AI adoption is accelerating, but scaling remains a major challenge. While over two-thirds of companies use AI in some capacity, fewer than one-third have succeeded in scaling it effectively. McKinsey’s latest survey reveals that this gap stems from strategy misalignment, operational complexity, and a lack of leadership in driving AI at scale.

This presents a significant opportunity for CIOs.

Far beyond technical execution, CIOs have a responsibility to shape AI strategy, establish governance structures, and align AI initiatives with business objectives. Success in AI now requires CIOs to lead not just technology deployment but strategic integration across the enterprise.

Why It Matters: CIOs are uniquely positioned to drive AI success at scale by leading both the creation and execution of a strategic AI vision. The failure to scale AI is often linked to misaligned goals, fragmented data systems, and poor governance, areas where CIOs have direct influence. By establishing a clear AI strategy, building scalable infrastructure, and fostering cross-functional collaboration, technology leaders can turn AI from an experimental tool into a core driver of business value. This is a leadership moment for CIOs to step up and drive measurable impact.

• Strategic Vision and Alignment: CIOs must lead the development of a comprehensive AI strategy that aligns with business objectives. This includes identifying the most valuable AI use cases, defining success metrics, and ensuring that AI initiatives support long-term business goals. A fragmented or reactive approach to AI often limits its impact, making it critical for CIOs to establish a clear, unified strategic direction.

• Governance and Leadership: Effective AI scaling requires structured governance. CIOs should implement oversight frameworks that define roles, responsibilities, and accountability for AI decisions. They need to coordinate between business units to prevent AI silos and ensure that AI tools and insights are used consistently across the organization. Strong governance reduces operational friction and enhances AI-driven decision-making.

• Talent and Capability Building: Scaling AI demands both technical and business expertise. CIOs must create a talent pipeline by recruiting AI specialists, training existing employees, and embedding AI knowledge into the broader organization. Building internal AI capabilities helps ensure that AI initiatives are not dependent on external vendors and that teams have the skills to adapt and scale AI solutions.

• Operational Integration: Embedding AI into business processes requires process redesign and organizational change. CIOs need to lead efforts to restructure workflows, create feedback loops, and ensure that AI insights translate into action. Integration challenges often stem from inconsistent data quality and resistance to change, areas where CIOs with appropriate board and CEO support can drive alignment and adoption.

• Long-Term Value Capture: Sustaining AI success requires continuous improvement. CIOs should establish mechanisms for monitoring AI performance, tracking ROI, and identifying areas for enhancement. Without ongoing refinement, AI projects risk becoming stagnant or misaligned with evolving business needs. CIOs must ensure that AI remains a dynamic, value-generating asset over time.

Go Deeper -> The state of AI: How organizations are rewiring to capture value – McKinsey