For an AI-driven, real-time fraud detection and risk management system in ahighly regulated financial environment, PMI-style guidance on AI governance stresses that the project must have access toappropriate, specialized expertisefrom the outset. This includes knowledge of AI methods, MLOps, financial risk management, compliance, data privacy laws, and sector-specific regulations (e.g., KYC/AML, transaction monitoring standards). When the project manager identifies a skills gap in the current team, the recommended approach is tobridge that gap promptlyrather than delaying or proceeding underqualified.
Option D—engage consultants to fill the expertise gap—aligns with this principle. External experts can provide immediate, targeted knowledge on regulatory constraints, model risk management, explainability requirements, and auditability expectations, all of which are critical for AI in financial institutions. Option A (delaying until internal expertise is developed) can significantly slow strategic initiatives and may still not provide the depth needed. Option B (proceed until expertise is needed) exposes the project to early missteps that are costly to correct. Option C (budget for consultant AI training) misaligns priorities; the immediate issue isusingexpertise, not training external parties.
Thus, the project manager shouldengage consultants to fill the expertise gapand ensure the AI system is compliant, robust, and responsibly implemented.
