Adopting AI at scale is not merely a technology project — it requires a step-change in governance, compliance and risk management. Below are five concrete shifts senior leaders must drive now to preserve trust, enable innovation and control downside risk.

1. Move from project-centric controls to lifecycle governance

AI systems are living products: models change, data drifts, and third-party models evolve. Governance should therefore manage AI across the full lifecycle — design, data, development, deployment, monitoring and decommissioning — with continuous validation and automated monitoring rather than one-off approval gates. See NIST’s AI Risk Management Framework for lifecycle approaches and playbook guidance.

2. Create multidisciplinary AI assurance and clear accountability

Assign explicit accountability (senior owner + independent assurance) and stand up cross-functional AI governance bodies (risk, legal, privacy, security, product, compliance). This reduces “responsibility gaps” where failures cross functional boundaries. International standards and management system guidance (ISO/IEC 42001) make multidisciplinary assurance central to trustworthy AI programs.

3. Adopt a tiered, risk-based approach to oversight

Not all AI systems carry equal risk. Use a tiered risk taxonomy (low → high) to calibrate controls, testing, and documentation (e.g., transparency requirements, human oversight, red-team testing). Governments and multilateral initiatives are converging on risk-based regimes; aligning internal classification with these frameworks reduces compliance friction and regulatory surprises.

4. Operationalize third-party and supply-chain risk management

Large organizations will rely heavily on vendors, models and data providers. Practical controls include standardized vendor questionnaires, contractual SLAs for model updates/patching, evidence of data provenance, and periodic third-party audits. These steps should be part of procurement, not an afterthought. Industry research notes senior leaders are already formalizing governance teams and vendor oversight as a top priority.

5. Couple governance change with structured change management and capability building

Governance failures often stem from people and culture, not just policy. Pair technical controls with a formal change program that addresses skills, incentives, and operating rhythms — using ADKAR/Prosci or a similar model to move individual and organizational behaviour. Invest in role-based training (model risk managers, AI auditors) and revise performance metrics to reward compliant innovation.

Practical first steps for leaders

• Map your inventory of AI systems and classify by risk within 90 days.
• Establish an AI governance council with a clear escalation path to the board.
• Deploy continuous monitoring for high-risk systems and vendor SLAs for external models.
• Launch a targeted change program to close skill gaps in model risk and compliance.

Sources & quick links

• Prosci ADKAR (change management model). Prosci
• NIST AI Risk Management Framework (AI RMF). NIST Publications
• ISO/IEC 42001 — AI management systems. ISO
• OECD AI Principles. OECD AI
• EU Artificial Intelligence Act (summary & implications). artificialintelligenceact.eu
• McKinsey — State of AI / enterprise governance research. McKinsey & Company