Scaling AI spend without a checklist accumulates silent risk. Responsible adoption needs scored review across use-case fit, data readiness, model risk, and operating ownership. Honesty in scoring beats optimism in slides.
Use-case fit asks whether the decision is frequent, valuable, and tolerant of defined error rates. Vague ambitions fail this gate for good reason. Clarity here saves months of unfocused build.
Data readiness covers access, quality, lineage, and privacy constraints. If you cannot explain where training and inference data come from, you are not ready. Auditors will ask anyway.
Model risk includes evaluation design, drift monitoring, and blast radius if the system fails. Treat failure modes as design inputs. Hope is not a control.
Operating ownership names who can change prompts, retrain, approve releases, and answer regulators. Committees without names do not page anyone at 2 a.m. Ownership is a launch requirement.
A low score is a remediation plan, not a permanent veto. Sequence fixes before production traffic. Premature scale turns known gaps into customer incidents.
Security review should include prompt injection, secrets exposure, and retention. These issues appear as soon as systems leave the lab. Late discovery is schedule risk and brand risk.
Change management belongs on the checklist. Training, incentives, and process redesign determine adoption. Accurate models that nobody uses still fail.
Portfolio governance prevents zombie pilots. Revisit scores quarterly and retire work that never clears the bar. Attention is a scarce resource.
Ethics Code uses this structure with clients to keep AI portfolios fundable and defensible. The checklist creates a shared language across risk, data, and engineering. Shared language speeds decisions.