Current revenue. Current customers. Current headcount. Current constraints. Current market conditions.

Then they ask a seemingly reasonable question:

What should we do next?

The problem is that starting from the present often produces incremental thinking.

If your planning process begins with today’s constraints, today’s resources and today’s assumptions, it becomes difficult to imagine a fundamentally different future. Strategic discussions drift toward optimisation rather than transformation.

The result is familiar.

A larger sales team.
A few new product features.
Some operational improvements.
A slightly better version of the business that already exists.

Useful, but rarely game-changing.

The most ambitious organisations take a different approach.

They start with the destination.
Then they work backwards.

The Principle Behind The Framework

Stephen Covey described this idea decades ago in The 7 Habits of Highly Effective People.

The concept sounds simple.

Yet organisations struggle to apply it consistently because they immediately jump from vision to execution. They discuss where they want to go and then rush into project plans, budgets and quarterly objectives.

What is often missing is the bridge between the future and the present.

Amazon built an entire management process around that bridge.

They call it Working Backwards.

Amazon’s Working Backwards Framework

Amazon’s approach became famous because it reverses the sequence most companies use.

Rather than starting with resources and asking what can be built, teams start with the customer outcome and ask what must be true to achieve it.

Before launching a product, teams write the future press release.
Before discussing implementation, they describe success.
Before debating priorities, they define the outcome customers will experience.

Only after everyone agrees on the destination do they begin planning the journey.

The process forces a different conversation.

Instead of asking:

“What should we build?”

Teams ask:

“If we were wildly successful, what would customers be saying?”

That shift sounds subtle.

In practice, it changes everything.

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Why Strategic Planning Fails

Many strategic planning exercises become exercises in resource allocation.

Which initiatives should we fund?
Which markets should we prioritise?
Which projects should we accelerate?

Those are important questions.

But they assume leadership has already agreed on what success looks like.

Often they haven’t.

The absence of a clearly defined future creates a hidden problem. Every executive carries a different mental model of where the organisation is heading.

The CEO imagines market leadership.
The CFO imagines margin expansion.
The Head of Product imagines innovation leadership.
The COO imagines operational excellence.

None of these goals are wrong.

But they are not necessarily the same.

As a result, organisations become highly productive while remaining partially misaligned.

People work hard.
Resources get allocated.
Projects get delivered.

Yet progress feels slower than it should because the organisation is pursuing multiple versions of success simultaneously.

Working Backwards solves this problem by forcing leaders to describe the destination in enough detail that everyone can see it.

The Future Press Release

The first step is deceptively simple.

Imagine it is 36 months from now.
Your company has exceeded expectations.
Customers love the product.
The business is thriving.
Competitors are paying attention.
Industry analysts are writing about your success.

Now write the press release announcing that outcome.

Not the strategy.
Not the roadmap.
The result.

This exercise forces leaders to articulate success in concrete language rather than vague aspirations.

One of the fastest ways to do this is with AI.

Future Press Release Prompt

The FAQ Test

Amazon doesn’t stop with the press release.

The next step is often more valuable.

Teams write the FAQ.

The reason is simple.

A future vision is easy to agree with.
The details are where strategy becomes real.

The FAQ forces leaders to confront difficult questions before committing resources.

What assumptions must be true?
What obstacles might derail us?
What would investors question?
What would customers challenge?
What capabilities are missing today?

Many strategic plans fail because organisations become emotionally attached to the vision without rigorously testing the assumptions underneath it.

The FAQ exposes those assumptions early.

Future FAQ Prompt

The Reverse Roadmap

Many organisations naturally think forwards.

Today leads to tomorrow.
Tomorrow leads to next quarter.
Next quarter leads to next year.

Working Backwards flips the process.

Once the destination is clear and the assumptions have been tested, leaders can begin mapping the path in reverse.

Starting from the future often reveals priorities that are invisible when planning from the present.

Capabilities that seemed optional become essential.
Projects that looked urgent become distractions.
Investments that felt risky become obvious.

The roadmap becomes less about activities and more about milestones that must exist for the future state to become possible.

Reverse Roadmap Prompt

The Real Value Of Working Backwards

Most leaders assume strategy is about deciding what to do.

The best leaders understand that strategy is first about deciding what success looks like.

Once that becomes clear, many decisions become easier.

Opportunities can be evaluated against the destination.
Investments can be prioritised.
Trade-offs become obvious.
Alignment improves.
Focus improves.
Execution improves.
The future becomes a filter for the present.

This is the deeper lesson behind Covey’s principle.

Beginning with the end in mind is not about vision boards or motivational slogans.

It is about creating enough clarity about the future that today’s decisions become easier to make.

Amazon simply turned that idea into a repeatable management system.

The Question Every Leadership Team Should Answer

If your company became extraordinarily successful over the next 36 months, what would the press release say?

If your leadership team cannot answer that question clearly, there is a good chance you are planning from the present instead of leading from the future.

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A transformation program falls behind schedule and leadership asks for a more detailed plan. A growth initiative misses expectations and another strategy review is commissioned. An AI rollout generates mixed results and governance expands. A new market opportunity emerges and the executive team spends months refining forecasts before making a move. In each case, intelligent people are trying to reduce risk. In each case, they may be making the situation worse.

The instinct is understandable. Executives are rewarded for being right. Boards reward confidence, investors reward predictability and organisations reward leaders who appear to have answers. Over time, this creates a powerful bias toward certainty. When uncertainty increases, leadership teams naturally respond by seeking more information, more analysis and more validation. Sometimes that is exactly the right response. The problem is that many of the most important challenges facing organisations today do not become clearer through analysis alone.

This distinction sits at the heart of a surprisingly common executive mistake. Leaders often assume every challenge can be solved using the same decision-making process. Gather the facts. Analyse the options. Build the business case. Execute the plan. That sequence works extremely well in some situations. It performs remarkably poorly in others. The issue is not the quality of the leaders or the quality of the analysis. The issue is that different categories of problems require fundamentally different approaches.

The certainty trap

Most executives spend their careers solving problems where expertise and analysis create a measurable advantage. A CFO evaluating an acquisition can improve the quality of a decision through deeper financial analysis. A COO redesigning a supply chain can model scenarios and optimise trade-offs. A CIO selecting a technology platform can compare vendors, assess risks and make a rational recommendation. These are difficult decisions, but they share a common characteristic. The answer exists. The organisation may not know it yet, but the answer can be discovered through expertise, investigation and analysis.

Success in these environments creates a powerful habit. Leaders become accustomed to the idea that uncertainty is simply a temporary lack of information. If the business gathers enough data, speaks to enough experts and performs enough analysis, the right answer will eventually emerge. The habit is reinforced because it works so often. The challenge is that many of today’s most important executive questions do not behave this way.

Culture change does not behave this way. Product innovation does not behave this way. Building a new market category does not behave this way. Responding to disruptive technology does not behave this way. In these situations, the future is not waiting to be discovered. It is being created through the actions of customers, competitors, regulators and the organisation itself. The answer does not exist yet. It emerges over time.

This is where organisations become trapped. Faced with uncertainty, they reach for the tools that have worked in the past. More planning. More governance. More analysis. More certainty. The result is that leadership teams become increasingly sophisticated at discussing the future while becoming progressively slower at influencing it. They mistake activity for learning and preparation for progress.

The framework that explains the mistake

Dave Snowden developed the Cynefin Framework after observing that organisations repeatedly struggled not because they lacked intelligence, but because they misclassified the problems they faced. Leaders would apply a management approach that was successful in one environment and assume it would work equally well in another. The problem was not execution. The problem was diagnosis.

The framework itself is deceptively simple. Some challenges are clear. The answer is known, best practices exist and consistency creates value. Some challenges are complicated. The answer exists but requires expertise to discover. Some challenges are complex. The answer emerges through experimentation and learning. Some challenges are chaotic. The immediate priority is stabilisation before deeper understanding becomes possible.

The power of the framework is not in the categories themselves. The power comes from recognising that different categories require different forms of leadership. A complicated problem benefits from analysis. A complex problem benefits from experimentation. A chaotic problem benefits from decisive action. The mistake most organisations make is treating all three as though they are merely complicated.

When that happens, transformation programs become trapped in governance structures designed for operational projects. Innovation initiatives become trapped in planning cycles designed for capital investments. Crises become trapped in committee discussions. The organisation applies the wrong operating system because it has misunderstood the nature of the challenge.

The executive test

Many strategy discussions begin with a familiar question: What should we do? The problem is that this question often arrives too early. Before discussing solutions, leadership teams should first ask what kind of problem they are solving. Is the answer already known? Can it be discovered through expertise? Must it emerge through experimentation? Or is the situation so unstable that immediate action matters more than analysis?

Very few organisations ask these questions explicitly. As a result, they spend months debating solutions to problems they have not correctly diagnosed. They argue about forecasts before understanding whether forecasting is even possible. They debate implementation plans before determining whether the answer needs to be discovered through experimentation. They optimise the wrong decision process because they never classified the challenge in the first place.

Use this prompt to pressure-test your assumptions before committing significant time, capital or attention.

Prompt: Problem Classification Audit

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The Executive Decision Standard

The practical application of Cynefin is to stop treating every decision as though it belongs in the same category. Most organisations have a single decision-making culture. The same governance process is applied to innovation programs and compliance initiatives. The same reporting cadence is applied to exploratory experiments and operational projects. The same demand for certainty appears regardless of whether certainty is actually available. This creates organisational drag because the management system no longer matches reality.

A useful leadership discipline is to distinguish between discoverable problems and learnable problems. Discoverable problems contain answers that can be uncovered through expertise, analysis and investigation. Learnable problems contain answers that only emerge through experimentation and feedback. Many organisations instinctively treat both categories the same way. They ask for more evidence when they should be generating evidence. They ask for better forecasts when they should be testing assumptions. They ask for confidence when they should be seeking learning.

This distinction matters because complex environments reward a completely different capability. Success does not come from having the most accurate prediction. Success comes from reducing uncertainty faster than competitors. The organisation that learns faster gains an advantage over the organisation that plans longer. That is why some companies appear to move confidently into new markets while others spend years analysing the opportunity. The difference is often not intelligence. It is learning velocity.

The learning organisation

Many executives describe their companies as learning organisations, but few define what that means operationally. In practice, a learning organisation is one that can systematically identify assumptions, test them quickly and adapt based on evidence. Its objective is not to avoid mistakes. Its objective is to discover reality faster than competitors. That capability becomes especially valuable in complex environments where analysis alone cannot reveal the answer.

The challenge for most leadership teams is that assumptions often remain hidden. They sit inside business cases, strategy documents and executive presentations without being explicitly identified. As a result, debates occur around conclusions rather than the assumptions that produced them. Progress accelerates when leadership shifts its focus from defending plans to testing assumptions.

Use this prompt to build a learning process around a complex challenge.

Prompt: Experiment Design Workshop

The board conversation

One of the most useful applications of Cynefin is at board level. Boards are naturally skilled at challenging analysis. Directors ask whether assumptions are realistic, whether financial projections are credible and whether risks have been appropriately considered. Those questions are essential when management is dealing with a complicated problem. They become less useful when management is dealing with a complex one.

When the challenge is complex, the board should spend less time asking whether management has the answer and more time asking whether management is learning quickly enough to discover it. That subtle shift changes the entire conversation. Instead of debating forecasts, the discussion focuses on experiments. Instead of seeking certainty, the board seeks evidence. Instead of asking whether management is correct, directors ask whether management is learning.

Use this prompt before your next board discussion involving strategic uncertainty.

Prompt: Board Challenge Simulator

The executive takeaway

Most leadership teams believe their biggest challenge is making better decisions. More often, the challenge is understanding what kind of decision they are making. A board that demands certainty from a complex problem will slow the organisation down. An executive team that treats a crisis like a strategy workshop will make things worse. A leadership group that mistakes experimentation for analysis will struggle to learn.

The first responsibility of leadership is not choosing the answer. It is correctly diagnosing the nature of the problem. Because once that becomes clear, the right management approach usually becomes obvious. And until it does, even smart organisations can spend years asking the wrong question.

AI is not just software, it is power

It decides what people see, what gets approved, who gets hired, who gets served, who gets watched, who gets replaced, and who gets ignored.

Pope Leo XIV’s core warning is crisp:

That should make every CEO uncomfortable.

Because most companies are not building AI around values.

They are building it around speed, cost, scale, and margin.

