AI checks AI: who is still accountable?
When AI reviews AI-generated work, accountability depends on people's understanding, time and authority to intervene. What should leaders put in place?
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Insights on organisation, strategy & AI.
If software is faster to build, what are customers paying for?
AI raises new questions about development time, pricing and customer value. What should software service providers reconsider about their offer?
Agent sprawl: do you know what is running?
As teams add AI agents, ownership and access can become unclear. Start by mapping what is running, who is responsible and how to stop it.
What should an AI agent be allowed to do?
Before an AI agent takes action, define its permissions, the decisions that require approval and who can intervene when something goes wrong.
Getting started with AI: choose the problem before the tool
Choose a specific problem, assign responsibility and agree how to judge the result. Give your first AI trial a clear purpose before selecting tools.
AI or conventional automation: what does the task need?
When are fixed rules enough, when can AI help, and when should you combine them? A practical way to assess reporting, analysis and workflows.
Seven decisions before an AI application becomes part of the workflow
Agree on purpose, responsibility, data use and review. Seven decisions to make before an AI application becomes part of everyday work.
Ethical AI: whose interests do your rules protect?
Ethical AI involves choices about interests, boundaries and challenge. Make clear who decides, who is affected and how an outcome can be reviewed.