AI & Automation

What you capture in the moment is messy by nature. What you do with it afterwards is where the real work begins. There is a moment, usually late in the day, when you open your notes and realise they are no longer helping you. At the time, they felt useful. You captured everything. Decisions, ideas, […]

AI output rarely fails because it is obviously wrong. It fails because it is plausibly wrong—structured, confident, and easy to accept without scrutiny. In practice, this means AI does not reduce the need for thinking—it shifts where thinking is required, from creation to validation. That shift changes the nature of consulting work. The risk is

Consultant admin rarely looks expensive while it is happening. It is a follow-up email after a meeting, a reminder to chase a review, a task update, a project setup step, a folder to create, a note to clean up, a status summary to assemble. None of these actions feels large on its own. Together, they

Discovery work is often slowed by language before it is slowed by logic. Stakeholders describe the same problem in different terms. Workshops generate useful ideas, but they arrive in overlapping fragments. Interview notes contain patterns, though those patterns are buried inside too much context to use quickly. That is why AI can be helpful in

Meeting notes seem simple until they are done badly. Then the project starts paying for them. Decisions are remembered differently, actions lose owners, and follow-up becomes heavier because the meeting did not leave behind a stable account of what actually happened. That is where AI can help, but not in the way people sometimes assume.