Those are fine goals. But they are not enough.

If AI is trained to cut cost, it may cut corners.
If it is trained to maximise engagement, it may feed addiction.
If it is trained to optimise labour, it may treat people as waste.
If it is trained to personalise everything, it may quietly manipulate everyone.

That is the Pope’s point.

Not “AI bad.”

More like: AI makes your hidden operating system visible.

If your culture is extractive, AI can extract faster.
If your culture is sloppy, AI can scale the slop.
If your culture is paranoid, AI can become surveillance.
If your culture is humane, AI can help.

The warning is simple: do not build Babel.

Babel is the biblical story of people building a tower to prove their power. Big, clever, impressive, and doomed.

It lost the plot because the tower became the mission: power, pride, and control replaced purpose.

That is the risk with bad AI strategy.

The dashboards improve. The system gets faster. The automation spreads. But no one can explain who owns the outcome, where the judgment sits, or why customers and workers should trust it.

That is not transformation.
That is Babel in business dress.

CEOs love scale. Fair enough.

But AI does not just help you scale.
It helps you scale what you already are.

The Pope is asking: scale of what?

Scale help, or scale harm?
Scale judgment, or scale noise?
Scale trust, or scale control?
Scale human ability, or scale human replacement?

That is the whole game.

Here is my take of the papal letter.

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AI needs owners

Not committees. Owners.

If an AI system denies a claim, rejects a candidate, prices a customer, flags a worker, or gives medical advice, someone senior must own the outcome.

“The model did it” is not a defence.

It is an admission you lost control.

I made the same point in Who Is Accountable When AI Decides?: AI can recommend, rank, draft, and detect. But it cannot be accountable. That remains a human job.

People need an appeal path

If your AI touches a person’s money, job, health, safety, status, or rights, they need a way to challenge it.

Fast. In plain English. With a human who can fix it.

No appeal path means no trust.

And no trust means the business model eventually breaks.

That is why Trust Is The New AI Battleground. As AI output becomes cheap and everywhere, trust becomes scarce.

Scarce things become valuable.

Do not automate your conscience

Some decisions are too human to hand off.

Hiring. Firing. Policing. Care. Credit. Education. War.

The more serious the consequence, the more human judgment must stay in the loop.

Not as theatre. As authority.

This is the difference between delegation and abdication. In AI Moves Work, Not Magic, I argued that AI does not remove the need for management. It moves the work. It changes where judgment is needed.

Bad leaders use AI to avoid decisions.
Good leaders use AI to make better ones.

Your workers are watching

If AI is just a headcount weapon, expect fear, resistance, leaks, and brand damage.

If AI removes dull work, grows capability, and shares the upside, people may come with you.

“Reskilling” is not a slide. It is a deal.

Who gets trained?
Who gets moved?
Who gets protected?
Who shares in the gains?
Who is told early enough to plan?

That is the actual work.

The better example is not “replace people with bots.” It is redesigning work around new capability. That is why CBA Redesigns Work for AI matters.

The dumb version of AI strategy is cost-out.
The serious version is capability-up.

Truth matters again

AI makes cheap content infinite.

That means trust becomes a moat.

Do not pump out machine-made noise and call it marketing.
Do not fake intimacy.
Do not hide synthetic work.
Do not make customers wonder whether anyone real is home.

Every company will be tempted to use AI to say more, publish more, target more, personalise more, and automate more touchpoints.

Most of it will be junk.
Some of it will be harmful.
A little of it will be useful.

The CEO job is to know the difference.

Institutions still matter

The Pope is not really worried that machines will become human.

He is worried that humans will become machine-like.

Measured. Ranked. Predicted. Optimised. Managed by systems nobody can explain.

That is the deeper warning.

AI can simulate output. It can simulate tone. It can simulate care. It can simulate authority.

But it cannot create trust by itself.

Trust comes from institutions that keep promises, own errors, tell the truth, and protect people when things go wrong.

That is why Synthetic Scale Fails Without Real Institutions is the business version of the same argument.

Scale without trust is fragility
Scale without accountability is risk.
Scale without humanity is just a taller tower.

The Pope says the task is to put “the human person at the center of our choices.”

That is the CEO takeaway.

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Use AI.
Move fast.
Compete.

Do not ship systems nobody owns.
Do not hide behind the model.
Do not reduce people to behavioural data.
Do not turn workers into cleanup crews for bad automation.
Do not scale what you would be ashamed to explain.

The Pope’s message is not anti-tech.
It is anti-stupidity.

Build AI that helps people do more, know more, make better calls, and live with more dignity.

Anything else is just Babel with a better user interface.

The AI industry remains obsessed with model performance.

Every major release is evaluated through the same lens. Which model is more intelligent? Which model performs better on reasoning benchmarks? Which model generates better code? Which model achieves the highest score on the latest evaluation suite?

These are useful questions for researchers, developers, and technology teams. They are increasingly less useful for CEOs.

The executive challenge is shifting. The question is no longer simply which model performs best today. The more important question is which AI provider an organisation is prepared to become dependent upon over the next decade.

That distinction may sound semantic. It is not.

Software decisions and dependency decisions are fundamentally different. Software can be replaced. Dependencies become embedded within operating models, governance structures, workflows, and organisational capabilities. Once that occurs, changing providers becomes materially more difficult, regardless of whether a better alternative exists.

Recent developments in the AI market suggest this transition may already be underway.

Enterprise Adoption Is Following A Different Logic

One of the more interesting developments over the past year has been the emergence of different adoption patterns between consumer and enterprise markets.

OpenAI remains the dominant consumer brand. ChatGPT has become synonymous with AI in much the same way that Google became synonymous with search. Consumer awareness, mindshare, and usage remain significant competitive advantages.

Yet enterprise markets have historically followed different rules.

Recent data from Ramp’s AI Index showed Anthropic overtaking OpenAI in business adoption for the first time, with Anthropic reaching 34.4% adoption compared with OpenAI’s 32.3%. Ramp tracks actual spending behaviour across tens of thousands of businesses, making it one of the more useful signals available in the market.

This does not prove Anthropic has built a superior product. Nor does it suggest OpenAI’s position is under immediate threat.

What it does suggest is that enterprise buyers may be optimising for a different set of criteria than consumers.

This would not be unusual. Enterprise technology markets have rarely been won solely through technical superiority. Reliability, governance, integration, vendor maturity, support, security, and long-term viability often become equally important. In many categories, they become more important.

The history of enterprise software is filled with examples where the technically strongest product failed to become the dominant platform.

AI may be entering a similar phase.

Revenue Growth Tells A Different Story

Another useful signal is where revenue growth is emerging.

Anthropic’s reported growth from approximately $1 billion in annualised revenue during early 2025 to more than $30 billion by April 2026 is remarkable. More interesting, however, is the nature of the demand driving that growth.

Much of the value appears to be coming from enterprise use cases. Coding environments, research workflows, legal operations, knowledge management, and internal productivity systems are becoming increasingly important sources of adoption.

These are not casual consumer interactions. They are operational workflows.

That distinction matters because operational workflows create stickiness. Once an organisation redesigns a process around a technology platform, the economics change. The conversation shifts away from feature comparison and toward continuity, governance, and risk management.

Those are the characteristics of infrastructure markets rather than software markets.

The Industry Is Already Behaving Like Infrastructure

The strongest evidence may not come from adoption metrics at all. It may come from how providers themselves are behaving.

OpenAI’s introduction of multi-year Guaranteed Capacity agreements is particularly revealing. The offering allows organisations to reserve long-term AI compute capacity across models and cloud providers, in some cases years in advance.

Capacity reservation is not a typical software construct. It is an infrastructure construct.

Cloud providers sell capacity. Telecommunications providers sell capacity. Utilities sell capacity. Organisations reserve capacity when they expect a service to become operationally critical.

At the same time, AI providers are investing extraordinary amounts into long-term infrastructure commitments. Anthropic’s reported commitment of up to $200 billion with Google is difficult to interpret as anything other than a belief that AI is becoming foundational infrastructure.

The market is gradually moving below the application layer. Models remain important, but increasingly the competitive battle is shifting toward ecosystem control, workflow integration, enterprise governance, and infrastructure scale.

That is typically where long-term winners emerge.

Strategic Implication

The strategic implication is not that organisations should choose Anthropic over OpenAI.

That conclusion would be simplistic and likely wrong.

The more important implication is that executives may be evaluating AI investments through the wrong decision framework.

Most organisations continue to approach AI as a technology procurement exercise. They compare features, benchmark results, and subscription costs. Those considerations matter, but they are not where the largest strategic risks reside.

The bigger question is how AI dependencies are forming across the enterprise.

A Three-Layer Framework For AI Strategy

I increasingly think AI decisions need to be separated into three distinct layers.

The mistake many organisations make is treating all three layers as though they are the same decision.

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Layer One: Models

This is where most executive discussions currently focus. Organisations debate GPT versus Claude, open source versus proprietary models, and performance differences across various tasks.

These decisions are important, but they are also becoming increasingly reversible. Model quality continues to improve across the market, and the gap between leading providers is narrowing.

Most organisations are allocating excessive attention to the layer where switching costs remain lowest.

Layer Two: Platforms

The second layer is significantly more strategic.

Platforms determine how AI interacts with enterprise systems, data assets, workflows, employees, and governance structures. This includes identity management, orchestration layers, agent frameworks, security controls, and integration architecture.

Unlike models, platforms become embedded.

Changing a model may take weeks. Replacing an enterprise AI platform may take years.

This is where dependency risk begins to emerge.

Layer Three: Operating Models

The third layer receives the least attention and may ultimately prove the most important.

AI is not simply changing how work is executed. It is changing how organisations are designed.

Management structures, workforce composition, decision-making processes, customer engagement models, and product development cycles are all beginning to evolve. These are not technology decisions. They are business model decisions.

Boards and executive teams should spend far more time discussing this layer than they currently do.

Three Questions Every CEO Should Ask

The next phase of AI strategy requires a different set of questions.

First, where are we becoming dependent on AI?

Leaders should map the workflows, processes, and decisions that increasingly rely on AI systems. Dependencies create both strategic advantage and strategic risk. Most organisations currently understand the former better than the latter.

Second, which dependencies are reversible?

Not every investment creates lock-in. Understanding which decisions preserve optionality and which create long-term dependence is becoming increasingly important. Switching costs are often invisible until they become operationally painful.

Third, what capabilities must we own?

Every organisation has a small number of capabilities that underpin competitive advantage. The objective is not to own everything. The objective is to ensure that strategically important knowledge, workflows, and decision-making capabilities do not become outsourced by default.

This is particularly important as AI agents become more deeply embedded within enterprise processes. The convenience of external intelligence can easily obscure the strategic value of retaining internal capability.

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The Emerging Divide

Over the next five years, organisations are likely to separate into two groups.

One group will continue treating AI as another software category. Their focus will remain on features, pricing, and vendor selection. They will optimise for procurement efficiency and internal productivity.

The other group will recognise that AI is becoming a foundational dependency within the enterprise. Their focus will shift toward strategic control, organisational capability, and long-term optionality. They will treat AI decisions in much the same way previous generations treated cloud strategy, ERP standardisation, or cybersecurity architecture.

However, the most sophisticated organisations will take the thinking one step further.

They will not only ask where they are becoming dependent on AI. They will ask how AI can make them more valuable, more embedded, and more difficult to replace within their own ecosystems.

History suggests the greatest value rarely accrues to those who simply consume infrastructure. It accrues to those who build on top of it.

The organisations that recognise this shift early will not necessarily choose better models.

They are more likely to make better dependency decisions and use AI to become more difficult to displace.

When growth slows, leadership teams tend to reach for the same playbook. They launch new features, cut prices, increase marketing spend, or copy whatever appears to be working for competitors. The logic seems sensible. If a competitor is winning customers, the answer must be to become better than them.

The problem is that every competitor is thinking exactly the same way.

Soon everyone is competing on the same dimensions: price, features, speed, convenience, service. Margins compress. Products become harder to distinguish. Strategic planning turns into a debate about how to win a race someone else designed.

Peter Thiel captured this reality in a single line:

Competition is for losers.

His point was not that competition disappears. His point was that the best businesses stop competing on the industry’s existing terms. They create new ones.

The Trap Of Incremental Improvement

Most strategy work is really optimisation.

Leadership teams spend their time discussing how to improve conversion by 5%, reduce churn by 3%, launch another feature, or move slightly faster than competitors. These are useful questions, but they are not strategic questions.

