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IBM i AI Adoption Roadmap: A Practical Plan From Pilot to Production

Updated August 10, 2026

The IBM i shops that get real value from AI adoption tend to share one trait: they start narrow. The ones that struggle tend to share a different trait: they try to adopt 'AI' as a broad platform initiative before proving out a single concrete use case. This roadmap lays out a phased, pilot-first plan.

Phase 1: Pilot

Pick one narrow use case

A single modernization pilot (a handful of fixed-format RPG programs converted with IBM Bob) or a single agent-querying pilot (using the IBM i MCP Server against data you already trust).

Confirm data and staff readiness first

Use our readiness checklist before starting ... the pilot's biggest risk is usually data quality or staff bandwidth, not the AI tooling itself.

Define what 'success' means upfront

A measurable outcome (time saved on conversion, prediction accuracy against a known holdout period) rather than a vague sense that the pilot 'went well.'

Phase 2: Integrate

Once a pilot proves out, integrate it into normal development or operational workflow rather than treating it as a side project. For a modernization pilot, that means folding AI-assisted conversion into your regular DevOps and code review process. For an agent-querying pilot built on the IBM i MCP Server, that means scoping, monitoring, and governing it like any other integration touching production Db2 for i data.

Phase 3: Scale

Only after a pilot has been integrated and proven in ongoing use should a shop expand to additional use cases or broader rollout. Scaling too early, before the first use case is genuinely embedded, is the most common way an AI initiative loses momentum.

This roadmap is intentionally general. Pair it with our AI project readiness checklist for the specific technical and organizational items to confirm before starting Phase 1.

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