What does it mean to lead as a CIO in the age of AI? Simply put, it means balancing the urgency of adoption with the need for measurable impact. The inherent complexity of achieving this balance is highlighted by Accenture research: 36% of organisations have already deployed AI, with 90% of CXOs planning to implement agentic AI within the next three years. Yet only 13% have managed to scale AI in ways that deliver business benefits.
To truly scale AI for impact, CIOs must now reinvent IT, integrating human expertise with trusted, scalable AI systems across the enterprise.
Here’s how:
- Start with rewiring IT from the core
AI is no longer just about efficiency; it’s about creating resilient, adaptive systems that allow enterprises to evolve at speed. In IT, agents are now acting as copilots — accelerating development, refactoring code, and supporting decision-making. Intelligent managed services are handling both routine and complex tasks, from maintaining legacy systems to enabling automation at scale.
These are early steps and opportunities to transform the way IT operates. However, their true potential can only be realised once the IT organisation rewires its technology foundation, rethinking cloud and edge architectures, embedding AI into service delivery, and investing in systems that can self-heal and adapt.
Critically, IT leaders must prioritise data and ensure it’s clean, connected, and governed. This requires building unified data platforms with automated pipelines that enforce quality checks, metadata tagging, and real-time integration across business functions. Without this, AI cannot scale reliably. According to the World Economic Forum, more than 60% of organisations globally identify skills in data, AI, and big data analytics as the top drivers of job growth over the next five years, yet many also flag data quality as a key barrier to scaling impact.
- Prioritise workforce transformation
No reinvention is complete without workforce transformation. Because tools alone will not drive scale or a culture of transformation, people will. AI is already redefining how IT teams work, taking care of routine tasks and making more space for what matters most. But as change accelerates, IT teams are struggling to keep up. Many technology leaders across APAC acknowledge that their training programs are not keeping pace with the speed of technological change.
And it’s no surprise, because the work itself is changing. As generative AI moves from experimentation to enterprise scale, routine tasks are being automated, while new responsibilities such as managing AI outputs, curating training data, and ensuring model integrity are emerging. Job roles are evolving from execution to orchestration, demanding broader systems thinking, cross-functional collaboration, and continuous reinvention of team structures. IT organisations must build AI fluency across the board. This means developing skills in AI engineering, data governance, prompt design, and responsible AI. It also means forming fusion teams that combine IT, business, and data expertise, and fostering a culture of continuous learning.
For CIOs, this moment also marks a leadership shift. Many are stepping into Chief Data and Information Officer (CDIO) roles, bridging enterprise strategy with next-generation technology. Others are overseeing AI adoption and performance across mission-critical operations. And as AI becomes part of the workforce, IT teams will be tasked with managing these systems as new teammates — onboarding, optimising, and ensuring they deliver value. The leadership model is changing to one that combines strategy, technical depth, and human insight.
- Build trust into a leadership objective
As AI systems gain autonomy, trust becomes the defining leadership objective. Risks such as bias, misinformation, lack of explainability, and lapses in security and data privacy are no longer hypothetical — they are operational realities that IT leaders must address directly. For instance, generative AI does not “think”; it predicts, making its responses powerful yet potentially misleading. Detecting and explaining errors requires stronger oversight.
Above all, people’s trust in AI, beyond its technical performance, is essential to driving adoption and realising its full potential. For CIOs, this means going beyond responsible use to embedding trust by design. Digital systems must deliver accuracy, consistency, and explainability while remaining secure and compliant. That requires formal AI governance with clear accountability, structured risk assessments, and ongoing testing using responsible AI tools. As concerns around data privacy, cybersecurity, and model integrity grow, real-time oversight and cross-functional coordination become critical.
The IT function is already among those delivering strong returns from generative AI. A recent Accenture report, Rethinking IT Operating Models for the Modern Enterprise (2025), found that IT ranked as the top business function for value delivered by generative AI use cases. Yet technology alone will not secure the future. The organisations that fuse human potential with trusted systems will be best placed to adapt, deliver, and lead despite disruptions.