Strategy starts somewhere else. It asks which assumptions the entire industry is making that no longer need to be true. That is where new market space emerges. Not from being slightly better, but from being fundamentally different.

This is the core idea behind Blue Ocean Strategy, developed by Chan Kim and Renée Mauborgne. Instead of fighting competitors in crowded markets, they argued that organisations should redesign the factors customers value.

The goal is not to win the existing game.

The goal is to create a new one.

The Most Useful Strategy Tool Nobody Uses

Blue Ocean Strategy produced dozens of frameworks. One remains particularly useful because it forces leaders to confront the hardest part of strategy: trade-offs.

The framework is called the ERRC Grid.

Eliminate.
Reduce.
Raise.
Create.

Most leadership teams spend almost all their time discussing the last two. What can we raise? What can we create? How can we add more value? How can we add more capability?

Almost nobody wants to discuss Eliminate.

That is usually where the breakthrough sits.

Every industry accumulates baggage over time. Features nobody uses. Processes nobody questions. Costs everyone assumes are unavoidable. The longer an industry exists, the more these assumptions become invisible.

The ERRC Grid forces leaders to challenge them directly.

The Courage To Be Bad At Something

Southwest Airlines remains the classic example.

The airline eliminated meals, assigned seating, and premium service. Traditional airlines viewed these features as mandatory. Southwest viewed them as costs.

By removing them, Southwest created faster aircraft turnarounds, lower operating costs, simpler operations, and a fundamentally different value proposition. It became intentionally worse at certain things in order to become dramatically better at others.

Most organisations struggle with this idea.

Executives often want the new feature and the old feature. The new service and the old service. The new workflow and the old workflow. Over time, complexity accumulates. Costs rise. Operations become harder. Customers become confused.

The result is a Frankenstein product.

Every meaningful competitive advantage requires sacrifice somewhere else.

That is why every strategy discussion should include one uncomfortable question:

What are we willing to be terrible at?

If you are trying to be excellent at everything, you are probably not creating a Blue Ocean. You are simply adding complexity.

Use this prompt to challenge the assumptions in your industry.

Prompt: Blue Ocean ERRC Grid

The goal is not to improve the existing market.

The goal is to redesign it.

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The Executive Test

Before approving any new feature, service, initiative, or investment, ask one question:

What are we eliminating?

If the answer is nothing, there is a good chance complexity is growing faster than value.

The strongest strategies are rarely additive. They are selective. They force leaders to decide what not to do, what not to build, and which customers not to serve. That discipline is often what creates the advantage.

The most successful companies often look strange when they first emerge because they are not trying to win the existing game.

They are building a different one.

Below are two governance tools that force the hardest part of Blue Ocean Strategy: making deliberate trade-offs. Most leadership teams understand the framework. Far fewer have the discipline to apply it.

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The Trap: Gold Plating

Most executives are afraid to eliminate anything.

Every strategic initiative arrives as an addition. New products, new channels, new reports, new features, new processes, new approvals. Very little ever leaves.

Over time, complexity compounds. Costs rise. Decision-making slows. Customer experiences become cluttered.

Blue Ocean Strategy only works when leaders are willing to remove things, not just add them.

The following prompts are designed to force that discipline.

Follow-Up Prompt 1: The Anti-Feature List

Use this to identify and remove complexity.

The objective is to separate genuine customer value from inherited complexity.

Follow-Up Prompt 2: The Non-Customer Tier

Use this to identify growth that competitors cannot see.

Most growth strategies focus on stealing customers from competitors.

Blue Ocean strategies focus on people who are not customers yet.

That is often where the biggest opportunities sit.

The Executive Takeaway

Competition feels productive because it is measurable.

You can track market share. You can benchmark features. You can compare pricing. You can watch competitors. Every board meeting can be filled with competitor analysis and market updates.

Creating a new market space is much harder because there is no scoreboard.

Which is exactly why it works.

Most organisations spend their energy fighting over existing demand. The best organisations redesign the market so they no longer have to.

That is the real lesson behind Blue Ocean Strategy.

Stop asking how to beat your competitors.

Start asking how to make them irrelevant.

The Workforce Is Changing Faster Than The Workplace

Some people now instinctively open ChatGPT before Google.

Others still use AI occasionally, mostly for experimentation, then return to their normal workflows.

That gap matters more than it appears.

One employee starts every draft with AI. Another still begins with a blank page. One asks AI to summarise reports, pressure-test ideas, structure meetings, and accelerate research. Another still treats AI as an occasional assistant layered onto an unchanged workflow.

At first glance, the difference feels small. Over time, it may become enormous.

Because the real AI divide may not ultimately be technical. It may simply separate people who rebuild their habits around AI from those who continue operating largely as they always have.

The data increasingly points in this direction.

Stanford’s 2025 AI Index found that 78% of organisations now report using AI in some form. Yet McKinsey found only 1% of executives describe their companies as “mature” in AI adoption, meaning AI is fully integrated into workflows and generating meaningful business outcomes.

That gap matters.

It suggests most organisations now have access to AI tools, but relatively few have deeply changed how work actually happens.

Gallup’s workplace research tells a similar story. Around 40% of employees report using AI at work at least occasionally, but only a small percentage use it daily. In remote-capable knowledge work, habitual use rises sharply, particularly among leaders and highly digital teams.

The implication is becoming difficult to ignore.

Access to AI is spreading rapidly. Behavioural integration is not.

Technology Changes Faster Than Habits

Organisations consistently underestimate how difficult habits are to change.

Corporate history is full of technologies that promised behavioural transformation but were mostly absorbed into existing routines.

Email did not eliminate meetings. In many organisations, it increased them.

Slack did not eliminate email. It simply created another communication layer.

Dashboards did not eliminate PowerPoint. Most companies still converted dashboards into presentations for executive meetings.

Generative AI may follow the same pattern.

Many organisations currently describe themselves as “adopting AI” when what they are really doing is layering AI tools onto fundamentally unchanged workflows, management structures, approval systems, and communication habits.

The software changes immediately. Behaviour changes slowly.

The interesting part is not that AI tools are spreading quickly.

It is that repeated AI usage appears to be changing behaviour itself.

That shift may become one of the most important competitive dynamics of the next decade, particularly for organisations still treating AI as a software rollout rather than a behavioural transition.

The rest of this article explores where the real divide may emerge, why some companies are already pulling ahead, and what leaders may still be underestimating.

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The Productivity Boom Nobody Is Questioning

A junior finance analyst can now generate a polished board-level variance summary in under five minutes using AI. Five years ago, that same analyst may have spent half a day tracing transactions, checking assumptions, investigating anomalies, and understanding what actually drove the numbers. The new process is dramatically faster. It may also produce weaker financial judgment over time.

That tension sits underneath much of the current excitement around AI inside finance functions. Most executive conversations understandably focus on productivity gains. Faster close cycles, leaner reporting teams, automated reconciliations, AI-generated commentary, and improved forecasting all present compelling operational and economic advantages. If AI can safely compress hours of analytical work into minutes, every finance organisation will eventually face pressure to deploy it.

The more difficult question is what happens once large portions of the underlying work disappear.

Finance Functions Run On Judgment

Finance functions do not ultimately operate on software. They operate on judgment. Experienced finance leaders develop an instinct for anomalies, inconsistencies, fragile assumptions, and operational risks that rarely appears explicitly inside systems or dashboards. Much of that judgment forms through repetition and exposure.

Analysts learn by tracing discrepancies across systems. Accountants learn by manually resolving broken reconciliations. Teams build commercial intuition by repeatedly working through situations where the numbers do not initially make sense.

Historically, much of this work looked inefficient. In reality, it was also training.

That distinction matters because AI is arriving at the exact layer where many organisations unknowingly developed future financial leaders.

Finance Has Seen Versions Of This Before

Most finance teams have already experienced smaller versions of this dynamic through spreadsheet dependency.

A model becomes operationally critical over time until only one or two people fully understand the underlying logic. Everyone else learns the workflow but not necessarily the assumptions, calculations, or structural weaknesses embedded underneath it. The spreadsheet continues producing outputs while organisational understanding gradually narrows.

The problem only becomes visible when something changes. A key employee leaves. A formula breaks. A reporting anomaly appears. A business assumption shifts. At that point, the organisation discovers it can still operate the process, but fewer people can confidently explain how the model actually works.

That is organisational memory loss in practice. Not dramatic collapse, but a gradual separation between producing outputs and understanding how those outputs are produced.

The process survives. The understanding becomes concentrated.

AI Changes The Development Path

Generative AI may scale this dynamic significantly because it reaches much further upstream than previous forms of enterprise automation.

Earlier generations of finance technology primarily automated workflows, processing, and administration. AI increasingly automates interpretation, synthesis, explanation, and analysis. Those activities have historically been where junior finance professionals developed judgment.

This creates a potentially uncomfortable paradox.

The first generation of AI-enabled finance teams may become highly productive before they become deeply experienced.

At first, the indicators look entirely positive. Reports improve. Commentary becomes more polished. Teams move faster. Junior staff contribute earlier. Executives receive cleaner summaries and faster responses.

Operationally, the transformation appears successful because, in many respects, it is successful.

The challenge is that highly polished outputs can obscure weakening capability underneath.

The first generation of AI-enabled finance teams may become highly productive before they become deeply experienced.

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The first wave of AI adoption has been dominated by productivity gains. The second wave will be defined by organisational consequences that most leadership teams still underestimate.

That is the focus of For Every Scale.

I write for CEOs, CFOs, COOs, boards, and executive teams navigating how generative AI is changing operational capability, management structures, and decision-making inside large organisations.

If this article resonated with you, subscribe for analysis on the strategic and organisational implications of AI beyond the hype cycle.

The Friction Was Doing More Than People Realised

A surprising amount of financial judgment historically came from doing slow, difficult work manually before automating it.

Analysts built instinct by repeatedly encountering inconsistencies, exceptions, and unexplained variances. Over time, they learned not just how to produce numbers, but how to interrogate them.

Generative AI may remove portions of that developmental layer. Not intentionally, but structurally.

There are already signs of this pattern emerging inside highly automated accounting environments. Researchers studying automated accounting workflows documented situations where staff struggled to manually reconstruct processes once automation systems were removed.

The systems operated effectively while the automation layer functioned normally. The vulnerability only became visible once people needed to work without it.

The problem with removing all friction from knowledge work is that friction is often where understanding comes from.

The Leadership Question Is Changing

For CFOs and COOs, this changes the leadership question entirely.

The challenge is no longer simply determining how much work AI can automate. The harder question is how the organisation continues developing financial judgment once many of the hard parts disappear.

That is not purely a technology problem. It is an organisational design problem.

Most executive dashboards are designed to measure productivity, throughput, cost reduction, reporting speed, and close-cycle compression. Those metrics matter and should matter. However, they do not necessarily indicate whether the organisation is still producing future experts.

They do not measure whether junior finance professionals are developing the judgment required to operate effectively under ambiguity, stress, or abnormal conditions.

Boards do not simply want accurate reporting. They want confidence the organisation understands why the numbers are accurate.

A Framework For Leadership Teams

The practical test for leadership teams is not whether AI saves time. It is whether the organisation has a plan to replace the learning that the saved time used to create.

A useful starting framework is what I think of as the Capability Preservation Test.

1. What work are we automating?

Not the process name. The actual judgment being removed from human practice.

A reconciliation process may also be teaching anomaly detection. A forecasting workflow may also be building commercial intuition. A reporting task may also be developing pattern recognition.

2. What capability did that work used to build?

Many repetitive finance activities were unintentionally serving as apprenticeship systems.

Before removing the work entirely, leadership teams should identify what forms of judgment were historically being developed through repetition and exposure.

3. Where will that capability now be developed?

This is where many organisations currently have a blind spot.

If AI removes large portions of manual analytical work, where does the next generation of financial instinct come from? What replaces the developmental pathway that previously existed?

4. How will we know people still have the capability?

Most organisations test process compliance. Far fewer test operational judgment.

That may need to change.

Manual reconstruction exercises, anomaly reviews, edge-case simulations, and model challenge sessions may become increasingly important in highly automated environments.

5. Who owns the capability, not just the system?

System ownership is not the same thing as capability ownership.

Someone inside the organisation needs to be accountable for ensuring critical financial judgment continues developing over time, even as automation increases.

