AI for IBM i Buyer's Guide
A practical buyer's guide to evaluating AI tools for IBM i environments without taking on unnecessary operational risk.
Start with bounded, internal use cases
AI for IBM i is still an emerging category, and the safest entry points are bounded internal workflows: documentation support, internal search, support ticket triage, or reporting summarization. Buyers should resist vendor pitches that lead with ambitious, production-facing automation before a lower-risk use case has proven value internally.
Settle data governance before evaluating vendors
Which data an AI tool can access matters more than which model powers it. Buyers should decide upfront what data sources are appropriate, what approval process is required, and what systems of record must remain authoritative regardless of what the AI tool suggests.
Vendors should be able to answer governance questions clearly, not just describe model capabilities.
- List data sources that are approved for AI-assisted workflows
- Define what always requires human review before action is taken
- Confirm which systems of record cannot be altered by AI output directly
Judge value by friction reduced, not novelty
The strongest AI use cases for IBM i teams increase clarity, speed, or documentation quality without weakening control over production systems. Buyers should evaluate pilots by whether they measurably reduce friction for a real team, not by how impressive a demo looks in isolation.