Following the time AI saves
Teams reported that AI saved them time. Nobody could say where that time went. I treated adoption as a question about how the work is organised, rather than a tool rollout.
Advisory
Technology and AI value assessment
Decide whether to scale, change or stop an AI investment, and identify what evidence would change that decision.
For: Technology and engineering leaders considering AI adoption or further investment.
In practice
What the work includes
Assess what AI adoption changes in the work, where costs sit and which benefits the available evidence supports.
- Measurement boundaries and a baseline
- An account of costs, constraints and benefit assumptions
- Open questions and possible next steps
Responsibilities during adoption.
Alongside the technology, examine who owns the work, who can authorise consequential actions and where verification or correction effort lands. These responsibilities shape whether a local productivity gain becomes useful organisational capability.
From AI productivity to economic valueCarry the decision into practice.
Where useful, the work continues into implementation: translate an agreed change into everyday responsibilities and practices, and review whether the intended behaviour and benefits follow.
- An implementation and adoption plan
- A regular review of progress and unresolved decisions
- A review of benefits, observations and remaining questions
Agreeing the work
We review the question and available evidence with the sponsor, identify gaps and agree how findings will be discussed. Duration, data access and client participation are agreed during scoping.
Describe the decision you’re facing.
A few lines are enough: what’s being decided, by whom, and by when.