The Risk That Arrives Slowly

The real long-term risk may not be hallucinations or bad outputs. It may be a gradual erosion in the organisation’s ability to independently understand its own operations.

Not because the systems stop working, but because fewer people are repeatedly exposed to the friction through which deep financial judgment historically formed.

The irony is that organisations moving fastest with AI may not notice this dynamic until years later because the early indicators look exactly like success. Productivity improves. Reporting accelerates. Teams become leaner. Outputs become more sophisticated.

Until the organisation encounters something the system was never designed for and discovers too few people still know how to think through the problem from first principles.

This is first-order problem solving: treating the visible failure instead of the system that created it.

Taiichi Ohno’s rule was simple:

The mistake most organisations make is assuming the root cause sits close to the incident itself. In practice, the opposite is usually true.

What looks like a technical failure at Level 1 is often a process failure at Level 3 and a leadership failure at Level 5.

A missed deadline is rarely caused by “slow engineering.” A customer escalation is rarely caused by “poor communication.” An outage is rarely caused by “human error.”

Those are surface-level manifestations of deeper operating design problems.

The five-level pattern

A typical failure chain looks like this:

Problem: We missed the Q1 shipping deadline.

1. Why did we miss the deadline?
   Engineering delivery slipped.

2. Why did engineering delivery slip?
   Requirements kept changing.

3. Why did requirements keep changing?
   Sales kept committing to new features during the quarter.

4. Why was Sales doing that?
   Their incentives rewarded revenue generation, not delivery feasibility.

5. Why were incentives structured that way?
   Leadership optimised compensation for growth without integrating operational constraints.

That fifth layer matters because it changes the corrective action completely.

A Level 1 fix sounds like this:

“Engineering needs to execute faster.”

A Level 5 fix sounds like this:

“Redesign commercial incentives so feature commitments require delivery sign-off.”

One treats symptoms. The other changes the system.

The “human error” trap
Many organisations stop their root cause analysis at the first socially convenient answer:

“Someone forgot.”
“Someone missed the check.”
“Someone made a mistake.”

That is not root cause analysis. That is blame with administrative language.

If your investigation ends with “human error,” the process design is probably incomplete.

Humans forget things. Humans skip steps. Humans get distracted. Good operating systems assume this in advance.

Toyota’s manufacturing philosophy was built around this principle. Instead of trying to create flawless humans, they designed systems that prevented predictable mistakes from occurring in the first place.

That distinction matters because organisations that punish individuals for process failures usually create two downstream problems:

1. Employees hide issues earlier.

2. The underlying failure mechanism survives untouched.

The result is recurring operational instability.

The executive rule

A useful leadership rule is this:

Never fire a person for a process failure you designed.

That does not eliminate accountability. It relocates accountability upward, toward system ownership.

If the process allows a predictable mistake to occur repeatedly, leadership owns the process.

This is why mature operational cultures focus less on blame and more on controls, incentives, interfaces and decision architecture.

The goal is not to find who failed.

The goal is to understand why the system permitted the failure.

Use this prompt to run a proper root cause analysis.

Prompt: Toyota 5 Whys Analysis

The next problem: recurrence

Even when organisations identify the root cause correctly, many still fail because they do not redesign the process afterward.

They conduct the post-mortem, document the findings, circulate the slides, then leave the workflow unchanged.

That guarantees recurrence.

Toyota addressed this using “Poka-Yoke”: mistake-proofing mechanisms that either prevent the error entirely or make it immediately visible.

A strong operational process should not rely on memory, heroics or vigilance.

It should make the correct behaviour automatic.

Examples:

A deployment pipeline that blocks production release without test coverage
A CRM that cannot progress without mandatory data fields
A procurement workflow that requires dual approval above spending thresholds
A board paper template that forces the recommendation onto page one

The pattern is always the same:

Do not ask people to remember critical controls.
Engineer the controls into the system.

Use this prompt to redesign the process itself.

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Follow-Up Prompt: Poka-Yoke Process Design

The culture problem

There is another reason most post-mortems fail:

People become defensive before the meeting even starts.

The moment employees believe the process is about blame allocation, the organisation loses access to accurate information. Teams minimise ownership, hide uncertainty and protect themselves politically.

That destroys operational learning.

High-performing operational cultures do the opposite. They separate accountability from humiliation.

The rule is simple:

That framing matters because operational transparency depends on psychological safety. Teams only surface weak signals early if they believe the organisation wants truth more than scapegoats.

The leadership move is important here.

The senior leader should absorb ownership of the Level 5 issue personally. That lowers organisational temperature immediately because it signals that the objective is diagnosis, not punishment.

Use this prompt to open a blameless post-mortem properly.

Follow-Up Prompt: Blameless Post-Mortem

The executive takeaway

Most operational failures are not random.

They are system outputs.

If the same issue keeps recurring, the organisation is probably treating symptoms instead of redesigning the mechanism producing them.

Rebooting the server may restore service.
It does not explain why the outage happened.

Replacing the employee may remove the individual.
It does not remove the structural condition that created the mistake.

The leadership discipline is to keep digging until the organisation reaches the design flaw underneath the visible incident.

That is the purpose of the 5 Whys.

Not to explain failure.

To expose the system that generated it.

After I published my recent piece on AI and “the Clankers”, a previous work mate commented: “It is a fine article, but does bear the hallmarks of being written substantially with AI.”

It was a fair observation.

AI absolutely helped smooth parts of the piece. But the comment stayed with me for a different reason.

Because it exposed something much larger now happening inside organisations.

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AI is weakening many of the traditional proxies we used to identify talent.

For years, companies rewarded people who could produce polished outputs. Strong presentations. Sharp written communication. Clean synthesis. Structured strategy language. Entire careers were built around the ability to look competent in information-rich environments.

The problem is that AI is becoming very good at all of those things.

Which means leaders now face a harder question:

How do you identify actual capability once polished output becomes cheap?

This is already happening. Executives are reading immaculate briefing papers and wondering whether the person behind them can actually think. Managers are reviewing beautifully structured strategy decks and quietly questioning how much original judgment sits underneath the formatting and fluency.

The old signals are weakening.

That matters because most organisations were designed around observable output. Promotions, influence, and credibility often flowed toward the people who sounded the smartest in the room.

AI changes the economics of that competence.

A mediocre thinker with AI can now produce work that superficially resembles the output of someone much stronger. Not equivalent. But close enough to create management noise.

The premium shifts away from production and toward discernment.

Can this person make good decisions under uncertainty?
Can they identify what matters before the data is obvious?
Can they ask better questions than everyone else?
Can they explain why the machine is wrong?
Can they see second-order consequences?
Can they earn trust?

These were always valuable traits. AI simply makes them easier to distinguish from performative competence.

Ironically, this may improve some organisations over time.

For years, many companies accidentally rewarded presentation fluency over insight, activity over judgment, polish over originality, and information control over decision quality. AI compresses the value of those things.

That does not mean expertise disappears.

It means expertise becomes harder to fake.

A useful analogy came back to me after that comment.

If someone eats an extraordinary meal, they might say:

“You must have a great stove.”

But experienced chefs know the stove is not the meal.

The stove matters. Great tools absolutely matter. But tools amplify capability unevenly. A great chef with an average stove still outperforms most amateurs with a commercial kitchen.

AI works the same way.

A weak strategist with ChatGPT is still a weak strategist. They simply become faster at producing plausible-looking work. Meanwhile, exceptional operators become dramatically more effective because the machine removes low-value friction around execution.

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That distinction is going to matter enormously for leadership teams over the next few years.

The answer is not to ban AI. That will fail, and in most roles it would be a strange objective anyway.

But leaders do need moments where the tool is removed.

My kids still do some assignments and exams by hand. Not because handwriting is the future, but because sometimes you need to see what is actually in the student’s head.

Workplaces need the equivalent.

Not permanently. Not theatrically. But deliberately.

Ask someone to explain their recommendation without the deck. Give them a live problem and watch how they reason. Ask what they changed after using AI. Ask which part of the machine’s answer they rejected and why.

The test is no longer whether someone can produce polished output.

The test is whether they understand it well enough to defend, adapt, challenge, and improve it.

That creates a practical leadership shift.

Use AI for production.

Test humans for judgment.

The board paper can be AI-assisted. The discussion should not be. The strategy deck can be polished with tools. The executive presenting it should still be able to handle ten minutes of unscripted challenge.

This is how organisations cross the chasm.

They stop pretending AI use is the issue.

The real issue is whether the human has become more capable, or merely more fluent.

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There is a particular silence that appears in leadership conversations when AI becomes real.

Not when it appears on a strategy slide beside phrases like “workflow automation” or “productivity uplift”. Not when someone demonstrates a chatbot in a boardroom. Not when consultants promise operational transformation.

The silence arrives when someone realises that a machine can now do part of the work they once believed made them valuable.

A senior marketer watches a campaign draft appear in thirty seconds. A lawyer sees a passable first review of a contract. A technologist watches software emerge from a prompt. A manager notices that reporting, synthesis, coordination, and follow-up are beginning to collapse into software. A graduate entering the workforce starts wondering whether the bottom rung of the career ladder still exists in the same form.

That is the moment the conversation changes.

People call these systems AI. Others call them Clankers. The term is partly tongue in cheek, partly useful shorthand for machines that feel mechanical, relentless, and increasingly embedded inside the work we used to think of as deeply human.

The hostility toward AI is not really about the technology itself.

It is about the fear of becoming unnecessary.

If this captures a conversation happening inside your organisation, share it with someone trying to lead through the noise.

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For decades, professional value was built around scarcity. Scarcity of information. Scarcity of expertise. Scarcity of access. Scarcity of people capable of interpreting complexity and turning it into action.

Modern organisations were designed around that scarcity. We created layers to move information between teams. We created functions to protect specialised knowledge. We created approval chains to manage risk. We created management structures to coordinate work at scale.

Then the Clankers arrived and started attacking the bottlenecks.

Not perfectly. Not safely. Not without hallucinations, errors, or operational risk. But fast enough to expose which parts of work were genuine judgment and which parts were simply friction disguised as expertise.

That is why this technological shift feels different.

Earlier waves of automation mostly attacked muscle, movement, or calculation. AI reaches directly into cognition. It writes, summarises, analyses, codes, compares, classifies, and recommends. It enters the white-collar operating system itself.

This does not mean humans stop mattering. It does mean many humans are being forced to reconsider why they matter.

That is a much harder conversation than deciding which AI platform to deploy.

History gives us a useful pattern here, although not necessarily a comforting one.

The printing press did not destroy knowledge. It destroyed a monopoly over knowledge. Scribes were not irrational to feel threatened because their economic role, social status, and craftsmanship genuinely were under pressure. Yet the machine expanded access to ideas, accelerated literacy, and transformed the structure of society.

Photography created a similar disruption. Many painters feared art itself was becoming obsolete once machines could capture realism instantly. What actually happened was more interesting. Once cameras handled realism more efficiently, painters moved toward interpretation, emotion, abstraction, and perspective.

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Calculators did not destroy mathematics. They reduced the value of manual arithmetic while increasing the value of reasoning and problem-solving.

The pattern is not that technology leaves everything untouched.

It is that technology changes where human value lives.

That pattern matters because AI is beginning to reshape the economics of knowledge work itself.

The strategic implication is easy to underestimate. AI is not merely another efficiency layer or software upgrade. It is a pressure test on the organisation’s theory of value.

If a process becomes dramatically cheaper and faster, what remains scarce? If generic knowledge work becomes abundant, what becomes premium? If first drafts are effectively free, what is actually worth paying for?

The answer is not more dashboards, more AI pilots, or more generated content.

The answer is judgment.

Judgment about what matters. Judgment about what is true. Judgment about what customers will trust. Judgment about which risks are acceptable and which are not. Judgment about where automation creates leverage and where human oversight remains essential.

This is where the Clanker conversation stops being a technology discussion and becomes a leadership discussion.

A machine can generate options, but it cannot own consequences. A machine can produce analysis, but it cannot carry accountability. A machine can accelerate execution, but it cannot decide what kind of organisation you are trying to build.

That remains human work.

Ironically, the AI era may increase the value of leadership while reducing the value of certain forms of management.

Many organisations have management layers that exist primarily to move information between systems, functions, and meetings. AI is beginning to compress that coordination premium. When reporting, synthesis, analysis, and follow-up become partially automated, some forms of organisational complexity start to look less necessary.

