Ideas
These are the questions rather than the answers — themes that develop across several pieces, and that I keep coming back to.
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If AI makes the finished artefact a weaker signal of capability, what should we assess instead?
As AI makes CVs, applications, presentations and take-home work easier to polish, assessment may need to move closer to the work itself: how someone frames a problem, uses tools, challenges output, makes decisions and stands behind the result.
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What if the most efficient team isn't the best team?
AI may allow smaller teams to produce the same or greater output, but teams also create learning, challenge, resilience, belonging and organisational memory. Those benefits are harder to measure than productivity and easier to lose.
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When software becomes abundant, what becomes scarce?
If AI makes producing software dramatically easier and cheaper, the important organisational constraint may move away from writing the software and towards deciding what should exist, why, and how it becomes useful.
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If models become interchangeable, where does durable organisational AI capability accumulate?
The durable capability around AI may sit less in the model itself and more in the context, permissions, tools, workflows, evaluations, controls and operating knowledge that allow models to work safely and repeatedly inside a real organisation.
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What changes when it becomes cheaper to find out whether an idea works than to prove in advance that it will?
AI can reduce the cost of experimentation, rework and changing direction. That challenges organisational processes designed for a world in which change was expensive and certainty had to be purchased before committing.
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As execution becomes cheaper, does clarity become more valuable?
AI can reduce the cost of implementation and rework without reducing the cost of ambiguity. As machines become better at execution, understanding the problem, defining intent and deciding what good looks like become relatively more important.
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If AI does more of the doing, how do inexperienced people become experienced?
Some work that looks routine or low-value also develops judgement. If AI removes that work, organisations need to replace the learning rather than simply remove the task.
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Does AI need human-like intelligence to become consequential, or is capability without judgement already enough?
A system can become extremely effective at pursuing an objective without understanding why the objective matters, which assumptions were implicit, what collateral effects matter, or when it should stop.
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If building becomes cheap, when does an experiment become something the organisation is prepared to own?
AI makes it easier to build and test software, which should lower the barriers to responsible experimentation. But permission to experiment is not the same as permission to operate: services still need ownership, support, security, resilience and accountability.
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When AI becomes infrastructure, how much of its cost and resilience is determined by architecture rather than procurement?
At enterprise scale, AI consumption is shaped by system design: model calls, context size, routing, retries, agent loops, evaluations and fallbacks all become cost and resilience decisions.
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When does measuring AI adoption stop being useful?
AI usage can be a useful transitional signal while organisations are building capability, but the enduring measures should be what changed: service quality, time, cost, reliability, user experience, learning or some other meaningful outcome.
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Can you have rigorous performance management without first having a rigorous understanding of performance?
Organisations need to address genuinely poor performance, but relative ranking can create an appearance of precision without resolving harder questions about outcomes, context, comparability, collaboration and what good performance actually means.
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Before we automate work, have we understood why the work exists in the first place?
Technology can make a good operating model dramatically better, but it can also make a poor one faster. Friction and workarounds are sometimes evidence about the system that should be understood before they are automated.
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What does expertise become when AI can increasingly perform the execution?
As AI takes on more execution, expertise does not simply disappear. More of its value moves into understanding the problem, setting direction, decomposing work, challenging outputs, recognising failure and exercising judgement about what should happen next.
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If technology makes delivery dramatically faster, can the functions that make delivery safe keep up without becoming the bottleneck?
As AI reduces the cost and time required to build and experiment, security, data, architecture, assurance and other enabling functions need to evolve too. Good governance should make responsible action easier, not depend on friction being slow enough to control behaviour.
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If AI changes what an organisation can feasibly do, should the strategy focus on AI or on the possibilities that have opened up?
The strategic significance of AI is not simply that organisations can now use AI. It is that activities which were previously too expensive, slow, complex or impractical may become feasible. Strategy should examine those changed constraints rather than turn use of the technology itself into the objective.
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Why are organisations better at celebrating what they launch than what they successfully remove?
Decommissioning rarely produces a visible new feature, but it can reduce cyber exposure, failure modes, support burden, architectural complexity and technical debt while making future change easier. Technology leadership needs to recognise removal as value creation, not merely cleanup.