Q1. Our organisation has a clear reason to use AI in Asset Management, linked to overall strategic objectives.

Q2. Funding and resources are in place for AI initiatives in Asset Management.

Q3. We have prioritised list of AI use cases in Asset Management (e.g., predictive maintenance, work scheduling, spares).

Q4. For the best use cases, we’ve agreed success measures (e.g., reduce downtime by X%, improve schedule compliance by Y%).

Q5. Our asset register and work order history are complete, consistent and reliable (right hierarchy, failure/cause/action coded, minimal free text).

Q6. We have useful condition data (inspection/monitor points) and sensor coverage on critical assets, with data accessible for analysis.

Q7. Our business has selected the AI systems that are approved for use and have made access available to employees.

Q8. We have a standard way to build, test, deploy and monitor AI models or agents (so they don’t silently drift or break).

Q9. We can pull the data we need (CMMS/EAM, historian, inspections) into a single place for modelling and testing, without fragile workarounds.

Q10. If AI recommends an action, we can automatically create/update work in our CMMS/EAM to generate action.

Q11. Our Asset management business processes are mapped with key decision points understood. We know which decisions AI can support or speed up and which we never want AI to touch.

Q12. There is good operational discipline around our Work Management processes.

Q13. We have a published AI Policy and Standards that defines responsible use in the Business and Asset Management.

Q14. Records management and an audit trail for AI decisions exist.

Q15. Asset management leadership understand AI opportunities and risks.

Q16. Training is in place for data/AI literacy for supervisors, maintainers, planners and engineers.