As we head into 2025, CEOs are focused on a clear set of priorities -- AI-enabled growth, dynamic capacity, risk management, and human-machine integration. Yet, many CIOs are still too focused on managing IT infrastructure and not stepping into their rightful role as strategic advisors. To meet the needs of today's CEO, the CIO must transform from a technology manager into a leader who drives digital business transformation. This shift isn't just about adopting AI; it's about aligning technology with the larger business strategy, creating value, and managing the balance between innovation and risk.

Here are the key actions CIOs should take to ensure they're not just managing IT, but actively enabling their organizations to grow, innovate, and transform in 2025 and beyond.

1. Build AI Literacy and Trust Within IT First

The first step in leading AI transformation is starting within your own organization. CIOs should focus on building AI literacy programs within their IT teams, ensuring they have a solid understanding of what AI can do and how it applies to their work. This is where quick wins come into play -- focus on immediate pain points within IT, such as improving operational efficiency or automating repetitive tasks, to deliver fast results. These early wins will create internal champions who can advocate for AI, helping spread the message across the organization.

Ask Yourself: Am I starting with quick, high-impact AI initiatives within my own team that can demonstrate real value? Have I identified the internal champions who will sell the success of these initiatives to their peers?

"AI isn't just a tool -- it's your path to transformation. If you're still managing technology, you're missing the point."

2. Win Hearts and Minds by Making AI Personal and Measurable

To ensure sustained AI adoption across the business, CIOs must focus on making the workday easier for employees. Every AI initiative should have two clear outcomes: personal impact on employees and quantifiable data for leadership. By showing how AI simplifies tasks or enhances productivity for individuals, while simultaneously delivering metrics that prove its impact, CIOs can win over both employees and leadership. This balance avoids the risk of AI feeling like "big brother" and ensures that AI is seen as a value-add, not a threat.

Ask Yourself: Are my AI projects producing measurable business value while also making a positive difference in employees' daily work? Am I balancing these two outcomes to ensure broad adoption and trust?

3. Start with Existing Problems to Drive Dynamic Capacity

When it comes to AI-enabled dynamic capacity, the key is to start where the company's current bottlenecks are. Whether it's production outpacing logistics, supply chain inefficiencies, or gaps in customer service, target the areas where problems already exist. By using AI and automation to solve these issues, CIOs can deliver immediate value that resonates across the business. Once that first problem area is resolved, the ripple effects will spread, allowing you to expand AI adoption gradually, eventually moving the entire operational chain from data-informed to data-driven decision-making.

Ask Yourself: Am I focusing AI efforts on the biggest pain points in the business today? Have I built a feedback loop to expand AI and automation from these problem areas out to the rest of the organization?

4. Keep Human Oversight Until Trust is Earned

The shift from data-informed to fully data-driven decision-making doesn't happen overnight. It requires building trust in the data. Until teams trust the data enough to follow its guidance without hesitation, human oversight is essential. Once you reach the point where the organization consistently relies on data and follows AI's lead without doubts or complaints, you can start to introduce more prescriptive AI models. This gradual shift ensures the transition is smooth and minimizes resistance.

Ask Yourself: Is my team ready to trust AI and data-driven decisions, or do we need more time with human oversight to build confidence? How can I help foster that trust through smaller wins?

5. Collaborate with HR to Lead the Human-Machine Workforce

Integrating AI into the workforce is a delicate balance, and collaborating with HR is critical to success. CIOs must build a strong relationship with HR leaders, focusing on creating AI literacy programs for the broader organization and preparing for a human-machine workforce integration. By aligning early with HR, CIOs can co-lead this transition, ensuring it's done thoughtfully and with employee trust at its core. The focus here should be on building trust first, so that when it's time for transformation, both sides are ready to lead together.

Ask Yourself: Have I built a strong relationship with HR to co-lead AI-driven workforce changes? Am I preparing the organization for this integration before it becomes an imperative?

6. Lay the Foundation with Accurate, Trustworthy Data

For AI and dynamic capacity to succeed, data is king. Moving from a fixed to dynamic capacity model requires accurate, timely, and trustworthy data. One of the first steps in this process is establishing a standardized lexicon of business terms and data definitions across the company. There should be one definition of a sale, a customer, or an employee. With a unified understanding of these core metrics, the organization can then scale AI and automation initiatives with confidence.

Ask Yourself: Is my organization's data consistent and trustworthy? Have we established a common language across the business to ensure that AI initiatives are built on a strong foundation?

7. Balance Innovation with Security from the Start

Security should never be an afterthought. In the rush to innovate and adopt AI, security must be considered before defining or quantifying the value of any project. This means working closely with the CISO from the beginning to ensure security is a core component of every AI and automation effort. By reducing friction between IT and cybersecurity teams and presenting a unified front, CIOs can streamline innovation while ensuring the organization remains protected.

Ask Yourself: Is security baked into my AI and data initiatives from the outset? Am I working closely with the CISO to reduce friction and create a seamless, secure environment for innovation?

8. Scale AI Adoption by Creating an Executive Steering Committee

Once you've gained momentum from smaller wins, it's time to scale. As leaders see the success of early AI initiatives, they'll naturally be more willing to commit to larger projects. At this point, CIOs should create an executive steering committee, comprised of key decision-makers from across the organization. This committee will help prioritize AI initiatives based on cost/benefit analysis and will ensure that future projects have executive buy-in from the start. Keep the group small, focusing on CIO peers and those who can actively contribute.

Ask Yourself: Do I have the right executive steering committee in place to help scale AI initiatives? Am I leveraging the early success of AI projects to build further momentum across the leadership team?

Conclusion

The role of the CIO is evolving, and CEOs are looking for leaders who can drive AI transformation, build dynamic capacity, and manage the shift toward a human-machine workforce. By focusing on small, personal wins, building trust in data, and collaborating closely with HR and cybersecurity, CIOs can lead their organizations through these complex transformations with confidence.

If you're unsure how to take these steps or need guidance on how to align your AI initiatives with CEO priorities, my team and I are here to help. We have the experience to guide you through the process, ensuring your organization is set up for success and that you're positioned as a trusted advisor at the executive table.