Peter Drucker spent decades warning that knowledge work would eventually force organisations to rethink management itself. AI may simply be accelerating the moment where information stops being the primary source of organisational value.

I explored this more directly in my earlier piece on management layers and AI compression, because the real organisational impact of AI is unlikely to come from chatbots alone. It will come from redesigning how decisions move through the system.

That does not make leadership less important.

It makes weak leadership easier to see.

The managers who create clarity, build trust, make decisions under uncertainty, and develop people become more valuable. The managers who mainly route information between meetings become harder to justify.

The same dynamic is beginning to appear across professional functions. AI will not eliminate legal judgment, but it may reduce the value of routine document review. It will not eliminate marketing, but it may reduce the premium on generic production work. It will not eliminate technology leadership, but it will change the economics of coding, support, and software delivery. It will not eliminate strategy, but it may expose how much “strategy” was simply expensive synthesis with little conviction attached to it.

The Clankers are not eliminating expertise.

They are exposing which expertise was genuinely scarce.

Where do you think AI genuinely reduces human value, and where does it force human value upward?

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There is also a reason many people instinctively recoil from AI hype.

The evangelists often skip over the emotional reality of transition. They talk about leverage, automation, and productivity as though people are merely inefficient systems waiting to be upgraded. That language may work inside a spreadsheet, but it fails inside organisations made of human beings.

People do not work solely for income. They work for identity, status, usefulness, belonging, and dignity. When a machine suddenly performs something they spent years learning, the reaction is not purely economic.

It is personal.

That does not mean all resistance is correct. But it does mean the resistance is real.

Leaders who ignore that reality will fail, not because the technology is weak, but because transformation is social before it is technical.

This is where many AI initiatives will quietly stall. Not in procurement. Not in model selection. Not in vendor evaluation. They will stall in the middle of organisations, where people are close enough to understand the capability and senior enough to feel threatened by it.

The most exposed group may not be junior staff. It may be the professionals whose value historically came from coordination, translation, reporting, and information control. AI challenges all four simultaneously.

This is also why simplistic slogans like “AI will not replace you, but someone using AI will” feel incomplete. They contain some truth, but they miss the deeper structural shift underway.

The divide will not simply be between people who use AI and people who do not.

It will be between organisations that redesign around abundance and organisations that continue operating as though information scarcity still protects them.

Once knowledge work becomes partially abundant, the premium shifts. It shifts from production to perspective. From access to trust. From speed to judgment. From managing work to designing systems where better work can happen.

That is the deeper leadership challenge now.

Not adoption.

Discernment.

The organisations that benefit most from AI will not necessarily be the ones with the most tools. They will be the ones capable of redesigning work thoughtfully around the new economics of cognition.

They will ask better questions.

Where is knowledge trapped? Where are skilled people wasting time on low-judgment tasks? Where does AI reduce friction? Where does it increase risk? Where do customers still require deeply human trust and accountability?

Those are not technical questions.

They are operating model questions.

And beneath them sits a profoundly human question.

What kind of work should humans now be doing?

That is why the Clanker fear deserves more respect than it usually receives. Beneath the panic, cynicism, and online shouting sits a rational understanding that the old professional bargain is changing. Learn the system. Accumulate expertise. Become difficult to replace.

AI weakens parts of that bargain.

But it does not eliminate the need for humans.

It raises the standard for what human contribution must become.

The printing press forced society to rethink knowledge. Photography forced artists to rethink art. The internet forced companies to rethink distribution.

AI is forcing organisations to rethink human value itself.

Not philosophically.

Operationally.

Who decides? Who checks? Who carries accountability? Who earns trust? Who understands the customer deeply enough to know whether the machine is right, wrong, or merely plausible?

Those questions will matter far more than which model is marginally better this quarter.

The Clankers are here. They will improve. They will become more embedded, more ordinary, and eventually less interesting as technology. That is what powerful technologies do. They move from wonder to infrastructure.

The real question is what leaders build around them.

If organisations use AI to flood systems with more mediocre output, they will deserve the backlash. If they use AI to remove humans from decisions requiring accountability, they will create risk at scale. If they use AI to avoid thinking, they will become faster and dumber simultaneously.

But if they use AI to remove low-value friction, improve decision quality, expand capability, and move human attention toward higher-order judgment, then the story changes entirely.

The Clankers do not have to make us less human.

They may force us to become more deliberate about what only humans should do.

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Disagree strongly? Good. Share it with your own view attached. The useful argument is not whether AI is good or bad, but where human value moves next.

I am genuinely interested in the considered disagreement here.

If you believe AI diminishes human value, make the case. If you think leaders are still underestimating the opportunity, make that case too. If you think the real risk is not the technology itself but the way organisations deploy it, I suspect you are closer to the truth than either extreme.

The worst outcome is not disagreement.

The worst outcome is letting the Clankers arrive while leaders continue asking small questions.

The big question is not whether AI can do more work.

It can.

The big question is what kind of work humans should now be doing.

For twenty years, digital marketing had a fairly simple visibility model.

You ranked for keywords.
You bought keywords.
You measured clicks, cost and conversion.

That model is now being interrupted by AI answers.

When a customer asks ChatGPT, Gemini, Claude, Perplexity, Copilot or Google AI Mode what to buy, which vendor to trust, what risks to consider or how to compare two products, your brand may be included, ignored or misrepresented before the buyer ever reaches your website.

That is why Answer Engine Optimisation, or AEO, matters.

But here is the bit CEOs and CMOs need to hear first:

The mistake is treating AEO as “SEO, but with prompts.”

It is not.

SEO and SEM became mature because search was measurable and monetised. Google built tools around keyword demand, ad auctions, impressions, clicks and conversion. That gave marketers a shared operating model.

AEO does not have that yet.

There is no reliable equivalent of a Google Keyword Planner for ChatGPT, Claude, Gemini, Perplexity and Copilot. There is no universal dashboard showing the exact prompts your buyers ask, how often they ask them, where you rank, which competitors appear, and which AI answer converted into pipeline.

Some vendors are trying to approximate this. Some are useful. But the category is still early.

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The market is moving anyway

This is not theoretical.

Google says AI Overviews and AI Mode can use “query fan-out,” where one user question is broken into multiple related searches across subtopics and sources before an answer is assembled. That means brands are not simply competing for one keyword anymore; they are competing to be useful across a buyer’s whole question set.

Google is also already serving ads in AI Overviews. But advertisers cannot directly target AI Overview placements, cannot opt out, and do not currently get segmented reporting when ads appear inside Search AI Overviews.

OpenAI is testing ads in ChatGPT too, while saying ads are separate from answers and advertisers cannot shape, rank or alter ChatGPT’s responses.

So the future is not “AI answers versus advertising.”

The future is AI answers and advertising, sitting inside the same customer journey.

The CEO question is not “Can marketing game ChatGPT?”

The CEO question is “Are we visible, accurate and trusted when AI systems explain our category?”

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What to do this quarter

Start with a simple AEO visibility audit.

Ask your marketing team to identify 25 to 50 commercially important questions a buyer might ask before choosing a vendor.

Not vanity prompts like:

“Who is the best company in our category?”

Use real buyer prompts:

“What are the best platforms for [use case]?”
“How do I compare [your brand] and [competitor]?”
“What are the risks of implementing [category]?”
“What should a CFO know before buying [solution]?”
“Which providers are best for mid-market companies?”
“What are the common mistakes when choosing [category]?”

Run those questions across the major AI engines.

Track four things:

1. Does your brand appear?

2. Which competitors appear?

3. Which sources are cited?

4. Is the answer accurate?

This is not perfect measurement. It is a baseline.

It will show whether AI systems understand your company, whether your competitors are better represented, and which third-party sources are shaping the answer.

That last point matters. In AI answers, your own website is not the only source of truth. Review sites, analysts, media coverage, documentation, customer stories, partner pages, product feeds and comparison pages can all influence what the system retrieves and summarises.

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Fix the boring stuff first

Before buying an AEO platform, make sure the fundamentals are not broken.

Can search engines crawl your important pages?
Are your product and pricing pages clear?
Are your comparison pages honest and current?
Do you explain use cases in plain language?
Do you publish evidence, not just slogans?
Are your customer stories specific enough to be useful?
Are your schema, merchant data, business profiles and documentation up to date?

Google’s own guidance for AI features is not “add a magic AI tag.” It says site owners should focus on standard search fundamentals: crawlability, useful content, structured data that matches visible content, and good page experience.

That is not glamorous. It is also the work most companies still have not done well.

Use AEO tools, but do not worship them

Tools such as Peec AI, Profound and similar platforms can help monitor how your brand appears across AI engines. They can track prompt sets, brand mentions, competitor visibility, citations and sentiment.

That is useful.

But these tools should be treated as sensors, not truth machines.

Many rely on controlled prompt testing. That means they show what an AI system said when asked a selected set of questions at a selected point in time. They do not necessarily show every real prompt your buyers used, every answer they saw, or every commercial impact that followed.

That does not make the tools useless. It means the output should guide experiments rather than become a board KPI without caveats.

A practical CMO dashboard might include:

• Share of AI answers where your brand appears.

• Competitors most often mentioned.

• Pages and third-party sources most often cited.

• Incorrect or outdated claims.

• AI referral traffic and conversion.

• Content gaps by buyer question.

• Changes month on month.

Microsoft is already moving in this direction with Bing Webmaster Tools’ AI Performance reporting, which shows cited pages and grounding query phrases for AI-generated answers across supported Microsoft experiences.

That is closer to where measurement is heading: not just “How much traffic did we get?” but “Are we being used as a source of truth?”

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The leadership takeaway

AEO is not mature enough to be treated like paid search.

But it is too important to ignore.

The companies that get ahead will not be the ones trying to stuff prompts into blog posts. They will be the ones making their business legible to AI systems: clear product information, useful category education, current data, strong third-party validation and content that answers the questions buyers actually ask.

For CEOs, this is a visibility and trust issue.

For CMOs, it is a measurement and content operating model issue.

For both, the instruction is simple:

Do not overreact.
Do not buy snake oil.
Do not wait until AI answers become the default interface for your category.

Because by then, your competitors may already be the answer.

Most Federal Budget analysis follows a familiar script.

Who won? Who lost? What does it mean for inflation? Will the Reserve Bank move? Which industry received the biggest cheque? Which household received the biggest rebate?

That analysis has its place. But it is not the most useful lens for Australian CEOs.

The more useful question is this:

That is the real story inside the 2026–27 Federal Budget.

Read as a traditional Budget, it is a mix of tax reform, fuel security, productivity measures, housing policy, regulatory changes, digital government investment, AI initiatives, energy intervention and cost-of-living relief.

Read as a business operating-system update, the pattern is clearer.

The Government is attempting to reconfigure the execution layer of the Australian economy: less regulatory duplication, faster approvals, more nationally consistent rules, a more digital interface with government, AI-assisted administration, more resilient energy and supply chains, and tax settings that push capital away from passive optimisation and toward productive investment.

That is the insight CEOs should take seriously.

Not because the Government has solved productivity. It has not.

Not because every reform will be delivered cleanly. It will not.

And not because business should accept every Budget claim at face value. It should not.

The Budget’s own productivity package claims it will reduce regulatory burden by $10.2 billion each year, lift long-run GDP by around $13 billion a year through National Competition Policy work with states and territories, and unlock an additional $400 million a year in R&D investment by young enterprises.

Those are large claims, but they are not automatic savings landing in company bank accounts.

The regulatory-burden estimate is a gross reduction, based on reforms being fully implemented, and some gains depend on policy design, agreement and implementation with states and territories.

That caveat matters.

But so does the direction of travel.

Across the Budget Papers and supporting material, the same verbs keep appearing: streamline, harmonise, accelerate, modernise, digitise, simplify, coordinate, reserve, secure, target and reform.

A conventional Budget wrap treats those as separate policy announcements.

A C-suite reading treats them as connected system changes.

The Budget is not just giving with one hand and taxing with the other. It is trying to change the rules of execution.

And that creates an immediate practical task: every executive team should run a 90-day Budget OS review.

Not a political discussion. Not a generic economist briefing. Not a government-relations note circulated after the fact.

A practical operating-model review.

In the next 90 days, CEOs should ask their executive teams to map the company’s top regulatory frictions, approval bottlenecks, digital-government touchpoints, AI-readiness gaps, tax and capital-allocation implications, energy and supply-chain exposures, workforce mobility constraints and state-by-state scaling costs.

