Principles
How to separate value from hype, validate the need before development, and reuse existing AI products.
AIOM defines one path from business need to adoption and validated impact: who makes decisions, which checks an initiative passes, and which artifacts are required at each stage.
Open access · practical templates · roles · processes · decision gates
Not a strategy slide deck, but working rules for selecting, delivering, adopting, and scaling AI initiatives.
How to separate value from hype, validate the need before development, and reuse existing AI products.
Responsibilities across business, the AI function, PMO, product owners, security, architecture, data, and IT.
How an initiative moves through intake, assessment, delivery, adoption, and impact validation without unnecessary bureaucracy.
The cards, criteria, templates, and instructions that keep decisions from depending on verbal agreements.
Every stage ends with a clear decision: continue, return for clarification, change route, or stop.
Capture the business need, initiator, impact owner, users, and an initial impact hypothesis.
Check value, feasibility, data, risks, overlaps, and the readiness of the business process for change.
Select the product route, plan the work, remove blockers, and prepare real use in the target process.
Compare plan and actuals, record the data source, and decide whether to scale, revise, or stop.
What is being launched, where risks sit, who owns outcomes, and which initiatives change business metrics.
Bring ideas, pilots, and products into a shared system with clear routes and a management rhythm.
Synchronize the AI initiative funnel with the corporate portfolio, budgets, resources, and reporting.
Security, architecture, data, IT, risk, and finance work through known criteria rather than urgent exceptions.
Start with an audit of the current portfolio and adoption process to identify which AIOM elements should come first.