AI Value Isn't Lost in Delivery. It's Lost at Selection.

AI value is rarely lost in delivery. It is lost upstream, in a two-fold failure. First, identification: opportunities surface through brainstorms and hype rather than from the business, so strong candidates are missed and weak ones fill the funnel.

Second, selection: use cases pile up, everything becomes a pilot, and weakly selected initiatives fail downstream (MIT: 95% of GenAI pilots show no measurable P&L return). Value cases fail to convince finance, narratives replace evidence, expected output quality (evals) is never defined, and AI agents raise the stakes: a selection mistake acts directly on operations, customers and compliance.

This talk addresses both failures. It introduces an identification practice grounded in business architecture, surfacing AI automation opportunities by design, not by chance. It then presents an evidence-based Use Case Selection Framework: value ranked and budgeted first, readiness (People, Process, Technology, Data) assessed at a depth aligned to risk, POCs used strictly to validate assumptions, and every decision backed by evidence and clear accountabilities.

Attendees will leave with a structured way to source AI candidates from their business architecture, a method to size assessment effort to risk, criteria for when a POC is justified, and the evidence required for a confident Go/No-Go. A funnel fed by design, and the confidence to move fast on the right use cases.

Join Mario Cantin and Modelware Systems to learn more!