IBM i AI Adoption Roadmap
A practical, phased roadmap for an IBM i shop adopting AI: pilot, integrate, and scale, written for a CIO or IT manager audience.
AI adoption on IBM i works best as a phased, deliberately narrow process, not a platform-wide initiative launched all at once. This roadmap gives a CIO or IT manager a practical sequence: pilot, integrate, scale.
Phase 1: Pilot
Pick one narrow use case
A single modernization pilot with IBM Bob, or a single agent-querying pilot using the IBM i MCP Server against data you already trust. Not both, and not a platform-wide rollout.
Confirm readiness first
Run through our AI project readiness checklist before starting ... data quality and staff bandwidth are the most common pilot blockers, not the AI tooling itself.
Define success upfront
Set a measurable target (time saved on conversion, model accuracy against a known holdout period) before you start, not after.
Phase 2: Integrate
Once the pilot proves out, fold it into normal workflow rather than leaving it as a side project. A modernization pilot should become part of your regular DevOps and code review process. An agent-querying pilot built on the IBM i MCP Server should be scoped, monitored, and governed like any other integration touching production Db2 for i data.
Phase 3: Scale
Expand to additional use cases only after the first one is genuinely embedded in daily operations, not just technically working. Scaling too early, before a use case has really proven itself in ongoing use, is the most common way an AI initiative loses momentum and budget support.
Pair this roadmap with our AI project readiness checklist before starting Phase 1, and our AI code assistants comparison if your first pilot is modernization-focused.