Your AI pilot needs a serious party pooper
Before scaling a successful AI pilot, test whether the business can afford to replicate the expertise that made it work.
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Fundamentals, method, and what we keep learning from working with boards and leadership teams.
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Before scaling a successful AI pilot, test whether the business can afford to replicate the expertise that made it work.
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Give business leaders the authority to govern AI investments, deployment rules and shared capabilities together.
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AI makes it easy to accept a polished answer. Leadership still requires the habit of questioning it and owning the decision.
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Refinery automation offers a useful starting point for AI leadership: examine the work, the authority and the operating conditions.
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The leadership test is concrete: can your executive team choose its three most important AI bets and own the consequences?
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Direct experience with AI gives executives better questions about delegation, controls and investment.
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Cheap content can consume expensive attention. Set a standard for the AI-assisted work your organisation circulates.
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Extend existing decision rights, specialist competence and operating controls to govern AI across its lifecycle.
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Start with a business problem, investigate the constraints and expand investment as the evidence develops.
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Build time, management support and practical learning into the transformation budget from the start.
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Use the deployment problem to choose what executives need to learn, practise and decide.
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Compare AI investment, delay and a smaller commitment on the same commercial terms.
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AI expanded what I can expect from myself. Executive teams need to decide where that extra capacity should go.
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A playful AI video earns attention for a reason. Curiosity deserves a place in how executives learn.
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Five combinations of AI assessment signals and how they change the order of executive action.
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Technology attracts investment before the business problem is clear.
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The AI agenda lacks shared executive direction and active sponsorship.
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Unofficial AI use expands beyond the organization’s visibility and guidance.
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Experiments continue without a credible route into everyday operations.
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AI speeds up individual tasks while the overall process stays unchanged.
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AI activity is visible, but its business value is unclear.
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Slow or unclear approval processes delay useful work and encourage workarounds.
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Middle managers lack the incentives, capacity, or confidence to turn AI ambition into daily work.
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Poor data quality and disconnected systems undermine dependable AI use.
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Teams lack the skills, time, or support to sustain new working habits.
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A guide to the ten AI strategy failure modes, with detailed explanations and practical actions for each.
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A plain-language map of the six rungs: chatbot, copilot, agent, agentic workflow, multi-agent team, and agent-managed team.
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Anything with a measurable outcome can be put in a loop. Why that quietly reframes how executives should think about their own work.
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Set your AI ambition to your organisation's actual readiness. Assess the board, the company, and each discipline before you set the target.
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