrong solution brilliantly. If you only trust the pole, you risk hubris.
But when you climb with both, methodical analysis on one hand, and wisdom on the other, you are better able to understand problems, see the wider context, and spot points of leverage.
That’s the difference between a strategist and everyone else.
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.
Jul 20, 2026 | Analytics, Operations, Strategy
If a job that took eight hours now takes one, most managers will call that a productivity gain.
They are right up to a point.
Economists usually measure labour productivity as output per hour worked. If we produce the same output in one hour instead of eight, productivity has risen whether we use the remaining seven hours to win another customer, dissect the accounts, or head for the beach.
The beach does not cancel the efficiency gain, it simply exposes the limits of the measure.
The conventional measures tells us how efficiently we produced what it can already see. It tells us very little about what happened to the released capacity, whether the output improved, or whether technology enabled valuable work that nobody previously attempted.
It is a stopwatch with a blindfold.
This matters as businesses adopt AI. Most discussion starts with replacement: write the report faster, answer the email sooner, produce the first draft without paying someone to stare at a blank screen.
Useful, certainly but not transformative.
If AI cuts eight hours of work to one and the business fills the other seven with more of the same work, the gain will eventually appear in output, margin or capacity. If the business simply adds seven hours of meetings, the technology has delivered potential while management has squandered it.
That is not a measurement failure alone. It is a management failure with excuses.
The larger opportunity comes when AI extends what a business can do.
Most SMEs sit on piles of data they barely use. The accounting system contains patterns in customer profitability, purchasing, margin leakage and payment behaviour. The CRM contains clues about lead quality, objections, lost sales, response times and repeat purchases.
Managers have always suspected gold lies in those records, but lacked the time, skills or patience to dig it out.
AI changes the economics of the digging.
A business can now examine why apparently similar customers generate wildly different margins. It can compare the language used by prospects who bought with the language used by those who disappeared. It can identify which products create revenue but consume so much handling, discounting and after-sales attention that they destroy profit.
None of this necessarily replaces an existing task. Often, nobody did the task in the first place.
That makes “hours saved” a miserably small way to assess the value.
The better questions concern decisions improved, waste prevented, risks spotted and identified opportunities leveraged.
My commercial history includes a significant time driving sales and profitability of dairy products.
Take yoghurt for example. The supermarkets report a yoghurt market. That broad category helps retailers size the category shelf space, then allocate space to product groups, brands and individual products. It creates a ‘category map’ products and preparation of management reports. It conceals much of the behaviour that drives the sale to the time poor, budget limited, harassed by kids shopping consumer.
Shoppers choose between fruited and plain, low-fat and full-fat, pot-set and stirred, indulgence and health, adult and children’s formats, single serves and family tubs. Then they behave inconsistently, driven by the behaviours at home that result in consumption. Shoppers have never read the segmentation presentation, and do not care about it at all. The same persons trolley can contain Greek yoghurt for breakfast, a sweet pouch for a child, a set in pot natural yogurt for a fussy daughter, and a premium dessert for Saturday night, and be different on every visit to the supermarket.
Traditional analysis could reveal these patterns, given enough clean data, statistical skill and time. AI does not invent the possibility. It lowers the cost and effort required to pursue it, then allows a manager to question the data repeatedly rather than wait three weeks for another report.
They can uncover submarkets hidden inside the convenient fiction called “the market”. It can improve assortment, pricing, promotion and product development. The value comes not from producing the old market report faster, but from seeing choices the old report blurred out.
Then comes the hardest category: genuine innovation. Not the pack design change, or the ‘new improved’ standard product, genuine, category generating new products.
Every important general-purpose technology eventually enables products, services and business models that people could not sensibly describe at the beginning. We struggle to measure these gains because measurement follows observable activity. It cannot count a commercial possibility before somebody discovers it.
Nor do conventional accounts handle quality and unpriced value in anything but hypothetical terms. A better diagnosis, a more useful forecast, improved process, or a tailored customer answer may create considerable value without creating a measurable additional unit of output. We do not need to throw out existing productivity measures. They answer an important question and allow comparisons over time.
Businesses need a second dashboard.
Alongside output per hour, managers should track the use of released capacity, the valuable work newly performed, improvements in product quality and management choices, improved cycle and takt times, and new revenue or margin that technology made possible.
That is the productivity challenge in businesses.
AI will save time. This is an opportunity also available to competitors and especially new market entrants, unencumbered by past choices and technology. The sustainable advantage will go to the businesses that know what to do with the no added cost capacity that better use of time can deliver.
As an interim GM 20 years ago I was contracted to a manufacturing business that while marginally profitable, was struggling with capacity. Analysis revealed what everyone knew at some level, that a key step in the manufacturing process was acting as a bottleneck, strangling capacity. A number of relatively simple process changes that required no capital and no personnel changes enabled the key step to operate an extra 6 hours a day, effectively nearly doubling capacity. That resulted in shorter customer lead times, dramatic increases in DIFOT, abolition of outside warehouse costs, and very happy customers increasing orders at the expense of competitors. The changes took a couple of months to work their way through the financial system, but when it did, the impact on profitability was substantial. However, it was only with hindsight and deep digging into the numbers, that the changes in productivity numbers could be tracked.
The foregoing examines the productivity challenge of an individual business.
In the recent budget, and following discussions, the Treasurer has loudly and consistently pointed to the ‘productivity package’ contained in the budget papers.
