AI projects are most successful when they begin with a useful business question, not a technology demonstration. For an OpenEdge team, that question might be how to summarize service histories, help employees find answers across operational documents, identify unusual transactions or give developers faster access to relevant application knowledge.
Start by mapping the information an AI experience would need. Separate structured application data from manuals, policies, support notes and other unstructured content. Then decide which sources are authoritative, how frequently they change and who is permitted to see them. An assistant that retrieves the right answer for the wrong user is still a security failure.
Keep the first use case narrow. Choose a workflow with clear inputs, reviewable outputs and a human decision point. A retrieval-based assistant for internal support teams is often easier to evaluate than an autonomous process that updates production records. Define success using measures such as time saved, answer accuracy, escalation rate and user confidence.
Architecture matters too. Avoid copying sensitive application data into unmanaged tools. Use governed integration points, preserve existing authentication and authorization rules, and log the context used to produce important responses. When an AI result influences a business process, users should be able to understand its source and correct it.
Treat evaluation as an ongoing practice. Test with realistic questions, incomplete records, conflicting documents and requests that should be refused. Invite subject-matter experts to score results and capture recurring failure patterns.
OpenEdge applications often contain years of valuable business logic and trusted data. AI readiness is not about replacing that foundation. It is about connecting new experiences to it carefully, proving value in stages and maintaining the reliability that made the application important in the first place.
