Decide What AI Success Means Before You Launch

If success is defined after launch, the team can choose whichever metric looks best. A pre-launch scorecard prevents activity, novelty, or model accuracy from substituting for business value. This decision connects to Build an AI Strategy That Starts With Business Value and Turn Company Priorities Into an AI Opportunity Portfolio, which provide the strategic direction and portfolio context. Use this four-part leadership framework Business impact — Name the outcome, baseline, target, and measurement method. Quality — Define correctness, completeness, correction rate, and unacceptable results. Adoption and operations — Measure eligible users, repeat use, completion, exceptions, latency, and recovery. Risk and trust — Track overrides, complaints, incidents, policy breaches, and unresolved controls. A practical decision example An internal knowledge assistant has thresholds for supported answers, material corrections, weekly adoption, response time, review effort, and restricted-data incidents. One strong metric cannot cancel a serious failure elsewhere. This is a hypothetical example; use your own baseline, constraints, and evidence. Evidence, governance, and responsible use Use the NIST AI Risk Management Framework to connect the initiative to governance, context, measurement, and ongoing management. The companion NIST AI RMF Playbook turns those functions into questions leaders can assign and review. The GAO AI Accountability Framework is useful for examining governance, data, performance, and monitoring across the system life cycle. Compare the plan with the OECD AI Principles, particularly transparency, robustness, accountability, and respect for people affected by the system. For generative AI, review the NIST Generative AI Profile and test representative cases using OpenAI’s evaluation guidance. Use the OWASP Top 10 for LLM Applications to discuss application threats before a model can access sensitive information or take actions. Check performance statements against the FTC’s guidance on AI claims, and review information handling with the FTC’s privacy and security resources. For a broader organizational management approach, study the ISO/IEC 42001 overview. Take this to the next leadership meeting For each metric, record target, minimum acceptable result, owner, source, review frequency, and action if missed. End with a scheduled stop, fix, repeat, or scale decision. Record the owner, evidence source, decision date, and what would cause the company to stop. The goal is not to make the document look complete. The goal is to make the next decision explicit, measurable, and accountable. Continue with Who Owns AI? A Practical Operating Model for Growing Companies. Use Run a Quarterly AI Portfolio Review: Keep, Fix, Scale, or Stop to revisit the decision with current evidence.