The challenge in delivering on the promise, and having the electorate understand the complexity of the productivity challenge faced by the Australian economy is immense. It is easy to pick at the edges and observe ‘nothing to see here’ because the policy documents are shallow, predictably lacking in any real and measurable project timelines and checkpoints, and the press releases are as useless as the claims of a carnival hawker. It is however a start.
Jul 14, 2026 | Customers, Sales
The Swiss army knife is not a tool. It may be a handy all-purpose implement, attractive to boys everywhere, but it has too many options packed into a small space to be a serious tool.
Marketers know this, but continue to pile features onto their products, and wonder why their multi-functional innovation fails to excite anyone.
In 2007, some academics figured out why. Zhang, Fishbach, & Kruglanski published a paper with the usual long academic name that clearly demonstrated the truth of the cliche ‘less is more’.
Across a series of experiments, now repeated by others, they demonstrated that as the number of promised goals increased, people judged the activity as less effective at achieving each individual goal.
For example: People see weight training in the gym as a way to build muscle. If you add losing weight, improving general health, and meeting new people as benefits, the exercise suddenly appears less effective at building muscle. This is irrespective of the value of weight training in that primary objective of building muscle.
The exercise has not changed, but the number of claims has.
Each additional benefit weakens the mental link between the solution and the outcome occupying the buyer’s attention.
That is the dilution effect. Buyers do not add benefits together, each added claim dilutes the power of the original.
To the inexperienced marketer, it seems that if one benefit creates some value, several more must create greater value. However, do additional stated benefits do not add to the value, instead they divide their benefits between them.
A former client is a lawyer. When I met him he was sitting in his suburban office, alone, with very few clients. His website and marketing collateral highlighted advanced skills and experience in estate planning where there was considerable financial and familial complexity, gained in 25 years with a major law firm. His various communications also noted extensive experience in commercial and criminal law, and that his rates were set to attract the local businesses as clients, saving them money on matters usually sent to big law firms.
It became clear that he was seen by those he was trying to attract as a jack of all trades, but master of none. As a result he attracted people who needed representation in low risk areas like minor driving offenses.
Those who would have benefited from his estate planning expertise wanted a specialist, not a generalist which is how he was seen, despite the demonstrated expertise.
It is the same in every field. The claimed extra expertise dilutes the impact of a specialist in one field. An electrician who sets up as a building maintenance man might get jobs cleaning gutters, fixing a hole in the gyprock, or cleaning out the back yard, but you would not hire him to rewire the house. ,
The problem does not lie in having several capabilities, it lies in shouting about all of them at once. Our attention is divided, so we see only one thing at a time. This is best illustrated by the famous ‘gorilla’ experiment.
This all feeds into the obvious conclusion that to sell, start with the problem your product solves for the person with that problem.
How many times has a salesperson trying to engage with you has recited the ‘about us’ page from their website? Nobody really cares when they have a problem, that your company was started by your great uncle in 1949 after he returned from the war. Talking through what the company does, describing the product, and listing the features hoping they snag you somehow does not work.
It is backwards.
Instead, ask a few questions: What changed? Why does the problem matter now? What outcome matters most? What happens if you do nothing? What would a good result look like?
These questions reveal and magnify the value of the outcome being sought. .
Once you identify that key outcome, it can be connected to the most relevant solution to their specific problem you deliver.
When you have a range of features, make the ones that do not directly solve the problem secondary. They may be useful later, but in an initial transaction, they just cloud the vision of the potential customer, diminishing the impact your primary benefit will deliver.
It says almost nothing because it tries to include everything.
A strong message forces a choice, which once made, is a powerful driver of executing a transaction.
Generative AI should help us simplify communication, but usually it does the opposite.
These systems learn through human examples, feedback and reward signals designed to make their answers useful and satisfying. They set out to please the marketer rather than addressing the solution to a specific problem a specific customer might have.
Make your communication clear, simple, and directed towards action.
The phrase “Simplicity is the ultimate sophistication” is often attributed to both Leonardo da Vinci and Steve Jobs. Irrespective of who said it first, it remains a foundation for action.
Do not reduce the value you create, reduce the number of things competing for attention.
Jun 30, 2026 | Customers
The customer is not always right, but the customer should always be heard by you.
The trick now available is to be able to listen in on every interaction in a marketplace with your product, and judge what the response should be.
You learn a lot from customers, in particular the ones who leave, or are dissatisfied and complain.
Sometimes those complaints have nothing to do with your product, but everything to do with the complainer wanting an audience. Even then you must respond, as there will be a lesson in better understanding the customer context.
Not responding is in fact a response, and the response is “I do not care about you”. This is rarely a smart way to deal with even an annoyed customer about to bolt.
As a marketer I have always advocated the notion that the good stuff happens on the fringes. As the saying goes, ‘every good idea starts as a heresy’. Therefore, hearing the heresy is a core part of being able to respond to new stuff.
There are now a multitude of tools the hear what is being said by those who engage in any way, no matter how far out on the fringe they may be. Tools that track every interaction with your brands irrespective of the medium. Every one of those interactions should be responded to, in some way. The obvious caveat is that the interaction to which you are responding should be associated in some way with your brand, and sometimes one interaction is enough when the catalyst is negative.
The ‘PS’ to the headline is that the right customer is always right.
When a customer fits the ideal customer avatar like a glove, they will always be right. The challenge in these days of hyper-personalisation is to adequately define the ideal customer in such a way that you are confident about who they are, and what you want them to do next.
