Operating model
An operating model for enterprise AI readiness
Enterprise AI readiness is often treated as a launch checklist. Approve the tools, publish the policy, train the teams, and hope adoption creates value. A stronger model treats readiness as a recurring operating loop.
Key takeaways
- Readiness is a live operating capability, not a one-off programme milestone.
- The loop should sense, understand, recommend, act, measure, and learn.
- Leaders need both executive signal and team-level drilldown.
Sense what is changing
Connectors, chat, workflows, decisions, work items, AI-tool events, and performance signals show where AI is entering the organisation. These signals are the raw material for readiness.
Turn signals into governed context
Signals become useful when they are linked to people, teams, capabilities, workstreams, tools, outcomes, spend, and risk. This context helps leaders understand not only that AI is being used, but how work is changing.
- People and teams
- Capabilities
- Work and decisions
- Spend and risk
Close the loop with measured learning
Readiness improves when recommendations lead to action, the result is measured and the learning updates future guidance and coaching.