Insights
Notes from engagements, written for the people who have to make the decision rather than the people who have to approve the slide. Roughly one piece a month.
Using AI to draft one email or summarise one call saves minutes. The larger gains come from redesigning the whole chain of work, so the output of one step becomes the input to the next without someone copying and pasting in between.
Read moreThe safest first AI project is an internal one: invoices, reconciliations, policy checks. It is also the right place to settle which tools are approved, what data can go where, and who answers when something goes wrong.
Read moreToken prices keep falling and AI bills keep rising. Agents do far more work per task than a chatbot, and most budgets were built for the chatbot. Here is how to price an agent before it ships.
Read moreMost AI errors that reach a client were visible to someone first. The fix is a review layer designed like any other control: what gets checked, by whom, against what, and what the AI may do without asking.
Read moreThe failure almost never happens in the model. It happens at the handover, when a pilot owned by innovation has to become a system owned by operations — and nobody funded that transition.
Read moreAccuracy on a golden set tells you almost nothing about an agent that takes actions. Here are the six layers we instrument on every build, and the thresholds we hold before a release goes live.
Read moreThree questions decide it: does this capability differentiate you, does the data sit inside your walls, and will the vendor market commoditise it within eighteen months? Everything else is noise.
Read moreEvery organisation has a few people who adopted AI on their own. The hard part is everyone else. Company-wide adoption depends on role-specific training, clear rules, and measuring use rather than licences.
Read moreThirty minutes with a principal. Bring the workflow you think is broken. You will know by the end of the call whether it is worth doing.
