Capability
AI Strategy & Enablement
Most enterprise AI programmes stall between the pilot and the platform. This is the work of choosing which workloads repay automation and putting governance around them before scale makes it costly.
What is actually included
- Assessment of which workflows repay automation, and which do not
- Tooling rollout for development teams, with standards and guardrails
- Governance and data-handling policy that survives an audit
- Enablement material so the capability stays with the team
The gap between the pilot and the platform
Most enterprises are not short of AI pilots. They are short of pilots that survived contact with security review, procurement, and the question of who maintains this. The pattern is consistent: a team demonstrates something genuinely impressive, and then it sits, because nobody established how it would be governed, what data it may touch, or which budget line it lives on.
That is not primarily a technical problem, which is why it tends to fall between the people who could build it and the people who could approve it. Years spent on the approval side of that line make it a familiar gap.
How the work sequences
- Inventory what is already happening, including the shadow usage nobody has written down. There always is some.
- Rank candidate workflows by the boring criteria: volume, repeatability, cost of an error, and whether the output can be checked.
- Draft the policy – data handling, review requirements, approved tooling, what is prohibited outright – in language a reviewer will accept.
- Take one workload all the way to production, so the policy is tested against reality rather than imagined.
- Hand over the standard, the tooling setup, and the enablement material the teams need to keep going.
The most valuable output of this work is often the list of things not to automate. Ruling out six of eight candidates is a better result than a portfolio of eight half-finished ones, and considerably cheaper.