The ROI of Agentic Futurization on IBM i

For most IBM i teams, the constraint on modernization was never ambition—it was capacity. The backlog of RPG and COBOL work is large, the pool of developers who understand it is shrinking, and every change to a system that runs the business carries real risk. Traditional tooling helped at the margins. Agentic AI moves the constraint itself.

From suggestions to finished, verified work

Coding assistants generate drafts a developer still has to read, run, test, and correct. On a decades-old system, that review is the expensive part. An agentic approach closes the loop: it executes builds, runs tests, detects failures, and retries until the work meets a concrete definition of “done”—so a person reviews a verified result, not a first draft.

Where the return actually comes from

The savings are not evenly distributed. They concentrate in the work that has historically been slowest and riskiest to touch:

• Backlog throughput. Routine conversion, refactoring, and documentation stop waiting on a scarce specialist for every step.
• Risk reduction. Automated testing and validation run on every change, so regressions surface before they reach production.
• Knowledge continuity. As experienced developers retire, the system’s behavior is captured and exercised by the loop rather than living only in people’s heads.
• Delivery speed. Parallel exploration compares multiple solutions at once, converging on the strongest instead of the first that compiles.

Making the case internally

The strongest business cases we see don’t lead with the technology—they lead with a bounded, measurable slice of the backlog, run it through an agentic loop inside the enterprise’s own infrastructure, and compare cycle time and defect rates against the current baseline. From there the pattern repeats.

If you’re scoping that first slice, our team can help you pick one where the economics are clearest.