The businesses that do this well will be better positioned to convert policy movement into execution speed.

The businesses that do not may discover that even when government removes friction, the bottleneck simply moves inside the enterprise.

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The Budget is not the strategy

The first mistake is to treat the Budget as the strategy.

It is not.

A Budget is an input into strategy. It signals intent, funding, constraints, priorities and direction. It does not guarantee delivery.

This distinction matters because the 2026–27 Budget contains a lot of ambition.

It is being delivered in a difficult macro environment. The Budget Papers describe the conflict in the Middle East as disrupting global oil supplies, creating volatility and straining supply chains, with headline inflation forecast to reach 5 per cent through the year to the June quarter 2026 and Australian economic growth forecast to slow from 2¼ per cent in 2025–26 to 1¾ per cent in 2026–27.

For business leaders, that means the Budget is not being written in calm conditions.

It is being written while the system is under load.

That is why the Budget’s central frame, resilience and reform, is more than a slogan. It is the operating tension.

Australia is trying to make the economy more resilient while also making it faster, more productive and more digitally enabled.

That is hard.

Resilience often adds buffers, redundancy, safeguards and intervention.

Productivity often requires simplification, competition, speed and lower friction.

This Budget tries to do both.

That makes it strategically important, but also implementation-heavy and politically exposed.

Government is part of your operating environment

One of the more overlooked lines in the Budget Papers is in Budget Paper No. 4.

It says businesses rely on government systems for registration, tax, superannuation and regulatory schemes, and notes that agencies will manage around $833.3 billion in 2026–27 to deliver services for individuals, families and businesses.

That should stop executives for a moment.

Government is not just a policy actor sitting outside the business.

For Australian companies, government is part of the operating environment.

It defines market rules. It approves projects. It sets reporting obligations. It regulates identity. It collects and verifies data. It shapes energy markets. It funds infrastructure. It influences labour mobility. It sets tax incentives. It decides which business structures are encouraged, tolerated or penalised.

For some companies, government is also a major customer, funder, regulator, platform provider or data counterparty.

That means changes to government systems are not just “public sector reform”.

They are changes to the business operating system.

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What Gen AI helps expose

This is where Gen AI can add value to Budget analysis.

Not by summarising the Budget faster. Everyone can do that now.

The value is in pattern recognition across documents.

When you read across Budget Paper No. 1, Budget Paper No. 2, Budget Paper No. 3, Budget Paper No. 4, tax explainers, productivity factsheets, regulatory reform material and portfolio statements, a different structure emerges.

Regulatory friction. Approval friction. Data friction. Identity friction. Energy friction. Tax friction. Capital friction. Labour mobility friction. Federation friction.

That is the C-suite insight.

The Government is trying to identify where the Australian economy is slow, duplicated, inconsistent, paper-based, approval-constrained, energy-exposed or tax-distorted and then patch those areas.

Whether the patch works is a separate question.

But the map itself is valuable.

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Layer one: regulation becomes a productivity battlefield

The clearest operating-system layer is regulation.

The Whole-of-Government Regulatory Reform Agenda focuses on five areas: better regulation, a single national market, faster processes, modernising regulation and regulatory stewardship. The stated objectives are to reduce compliance time and costs, make it easier to build, invest and innovate, and improve government services.

For CEOs, the important point is not simply that “red tape is being cut”.

That phrase is too blunt.

If approval pathways get faster, reporting obligations become more digital, payroll tax administration becomes more harmonised, company registers become more reliable and regulators become more data-enabled, then the companies that benefit first will not necessarily be those with the loudest public-policy voice.

They will be the companies with the cleanest data, clearest governance, fastest internal decision cycles and most mature compliance operations.

That is the hidden edge.

A business with fragmented systems, poor documentation, unclear ownership of regulatory obligations and manual reporting processes may see little benefit from a simpler external system. The friction simply moves inside the company.

A business with modern legal operations, automated evidence capture, strong data governance and proactive regulatory intelligence will be better positioned to convert reform into speed.

This is why the Budget should not only be read by government affairs teams.

It should be read by the COO, CIO, CFO, General Counsel and Chief Risk Officer together.

Layer two: approvals become a board-level timing variable

The Budget repeatedly returns to approvals: environmental approvals, housing approvals, foreign investment approvals, resources approvals, telecommunications approvals and construction processes.

That matters because approvals are not just compliance events. They are timing variables.

For project-heavy sectors — energy, infrastructure, resources, housing, telecommunications, logistics and advanced manufacturing — approval time affects capital deployment, financing cost, delivery sequencing, stakeholder confidence and competitive position.

Budget Paper No. 4 says the Government is providing more than $500 million over four years from 2026–27 to implement environmental law reforms, establish the National Environmental Protection Agency from 1 July 2026, develop streamlined assessment pathways with states and territories, and modernise environmental information, data and digital systems, including through AI. It also says on-time decisions under the EPBC Act increased from 78 per cent in 2022–23 to 95 per cent in 2025–26.

That is material for boards.

The question is not simply, “Will approvals get faster?”

That requires knowing which projects in the capital pipeline are approval-constrained, which are documentation-constrained, which require state or local interfaces, and which would benefit from pre-emptive environmental, community or data work.

But there is a critical counterpoint.

Faster approvals are only valuable if they are trusted approvals.

If speed is achieved by making processes more opaque, community trust may deteriorate and litigation risk may rise. AI-assisted regulatory systems can help triage, guide and accelerate decisions, but they also create risks around explainability, procedural fairness, model governance and public confidence.

Business should avoid the lazy formulation that faster is always better.

The better test is whether the system can deliver approvals that are faster, more predictable and more trusted.

Layer three: the federation tax on scale may fall

Australia’s federal structure has long imposed a hidden tax on scale.

A company operating across states can face inconsistent licensing, screening, tenancy, payroll tax, construction, transport, product safety and reporting requirements.

Large companies absorb this through legal and compliance overhead.

Smaller companies slow down, avoid jurisdictions or choose not to scale nationally.

The Budget’s single national market agenda is therefore strategically important. The reform areas include worker screening, occupational licensing, health practitioners’ scope of practice, non-competes, harmonised standards, heavy vehicle reforms, right to repair, retail tenancy, payroll tax administration and consumer energy resources.

This is especially relevant for scaleups and mid-market businesses.

The Australian market is already small by global standards. Fragmenting that market into state-by-state compliance variants makes it smaller again.

A more nationally consistent market could lower the operating cost of expansion. It could also improve labour mobility, particularly in sectors where licensing and screening delays constrain capacity.

But this is also one of the Budget’s biggest implementation risks.

The Commonwealth can publish the release notes. It cannot single-handedly force every state, territory, regulator, local authority and agency to install the patch on time.

Budget Paper No. 3 shows total payments to the states of $207.8 billion in 2026–27, rising to $238.2 billion in 2029–30.

That gives Canberra influence, but not omnipotence.

Many of the productivity gains depend on coordination across governments. For CEOs, that means the reform agenda should be treated as a probability curve, not a certainty.

A national retailer, builder, care provider, health operator, logistics business or professional-services platform should not wait passively for harmonisation.

It should map the highest-friction cross-border processes now and identify which reforms, if delivered, would change expansion economics.

Layer four: government becomes more like an API

One of the most underappreciated Budget stories is the changing interface between business and government.

The Budget advances a more digital government interface through Digital ID, the Consumer Data Right, tell-us-once reforms, business register uplift, Director ID integration, ABN and Super Fund Lookup improvements, and better synchronisation between ASIC and the Australian Charities and Not-for-profits Commission.

The Budget Overview also says the Government is investing $654.3 million to expand Digital ID and $62 million into the Consumer Data Right.

The C-suite implication is significant: government is becoming more platform-like.

That does not mean government will become easy to deal with.

It means the interface will become more digital, more identity-enabled, more data-linked and, eventually, more automated.

For CEOs, that affects customer onboarding, payroll, tax, grants, procurement, regulated service delivery, privacy architecture, due diligence and fraud prevention.

For CIOs and CDOs, it raises interoperability questions.

For General Counsel and Chief Risk Officers, it raises questions about data sharing, consent, identity proofing and cyber exposure.

There is real upside here.

A well-executed Digital ID and CDR ecosystem could reduce duplicated data collection, lower onboarding friction and improve trust.

But there is also a serious caution.

“Tell us once” is attractive until the data is wrong, stale, over-shared or breached.

Digital identity infrastructure is only as valuable as the trust architecture around it.

The Budget’s direction is clear. The executive task is to prepare for a world in which dealing with government becomes less paper-based and more API-like — but also more auditable, more data-dependent and potentially more enforceable.

Layer five: AI is the control plane

There is a temptation to scan the Budget for “the AI package” and then decide whether the technology sector won or lost.

That misses the bigger story.

The Budget’s most important AI signal is not a standalone AI grant.

It is the embedding of AI into regulatory and administrative workflows.

The regulatory reform material says the APS is exploring AI to improve efficiency, reduce friction, improve regulatory navigation, increase transparency and support more timely and consistent service delivery. Examples include the Therapeutic Goods Administration using AI to evaluate medicines already approved by comparable overseas regulators, the environment department piloting AI to assist proponents seeking EPBC approvals, Finance expanding GovAI, and the fisheries regulator investing in AI tools for electronic monitoring.

Budget Paper No. 4 also describes AI use cases across government, including environmental regulation, veterans’ claims, IP Australia tools, ATO myTax prompts and National Library transcription.

For business, this is more consequential than it may look.

It means companies will increasingly interact with government systems that are AI-assisted. They may be guided, assessed, prompted, triaged or monitored through AI-enabled processes.

That changes the compliance model.

The old model was document-heavy and episodic: submit, wait, respond, escalate.

The emerging model is data-heavy and continuous: expose, verify, query, reconcile, monitor.

This is not automatically good or bad. It depends on implementation.

But it does mean companies need to think about machine-readable compliance.

Can your organisation produce clean evidence quickly?
Are obligations mapped to owners, systems and records?
Can you explain how data was generated?
Can you reconcile regulatory submissions with operational reality?
Can you respond to AI-assisted government queries without scrambling across spreadsheets, emails and legacy systems?

The Budget’s AI story is not just about whether businesses use Gen AI.

It is about whether government does — and whether business is ready to operate in that environment.

Layer six: tax reform nudges capital allocation

The Budget’s tax reforms will attract plenty of political commentary.

For CEOs and CFOs, the more useful lens is capital allocation.

Budget Paper No. 1 describes the tax package as including changes to negative gearing, capital gains tax and discretionary trusts, alongside business measures such as loss treatment improvements, venture capital incentives, a permanent small business instant asset write-off and a more targeted R&D Tax Incentive.

The business-side reforms are strategically important.

The Budget says the Government will introduce permanent two-year loss carry back for companies with up to $1 billion in turnover from income years after 1 July 2026, with the measure expected to directly benefit up to 85,000 companies each year.

It also makes the $20,000 instant asset write-off permanent for businesses with turnover of up to $10 million, which the Budget says will provide long-term certainty for up to 4.1 million businesses.

From a strategy perspective, the direction is clear.

The tax system is being used to shift the after-tax return profile away from passive asset accumulation and toward work, new housing supply, innovation, startups and business investment.

That does not mean every measure is simple or low-risk.

Family-owned businesses, private groups, property investors and founders using trust structures will need careful advice. Some reforms phase in later. Some create planning windows. Some may produce behavioural changes that are hard to forecast.

For CFOs, the question is not merely “what is the tax impact?”

The sharper question is:

For some businesses, the answer will be yes.

For others, the answer will be marginal.

For trust-heavy private groups, the answer may be that the Budget increases the urgency of structural review.

Layer seven: energy and supply chains move to the centre

The Budget’s response to the global oil shock is one of its clearest resilience signals.

The Budget Overview says the conflict has affected supply chains for fertiliser, chemicals, aluminium and plastics, while higher fuel prices are expected to weigh on households and business activity.

The Government is also introducing a 20 per cent domestic gas reservation from 1 July 2027, requiring LNG exporters to reserve a proportion of production for Australian users.

For COOs, procurement leaders and CFOs, the message is blunt: energy, fuel and logistics resilience are now board-level variables.

This Budget treats fuel not merely as a commodity but as strategic infrastructure.

It also treats electrification, domestic gas availability, clean fuels and supply-chain monitoring as part of national resilience.

That may be sensible in the shock environment.

But it also means government is becoming more interventionist in strategic inputs.

The domestic gas reservation is a good example.

