Leadership in the Age of AI
AI is not a strategy problem; it is a leadership one. How leaders move organisations from a working pilot to real adoption — and why the bottleneck is rarely the technology.
AI won't automatically improve your strategic decisions. A few conditions decide whether it does.
Generative AI does not automatically sharpen your strategic decisions. A 2025 systematic review finds the effect is conditional, on your data, environment and culture. Build those conditions, and the same tool becomes an advantage rather than confident noise.
AI armed your attackers before it armed your defence. Cyber is now a board problem.
AI has armed attackers faster than defenders: flawless phishing, convincing deepfakes, machine-speed attacks, and a new attack surface from your own AI. The WEF's 2026 cyber survey names AI the top force reshaping the threat. That makes cyber an enterprise risk, not an IT line item.
78% of organisations now use AI. That tells you nothing about whether it works.
AI adoption has hit 78%, and it is being used as a reason to invest. It is the wrong reason. Adoption measures activity, not return, and Stanford's own AI Index admits the payoff is still unproven. Lead on measured value in your own numbers, not on the crowd.
Your finance function is full of AI. Is it deciding any better?
Finance has adopted AI almost everywhere, so using it is no longer the advantage. KPMG's 2026 data shows the separation now comes from trust: whether you can stand behind what the AI decides, and prove it.
Ten strategic tech trends for 2026. You can act on three. How do you choose?
Gartner's 2026 trends list has ten items, each urgent, each with a prediction attached. You cannot act on ten. The useful question is which few gate the rest, and this year the answer is the cluster that lets you trust AI to act on its own.
Your rivals bought the same AI you did. So what actually becomes your advantage?
Your competitors can buy the same AI you can. A systematic review of 1,377 studies finds the durable advantage was never the tool: it is the knowledge your organisation creates with it, and that cannot be bought or copied.
Startups seem to move faster with AI. Should you copy them, or play your own game?
Nimble startups seem to run circles around big companies with AI. A review of 2,670 studies finds the two are not playing the same game: startups win on experimentation, incumbents on scale. The edge is knowing which is yours, and borrowing one move from the other.
Your board's most expensive debt is not on the balance sheet
Every board understands technical debt. The costlier debt is the one no one audits: most boards still run a Phase One operating system, built for incremental growth and a single bottom line, while the world has moved to the commoditisation of intelligence.
Your AI is already inside your risk framework. APRA has just said so
APRA is not writing a new AI rulebook. Its framework is principle-based and technology-agnostic, so the standards you already answer to, CPS 220, CPS 230 and CPS 234, already cover AI. The work is mapping AI onto obligations you already carry.
An AI made the decision. So who is liable when it gets it wrong?
When an AI system declines a loan, prices a policy or flags a claim, the legal duty to the customer does not move to the algorithm or the vendor. Australia's obligations are technology-neutral, and under the Financial Accountability Regime a named person still owns the call.
What happens when your one AI vendor goes down? APRA wants your exit plan
APRA found entities leaning on a single AI provider with untested exit plans, and judged point-in-time assurance unfit for models that drift. CPS 230 already requires tested continuity. Resilience is a substitution you have rehearsed, not a document you filed.
What does an enterprise AI governance policy actually look like?
An enterprise AI governance policy is one short, readable document covering seven parts: acceptable use, approved tools and a route to approve new ones, data handling, human-in-the-loop accountability, risk tiers, monitoring and review cadence, and named ownership.
What does an actual AI strategy document contain?
An AI strategy document is a short, defensible argument about how AI changes the way your business makes money, followed by the six choices that argument forces: an economic thesis, a value map, an operating model, a workflow-redesign plan, a capability plan, and a measurement framework tied to the
We ran an AI pilot and it went nowhere. What did we get wrong?
You ran the pilot, the demo got applause, and six months on there is nothing on the P&L. You are not an outlier; MIT found 95% of AI pilots land the same way. Here is why, and what the 5% who win do differently.
Everyone says have an AI strategy. What does that actually mean in practice?
Having an AI strategy is not owning a document. It means living by three decisions: where AI does and does not touch the business, who owns each bet, and how you will know it worked.
We bought the tools and nobody uses them. How do we drive adoption?
You bought the licences six months ago and almost nobody logs in. The seats are dormant because you bought the easy 10%, the software, and left the 70% that drives AI adoption, the people and process, still in the box. Here is how to close the gap without pushing harder or quietly cancelling.
My CEO wants ROI numbers on AI. How do I measure that?
The board stopped accepting hours saved as proof. Here is how to measure AI ROI in the financial language a CEO and board actually read: payback, NPV, IRR and total cost of ownership, with revenue and margin as the headline, and why most cases stall in the 70% that is people and process.
My board asked for an AI strategy and I don't know where to start
Start with the outcome, not the technology. Name the two or three business results you want this year, attach a pilot to each, give it an owner and a measure. That one page is your strategy.
How to tell a real AI advisor from someone who discovered ChatGPT last month
A real AI advisor makes you more capable of deciding for yourself; a repackager makes you dependent on them. Everything else — the jargon, the demos, the deck — is noise.
How do we govern AI across teams and stop shadow AI?
Shadow AI is not a workforce going rogue; it is a trust and visibility signal. Why approved alternatives, not bans, are what bring AI use back into the light, and the three moves that restore clear governance across your teams.
Why your AI strategy keeps failing on broken hardware
Your AI strategy keeps stalling and you are quietly exhausted before it has even properly started. The research is clear: the bottleneck is no longer the technology. It is the human system running the rollout. An AI leadership mindset begins with upgrading the operator first.
How do I build a 90-day AI risk action plan?
A 90-day AI risk action plan turns the diffuse "I do not know what we are exposed to" anxiety into four time-boxed workstreams a leader can take to the board next quarter: inventory, governance, exposure and ownership.
APRA just put your board's AI literacy on the supervisory agenda
On 30 April 2026 APRA told every bank, insurer and super fund it regulates that AI governance is lagging adoption, and put the board's own AI literacy on the supervisory agenda. A board can delegate the build, but not the understanding.
Why Australia ranks last in AI trust
Australia ranks last in the world for trust in AI — and the reason is not that we don't use it. It's that we use it without trusting, training in, or understanding it. That gap is the real story.
The three questions every board should ask before investing in AI
Before your board approves another AI investment, it should be able to answer three questions — and most boards cannot answer any of them. They are about your readiness, not the technology.
Why most organisations fail at AI adoption
Most organisations fail at AI adoption for a reason that has almost nothing to do with AI. The strategy is sound and the models work — what fails is the human system that has to absorb it.