Reflection
For people deciding what AI should do in their organisation. Reflections and working materials with sources you can follow.
What does a second AI model actually check?
Two models agree. Which errors still survive? A study and a fictional calculation help leaders assess an additional AI review step before buying it.
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Which usage targets can AI surveys justify?
Before a survey becomes an organisational instruction, examine its population, outcome and inference. With BCG, Gallup and a fictional adoption-rate calculation.
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Who reviews the extra work that AI prepares?
More prepared work requires available attention. How leadership teams can assess review effort, exceptions and backlogs before expanding AI use.
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Who is missing from your successful AI pilot?
Early volunteers help a team learn. Wider adoption also depends on who received an invitation, could actually participate and remained visible in the results.
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What another hospital needs from an AI incident report
Share AI incidents so another hospital can investigate: describe relevant conditions, retain uncertainty and keep report counts distinct from event rates.
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Which assumptions hold up your AI strategy?
Turn an AI opportunity into assumptions you can examine: separate benefit, purchasing and revenue, then justify the next resource commitment.
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What should a negative result teach the research team?
When AI supplies many hypotheses, informative laboratory work becomes scarce. A proposal for research leaders to distinguish explanations and keep negative findings useful for later selection.
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What remains when an AI feature is copied?
An AI company can face feature imitation while accumulating useful capabilities. Examine which demonstrable customer benefits the next deployment can inherit from the work already done.
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Patient questions between visits need a responsible recipient
An AI companion creates a service commitment between appointments. A proposal for hospital leaders to follow open questions through acceptance, action and resolution.
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Who owns the outcome when AI work crosses departments?
A fictional procurement case shows how AI can shift work between teams. Outcome ownership needs a shared definition of success and clear decision rights.
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What an AI benchmark can tell a hospital
Technical capability, collaboration and patient outcomes answer different questions. Eight supplier questions help hospitals connect benchmark evidence to the intended use.
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AI saves ten minutes. What happens next?
How less drafting time becomes a real benefit: a complete worked example including review, rework, abandoned attempts and the decision about where the time saved should go.
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AI and early careers: how leaders can read conflicting employment evidence
US payroll data, a Danish study and an international task index answer different questions. A guide to interpreting them when making workforce decisions under uncertainty.
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What can we still do after AI has helped?
Two education studies show how strongly the effects of AI depend on the learning environment. They raise practical questions for clinical education and leadership development.
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When AI shares expertise: what should experienced professionals do?
A large customer-support study raises a leadership question: how can organisations share expertise while continuing to develop the capacity for new decisions?
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The responsibility behind an AI draft
An AI draft needs inspectable evidence and a clear handover of responsibility. A fully fictional administrative workflow shows who approves each claim and the action it may support.
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An AI-designed antibody binds: which decision comes next?
What a binding result means for drug development, and how pharmaceutical companies can select the next experiments by the decisions they inform.
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A robot’s capacity includes the people who support it
How organisations can include human assistance, interruptions and changing environments when planning the use of physical AI.
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AI electricity use: assess requests, workflows and infrastructure separately
How leaders can interpret AI electricity figures and turn individual requests into an accountable view of a complete workflow.
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When experienced professionals hesitate over AI: what remains unresolved?
How leaders can turn reservations about AI into concrete questions without inferring motives from experience, age or a refusal.
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AI makes the task easier. How should collaboration change?
A reflection on Benedict Evans’s conversation: what individual results tell us about team composition, and how a new way of working can be examined.
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What an AI disclosure changes about a medical answer
Identical answers can receive different evaluations depending on their attributed source. What research shows about AI disclosure, clinical review and clear explanations.
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Ambient documentation: what remains after working hours?
What clinical studies show about note-writing time, exhaustion and after-hours work, and which measurements hospital medical leaders need for a local pilot.
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