For domestic gas users, it may offer greater supply confidence and price relief.

For LNG exporters and investors, it may raise questions about sovereign risk, contracting expectations and future policy intervention.

This is the pattern CEOs should watch: the Budget is lowering some risks by increasing government involvement in strategic markets.

That can be stabilising in a crisis.

It can also change investment signals.

The practical executive response is not ideological. It is to model exposure.

Which parts of your cost base are vulnerable to global fuel prices, gas availability, freight disruption, supplier stress or regulatory intervention in strategic inputs?

Which suppliers are themselves exposed?

What is the pass-through mechanism into price, margin and service levels?

If energy resilience is part of the national operating system, it should also be part of enterprise strategy.

The hidden dividend is speed

The Budget promises lower compliance costs, faster approvals, simpler tax, better digital identity, more consistent national rules and AI-enabled government systems.

But here is the uncomfortable truth:

Every operating-system upgrade creates winners and laggards.

Companies with modern ERP systems, clean customer identity processes, strong data governance, reliable document management, mature tax operations, integrated risk systems and active regulatory monitoring will be able to move faster.

Companies with fragmented systems, inconsistent records, spreadsheet dependency and unclear accountability will not.

That is why this Budget should trigger a C-suite readiness review.

The right question is:

If Canberra removes friction, are we operationally capable of converting that into speed?

For many organisations, the answer will be uncomfortable.

The critical read

There is a lot in this Budget for business to welcome.

Lower regulatory burden, faster approvals, more nationally consistent markets, stronger digital identity, better data flows, improved business tax treatment and greater energy resilience are all legitimate business priorities.

But CEOs should not read the Budget like a government brochure.

There are three reasons to be cautious.

First, many of the benefits are prospective, conditional or dependent on implementation. The regulatory-burden numbers are gross estimates, assume full implementation and, in several areas, rely on agreement and delivery across states and territories.

Second, digital government can reduce friction while increasing exposure. Digital ID, CDR and tell-us-once reforms may simplify interactions, but they also increase the importance of cyber resilience, consent management, data quality and identity governance.

Third, faster government can become more powerful government. AI-enabled regulators, better data matching, real-time payment checks, business-register uplift and fraud detection may reduce waste and improve service delivery. They may also increase enforcement velocity.

That is good for compliant businesses.

It raises the stakes for businesses with messy data, weak controls or casual reporting habits.

This is not a reason to oppose reform.

It is a reason to prepare for it properly.

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The 90-day Budget OS review

The most useful response to this Budget is not to ask, “What did we get?”

Ask this instead:

A practical 90-day executive review should produce three outputs: a friction map, a readiness scorecard and a board-level decision agenda.

Days 1–15: build the friction map

Start by identifying the top 20 external frictions that slow the business down.

Include regulatory approvals, reporting obligations, state-by-state differences, tax administration, identity checks, supplier assurance, data requests, workforce licensing, government procurement, energy costs, grants, import/export processes and project approvals.

Rank each friction by cost, delay, executive attention, customer impact and strategic importance.

Then map each friction against the Budget’s reform areas.

The purpose is to identify where government policy may create a speed advantage — and where the business is not ready to take it.

Days 16–30: review the approvals pipeline

For every major project, identify the critical path.

Which approvals are Commonwealth, state or local?

Where are the dependencies?

What evidence is missing?

What environmental, community, planning, foreign investment or infrastructure constraints could slow the project?

Where might AI-enabled, streamlined or harmonised processes become relevant?

The goal is not to assume approvals will magically accelerate.

The goal is to prepare the company so that if approval pathways improve, internal readiness is not the bottleneck.

Days 31–45: assess digital-government readiness

Map every major point where the company interacts digitally with government.

That may include tax, superannuation, payroll, ASIC, ABR, Director ID, grants, procurement, sector regulators, Services Australia, Digital ID, CDR, customs, workplace relations, health, aged care, energy or environmental regulators.

Then ask: are our systems ready for more digital, data-linked, identity-enabled interaction with government?

This is not only an IT question.

It is a governance question.

Days 46–60: test machine-readable compliance

Run a practical compliance drill.

Pick three regulatory obligations and ask the business to produce the supporting evidence quickly.

Can the evidence be pulled from governed systems?

Is ownership clear?

Are records complete?

Can the company reconcile what it reports externally with what is happening operationally?

Could the evidence withstand an AI-assisted regulatory query?

If the answer requires spreadsheets, inbox searches and heroic manual effort, the company is not ready for the next phase of digital regulation.

Days 61–75: review tax, capital and structure

The CFO should translate the Budget’s tax changes into investment implications.

Do loss carry-back, loss refundability, venture capital incentives, R&D changes or the instant asset write-off change investment timing?

Do trust, CGT or negative gearing reforms affect ownership structures, succession planning or private-group strategy?

Do tariff, reporting or business-register changes reduce compliance cost?

Are there investments that now have a different after-tax risk profile?

The goal is not tax minimisation.

The goal is better capital allocation.

Days 76–90: stress-test resilience

Finally, the executive team should stress-test energy, fuel, freight, supplier and geopolitical exposure.

What happens if fuel prices remain elevated?

What happens if gas availability changes?

Which suppliers are exposed to fertiliser, chemicals, aluminium, plastics or freight disruption?

Can costs be passed through?

Where are contracts too rigid?

Where is inventory too lean?

Where does the company need supplier substitution, dual sourcing, electrification, hedging or pricing flexibility?

This is the resilience layer of the Budget.

It belongs at executive committee level, not just in procurement.

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The CEO takeaway

The 2026–27 Federal Budget is not just a fiscal document.

It is an attempted rewrite of parts of Australia’s business operating system.

Some of the rewrite is compelling.

Lower regulatory burden, faster approvals, better digital identity, more nationally consistent markets, stronger resilience and more productive capital allocation are all things Australian business has needed for years.

Some of it is uncertain.

The claimed productivity benefits rely on implementation. Commonwealth-state coordination is hard. AI-enabled regulation must be trusted, not just fast. Digital government must be secure, not just convenient. Tax reform may improve long-term incentives while creating near-term complexity for private groups and investors.

That is why CEOs should read this Budget neither as a handout list nor as a government brochure.

Read it as a directional map.

The Government is signalling the economy it wants: faster to build, easier to invest in, harder to game, more digital, more AI-enabled, more resilient, more nationally consistent and less tolerant of passive tax arbitrage.

The companies best positioned for that economy will not be the ones that merely understand the Budget.

They will be the ones that update their own operating systems first.

Teams usually present the data first, then the analysis, then the implications, and finally the recommendation. That may reflect how the thinking happened, but it is not how senior leaders consume information. Executives do not want to reconstruct the argument in real time. They want the answer first, then the evidence that supports it.

That is the discipline behind Barbara Minto’s Pyramid Principle, developed during her time at McKinsey. The core rule is simple:

This matters because executive decision-making depends on compression. A CEO, board member or executive committee should be able to understand the recommendation, the decision required, the reason it matters, the evidence and the next step within the first minute.

If your presentation saves the “big reveal” for slide 19, you have already lost the room.

The mystery novel problem

Inexperienced leaders often treat presentations like mystery novels. They build context, reveal clues, walk the audience through the analysis, and hold the conclusion until the end.

That is fine for fiction. It is terrible for board communication.

The board does not want a journey. It wants the bottom line. The first slide should contain the full argument; the rest of the deck should defend it. The same principle applies to executive emails. The first sentence should tell the reader what you need from them: the ask, the decision, the recommendation, the risk or the answer.

This is not a style preference. It is an operating issue. Bottom-up communication forces senior leaders to do the writer’s work: extract the point, infer the recommendation and identify the decision. That slows the organisation down.

The executive rule

Before sending a board paper, strategy update or executive email, apply one test:

If not, the communication is not ready.

Poor executive communication is rarely caused by weak thinking. More often, the answer exists but is buried under context, process, detail and corporate cushioning. The fix is to put the conclusion first, then organise the evidence.

Use this prompt to clean up a single draft.

Prompt: Minto Pyramid Rewrite

The point is not to make the writing sound like McKinsey. The point is to force the thinking into executive order: answer first, logic second, evidence third, detail last.

But fixing one draft is not enough. The bigger problem is that most leadership teams do not have a shared communication standard. Every team builds decks differently. Every update has a different structure. Some people lead with the answer; others lead with process. Some bury the ask; others hide weak logic under dense slides.

That creates executive drag.

For paid subscribers, below is the executive communication standard I would give to a CEO, Chief of Staff, COO or strategy leader who wants faster, clearer decisions from their team.

The Executive Communication Standard

Most leadership teams do not have a writing problem. They have a decision-throughput problem.

The CEO is forced to decode the ask. The board is forced to infer the recommendation. The executive committee is forced to sit through the analysis before seeing the conclusion. Over time, this becomes an operating tax on the organisation.

Use the following standard with your team.

1. The 60-second rule

Every executive communication must be understandable in 60 seconds. The reader should be able to identify the recommendation, the decision required, why it matters, the evidence, the trade-off and the next step.

This applies to board papers, executive emails, investment cases, risk papers, transformation updates, vendor recommendations and operating reviews. The standard is not “short.” The standard is clear. A long paper can still be executive-ready if the answer is obvious early. A short email can still fail if the ask is buried.

2. The first-slide rule

Every board deck must have a first slide that contains the full argument. Not a contents page, background slide, market overview or project timeline. The actual argument.

Use this structure:

Recommendation: What should we do?
Why now: What has changed or become urgent?
Evidence: What proves this is the right move?
Trade-off: What are we accepting, rejecting or risking?
Decision required: What do we need from the board?

If slide one does not contain the argument, the deck is not ready. The rest of the deck should defend the first slide, not discover it.

3. The executive email rule

Every executive email should start with one of four labels:

Decision: I need you to decide X by Y.
Recommendation: I recommend X because Y.
Risk: X is off track and requires intervention.
Update: No decision required; this is for awareness.

That one change tells the reader how to process the message before they start reading.

A weak executive email starts like this:

Hi Sarah, hope you’re well. I wanted to provide some background on the vendor implementation workstream. As you know, we kicked this off in March following the steering committee discussion…

A better version starts like this:

Decision: I need your approval by Friday to pause the vendor rollout for two weeks because the current integration risk is higher than the launch plan assumes.

The context is not removed. It is moved below the decision.

4. The decision-request template

Use this structure for any executive ask:

This template forces the writer to separate the decision, the recommendation, the evidence and the trade-off. That separation matters because executives do not just need to know what you think. They need to know what choice they are being asked to make.

5. The board-deck rule

The main deck is for judgement. The appendix is for proof.

Most board decks are too long because teams confuse analysis with insight. They include every chart because they did the work. They keep every slide because someone might ask. They present the process instead of the conclusion.

Use this rule:

If a slide supports, explains, documents or proves the work, it probably belongs in the appendix. Do not delete the detail. Just stop leading with it.

When challenged, use this line:

I have that detail in the appendix. I have not led with it because it supports the recommendation rather than changes it. I can pull it up if useful.

That sentence shows you have done the work, keeps the room focused on the decision, and prevents the meeting from being hijacked by supporting analysis.

6. The red flags

Your team is communicating bottom-up if you regularly see:

• “By way of background…” in the first paragraph

• decks that start with market context

• emails where the ask appears after paragraph four

• slides titled “Analysis” but not “Implication”

• recommendations supported by adjectives instead of evidence

• appendices disguised as main decks

• executive summaries that summarise activity, not judgement

• meetings where the decision emerges in the final five minutes

These are not style issues. They are operating signals. They tell you the organisation is making senior leaders work too hard to find the point.

7. The CEO script

Use this with your team:

From now on, I want the answer first. Every board paper, executive email and decision request should begin with the recommendation, the decision required and the evidence. Put the analysis in the appendix. I do not want less rigour. I want the rigour organised around the decision.

That last line matters. This is not a call for shallow communication. It is a call for disciplined communication. Do not remove the analysis, weaken the evidence or dumb down the work. Organise it around the decision.

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Two follow-up prompts

Use these to enforce the standard.

BLUF audit for executive emails

Appendix rule for board decks

The executive takeaway

Communication quality is an operating issue. Bad communication slows decisions, and slow decisions create organisational drag.

If your team writes bottom-up, your executives have to reverse-engineer the answer. If your board decks bury the recommendation, your directors have to excavate the point. If your emails hide the ask, your decisions slow down.

