Agility is the new IT currency: A roadmap for skills, readiness and innovation

In an era of constant technological change, agility is more than a buzzword; it is the single most critical characteristic of a high-performing IT department. While C-suite leaders look to technology for a competitive edge, many CIOs find themselves wrestling with a fundamental challenge: Innovation is only as strong as a team’s ability to adapt. The most ambitious transformation roadmaps, from AI adoption to cloud migration, will inevitably stall if the workforce’s skills have not kept pace with the technology. This places a new mandate on the CIO, one focused less on managing technology and more on cultivating a culture of continuous learning.

CIOs need to think and act like chief learning officers, treating skill development as a core strategic function rather than solely an HR responsibility.

The urgency of this shift is clear in the data. The World Economic Forum’s Future of Jobs Report continues to list digital skills, cloud know-how and AI literacy among the fastest-growing capabilities. Meanwhile, research from CompTIA shows that nearly three-quarters of CIOs see skills alignment as the top barrier to realizing the value of their technology investments. This creates a dangerous gap between ambition and execution.

In its 2026 Global CEO Survey, PwC found that CEOs’ top concern is whether they are “transforming fast enough to keep up with technology, including AI”. Yet other PwC research on workforce hopes and fears reveals that only a small fraction of workers use generative AI daily. The pace of innovation is dramatically outstripping workforce readiness, creating an urgent mandate for CIOs to become agents of change.

From my perspective, AI upskilling must be treated as a strategic operating system for the entire IT department. Competitive advantage comes from a deep, holistic understanding of where AI fits, what business problems it solves and how humans and systems can work together effectively. It cannot be an afterthought or a hopeful assumption.

You can’t steer without knowing your starting point

To build an effective upskilling program, you must first understand your current capabilities. I advise CIOs to begin by mapping their existing IT, data and AI skills landscape to identify strengths and, more importantly, to expose blind spots or gaps before they become business risks. This requires a structured skills-mapping process that inventories both core technical skills and adaptive work behaviors. The former includes essentials like cloud fluency, modern software engineering, cybersecurity and data science literacy. The latter is about nurturing the human element and the skills that enable teams to thrive amidst change.

Using established competency frameworks, such as the NIST NICE Framework for cybersecurity roles, can provide a standardized language and structure to this process. These frameworks help create consistent job descriptions and clear learning pathways. This assessment should go beyond just listing skills. CIOs should understand how teams actually apply those capabilities in real delivery environments. Regular reassessment, ideally semi-annual, ensures your skills map stays current as technology evolves and business priorities shift, preventing your upskilling program from becoming misaligned.

Build a continuous learning system, not just a training program

The most effective CIOs I know treat learning like a living program, one designed to evolve as technology, roles and business priorities change. This means moving beyond sporadic, one-time training initiatives to build an ongoing capability-building system. Key elements include role-based, modular learning paths. For a cloud engineer, this might mean a path focused on advanced container orchestration and AI-powered observability tools. For a project manager, the path might focus on agile methodologies for running AI projects and data-driven reporting.

It is also critical to create safe environments for experimentation, such as internal sandboxes or pilot programs, where teams can apply new AI skills without operational risk. This fosters a culture where failure is seen as a valuable learning opportunity, not a mistake to be hidden. Furthermore, encouraging peer-led learning through internal workshops, hackathons and formal mentorship programs can accelerate skill transfer and break down the silos that so often hinder progress.

This is crucial because adoption alone does not guarantee success. For instance, new research from the Project Management Institute demonstrates this very point. In our Gen AI and Agility report, we surveyed 2,000 project professionals who use both agile practices and GenAI. We found that while adoption is growing rapidly, the actual value they realize varies widely depending on how the technology is applied and whether agile values are genuinely practiced. Simply giving teams new tools is not enough.

Governance also plays a critical role here, ensuring that learning investments stay connected to business outcomes. In practice, this could mean a small, cross-functional council that meets quarterly to review learning metrics, assess alignment with new business goals and make decisions on retiring old training modules and commissioning new ones. This keeps the program dynamic and prevents it from becoming obsolete.

Embedding this practice into your IT culture is what makes it stick. CIOs can use several tactics to weave continuous learning into the fabric of their departments. Link skill updates to project retrospectives. Tie career progressions and compensation to skills mastery in core areas like AI literacy and data integrity. I’m also a huge advocate for holding “innovation days” where teams can explore new AI tools and features, building confidence with the very technologies the organization is already investing in. Without this focus on adoption, even the best technology is wasted. A 2025 report on digital adoption from WalkMe found that enterprises wasted millions on underused tech last year alone because adoption was an afterthought.

Avoid the common detours on your upskilling roadmap

As you travel this path, be mindful of common pitfalls that can easily derail your efforts. One of the most frequent pitfalls I see is chasing a single trend, like GenAI, at the expense of foundational IT skills. I’ve seen organizations invest heavily in a single large language model API for all employees while their core network infrastructure remains outdated and vulnerable, creating a lopsided and fragile capability. Another pitfall is treating training and upskilling as a “one-and-done” event. Without continuous reinforcement and opportunities for real-world application, the natural “forgetting curve” takes over and knowledge quickly fades. Finally, a failure to apply governance leads to a “wild west” scenario. This results in one department becoming highly proficient in a specific AI tool that is incompatible with the rest of the enterprise, creating new, more complex silos instead of breaking them down. Upskilling for the AI era demands balance; you must build depth in core disciplines alongside adaptability for new technologies.

This AI era requires CIOs to be cultivators of talent, not just managers of technology. Our role is to model and encourage adaptability, continuous learning and disciplined experimentation across our entire IT workforce. To be truly agile, our teams must be empowered with the skills and the confidence to match their ambition. The time has come to move from a model of reactive training to one of intentional, strategic capability-building that becomes part of your organization’s very DNA. By leading this journey, you can ensure your teams and your organization are ready to meet the future with confidence.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?