The fix is simple but uncomfortable: put the answer first, then defend it.

That is the Minto discipline. It is the BLUF discipline. It is the Appendix Rule. And it is the difference between communicating work and communicating judgement.

Most people imagine banking AI as a chatbot.

That is the wrong place to look.

The more interesting Westpac story is happening behind the banker: AI drafting, summarising, searching, triaging, surfacing information and supporting lending workflows before a customer ever sees the output.

Westpac has rolled out Microsoft 365 Copilot to 35,000 employees, contractors and service providers after a 15,000-person pilot. It has also been reported to be using Copilot Studio for internal GenAI agent development, while iTnews has reported that Westpac is looking at AI across business banking workflows including documentation, KYC, annual reviews, credit memos and customer communications.

That is real activity.

But it is not yet proof of transformation.

It is proof that AI is moving into the seams of banking work.

Before you read on, where is AI already drafting, summarising, recommending or prioritising inside your organisation before anyone senior sees the output?

That is where the real governance question starts.

Below, I unpack why Westpac is a useful COO/CRO case study, and why “the bank still owns the mistake” may be the most important AI principle in regulated industries.

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Your customer does not want a drill. They want the hole. Or the shelf. Or the deck. Or the repaired hinge. Or the Saturday project that does not collapse into three extra trips, a YouTube rabbit hole and a fight in the garage.

That is why Bunnings’ new AI assistant, Buddy, is more interesting than the usual “retailer launches chatbot” headline. Bunnings says Buddy can help customers find products, check local stock and get advice for their next project; it sits just to the right of the search bar, which is symbolically perfect.

Because Buddy is not just another search feature. It is a test of whether Bunnings can digitise one of the most valuable things in hardware retail: the moment where a half-formed customer problem becomes a confident shopping basket.

Bunnings is refreshingly upfront that Buddy is still imperfect. Its own site says Buddy is powered by AI and “at times won’t get things exactly right,” and that its answers are based on the Bunnings website, the Workshop community and broader world knowledge. That caveat is the story.

The future of AI in retail is not a neat vendor case study with a perfect box on top. It is a live experiment in trust.

Think about your own business. Where has AI already started speaking on behalf of your brand; in customer service, sales, product advice, search, onboarding, support, legal, HR or investor communications? That is the question I would take to the next executive meeting.

Below I unpack why this matters for the CEO, CMO, board and customer, and why hardware retail may be one of the clearest early tests of whether AI can create real commercial advantage rather than just a better FAQ.

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The website sounds polished. The LinkedIn posts sound casual. The sales deck sounds aggressive. The investor update sounds cautious. Each asset may be fine in isolation, but together they create confusion. And confusion reduces trust.

People do not trust corporations. They trust characters.

Characters are consistent. They have a worldview. They speak in a recognisable way. They know what they believe. Most brands do not have that. They have tone guidelines.

That is not the same thing.

Why Brand Voice Breaks

Brand voice usually breaks for one simple reason: too many people are trying to make the company sound acceptable.

Marketing wants energy. Sales wants urgency. Legal wants safety. Leadership wants authority. Social wants personality. By the time everyone has contributed, the voice becomes neutral.

Professional. Approved. Forgettable.

The result is a brand that sounds like it was written by a committee. Which it probably was.

The more people try to protect the brand, the more likely they are to erase it.

If this sounds familiar, send it to the person in your company who says “this doesn’t sound like us” but has never defined what “us” actually means.

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The Archetype Problem

Great brands do not just choose words. They choose a role.

Nike is The Hero. Apple is The Creator. Patagonia is The Explorer.

These brands are not consistent by accident. They understand the character they are playing. That character shapes the language, the enemy, the offer and what they would never say.

Carl Jung’s archetypes are useful because they force a brand to make a choice.

Are you The Sage, The Rebel, The Everyman, The Ruler, The Creator or The Caregiver?

Each archetype carries a different emotional contract with the market. The Sage teaches. The Rebel provokes. The Ruler commands. The Everyman simplifies. The Creator imagines. The Caregiver reassures.

The problem starts when a company tries to be several of them at once.

A brand cannot sound like The Sage on the website, The Jester on social and The Ruler in sales. That creates distrust. Not because the copy is bad, but because the character is unstable.

The Brand Voice Audit

Most companies try to fix voice by editing sentences. That is too late.

The real issue is upstream. You need to diagnose the character before you rewrite the copy.

A useful way to do this is to compare three pieces of live marketing material. Not the brand guidelines. Not the positioning deck. The actual copy customers see.

Start with this prompt.

Brand Archetype Audit Prompt

Why This Works

This exercise reveals something most teams avoid: your brand voice is not what the guidelines say. It is what the market repeatedly hears.

If the market hears confidence in one place, playfulness in another and desperation somewhere else, it does not form a clear impression. It forms doubt.

Consistency is not about sounding the same everywhere. It is about sounding like the same character everywhere.

That distinction matters. A founder can speak differently in a sales email, a keynote and a customer update. But the worldview should remain intact. The posture should remain intact. The underlying character should remain intact.

That is what builds trust.

This is a useful exercise to run with a founder, CMO, Head of Marketing or sales leader. Each function usually thinks the brand sounds inconsistent because of someone else. The audit makes the inconsistency visible.

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Before You Write More Copy

Most companies do not need more content. They need a stronger character.

Without character, every new asset becomes another opportunity to dilute the brand. Another campaign. Another landing page. Another sales sequence. Another slightly different version of who the company claims to be.

The exercises below are designed to solve the deeper problem. Not just what you sound like, but what you stand against and how to scale that voice without rewriting everything yourself.

Subscribers get access to the two follow-up prompts below. They are designed to help you sharpen the brand’s enemy and turn the voice into an operating system your team can actually use.

Most companies skip this step. Then wonder why every piece of copy needs founder intervention.

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The Anti-Persona

Strong brands do not only know who they are for. They know what they are against.

This is where most corporate messaging becomes weak. It tries to be positive about everything: inclusive of every use case, open to every buyer and acceptable to every stakeholder.

That sounds safe. But safe marketing is usually invisible.

To create a memorable brand, you need contrast. You need tension. You need an enemy.

Not a competitor. A competitor is too small.

The enemy should be a status quo belief, behaviour or habit that hurts your customer.

Apple fought conformity. Salesforce fought installed software. Basecamp fought workplace complexity.

The enemy gives the brand its edge. Without one, the voice becomes generic.

Anti-Persona Prompt

The point is not to become aggressive. The point is to become clear.

A brand without an enemy has no tension. And a brand without tension is easy to ignore.

Send this section to the person who keeps asking, “But who are we alienating?” Because the honest answer should be: the wrong customers, the wrong expectations and the wrong category assumptions.

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The Intern Test

The final test of brand voice is not whether the founder can write it. The founder usually can.

The real test is whether someone junior can write in the voice without destroying it.

That requires governance. Not a 40-page brand book. A usable operating system.

Most tone guides fail because they describe the voice in adjectives: bold, clear, human, smart. These words are too vague to be useful. Every company thinks it sounds clear and human.

A useful voice guide shows the difference between acceptable language and owned language. It gives examples. It creates boundaries. It helps the team know when something sounds wrong.

Voice Style Guide Prompt

This is where brand voice becomes scalable. Not because everyone becomes a great writer, but because everyone understands the character.

This is the part to share with anyone managing junior marketers, agencies, freelancers or sales teams. If the voice only works when one person writes it, it is not a system. It is a bottleneck.

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The Final Reality

A brand voice is not a tone. It is a pattern of trust.

The market learns who you are through repetition: repeated posture, repeated language, repeated beliefs, repeated enemies and repeated choices.

If those signals change from channel to channel, the brand becomes harder to trust. Not because buyers consciously analyse it, but because inconsistency creates friction.

A clear voice removes that friction. It tells the market what kind of company they are dealing with. It tells customers what to expect. It tells the team what to say no to.

Most companies sound corporate because they are trying to avoid being disliked. But memorable brands are not built through universal approval. They are built through recognition.

People should be able to read your copy without seeing your logo and know it came from you.

That is the standard.

Not professional. Recognisable.

The Question Every Leadership Team Should Answer

If someone removed your logo from your homepage, sales deck and LinkedIn posts, would anyone know they came from the same company?

If the honest answer is no, you do not have a voice problem. You have a character problem.

Ask this in your next marketing meeting. Then compare the answers from brand, sales, product and leadership. The gap between those answers is where the work is.

CEOs have heard this song before.

XML was going to make systems talk.
BizTalk was going to make integration manageable.
SOA was going to make the enterprise composable.
RPA was going to take the robot out of the human.

Every wave had truth in it.
Every wave also had theatre.

And every wave left behind the same lesson: automating part of the work is not the same as moving the work.

That is the lens CEOs should bring to agentic AI.

Not cynicism.
Not hype.

Pattern recognition.

Because if agentic AI is sold as another way for machines to “talk to machines,” it will disappoint.

If it is understood as a way to move work through a business with less human glue, it becomes much more interesting.

That is the difference.

BizTalk moved messages.
RPA moved clicks.
Agentic AI may move work.

The rest of this article is about the real CEO question: not whether AI agents are impressive, but where they can remove friction from the way your company actually runs.

We will look at why the comparison with RPA and older integration waves matters, where the value is likely to sit, and how a CEO and CTO can talk about agentic AI without getting trapped in vendor fantasy.

The useful question is not:

“Should we deploy agents?”

It is:

That is where the money is.

The enterprise is full of human glue

Most companies do not move slowly because their people are stupid.

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There is an easy version of the PocketOS story.

An AI coding agent went rogue. It ignored instructions. It deleted production data. Then, in a moment almost too perfect for the internet, it “confessed” in writing.

That is the viral version.

It is not the version CEOs and Chief Risk Officers should care about.

The harder truth is this: a business allowed a non-human worker to access authority it did not properly understand, limit, supervise, or recover from.

That is not an AI story first.

It is a governance story.

Or, more plainly: the intern had the keys.

According to Jer Crane’s original post and subsequent reporting by The Register, a Cursor AI agent working on what was supposed to be a staging issue found a Railway credential and used it to delete PocketOS’s production data environment. PocketOS serves rental businesses that rely on its software for reservations, payments, customer records, and day-to-day operations.

For those customers, this was not an abstract AI failure.

It was Saturday morning chaos.

Bookings disappeared. Customer records were missing. Operators had to reconstruct reality from payment histories, emails, calendars, and whatever else they could find.

Railway later told The Register that the data had been restored and that additional safeguards had been added. That is important. It changes the ending of the incident.

It does not change the lesson.

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The Prompt

Act as a Military Strategist (John Boyd OODA Loop).

We are facing a market change/competitor move: [Insert Situation].
We are currently stuck.

Diagnose our OODA Loop:
1. Observe: Do we have real-time data, or are we looking at last month's report?
2. Orient: Are we paralysed by "Analysis Paralysis"? What cultural bias is stopping us from seeing the truth?
3. Decide: Who is the bottlenecks? (Is it a person or a process?).
4. Act: Script the "Minimum Viable Move" we can make *today* to disrupt the competitor's rhythm.

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The Executive Upgrade: Subscribers Only

The 100% Certainty Myth
General Colin Powell had a rule: The 40-70 Rule.
If you have less than 40% of the information, don’t decide.
If you wait for more than 70% of the information, you are too late.
You must train your team to pull the trigger at 70%. The cost of being wrong is usually less than the cost of being slow.

Here are the two Governance Protocols to force speed.

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Follow-Up Prompt 1: The One-Way Door Test

(Use this to stop over-thinking)

Act as a Decision Architect (Jeff Bezos framework).

I am facing a decision: [Insert Decision].
I am hesitating because I want more data.

Classify this decision:
1. Type 1 (One-Way Door): Irreversible. High Consequence. (Take your time).
2. Type 2 (Two-Way Door): Reversible. Low Consequence. (Decide immediately).

If it is Type 2, give me a "Bias for Action" plan to execute it by close of business today.

Follow-Up Prompt 2: The “disagree and Commit” Accelerator

(Use this when the team is gridlocked)

Act as a Chief of Staff.

My team is stuck in the "Orient" phase debating [Topic].
They are waiting for "Consensus."

Draft a directive to the team that:
1. Declares that "Consensus" is not the goal.
2. Assigns a deadline for the decision (e.g., 2 hours).
3. States that if they cannot agree, I will make the decision based on the 70% information we have now, and we will fix it later if we are wrong.