Organizations are pursuing diverse strategies to build AI-ready teams
The conversation around AI is moving quickly, but building an AI-ready workforce is about execution. Right now, organizations are taking different paths depending on where they are in their AI journey and what their business priorities are. One thing is clear: you can’t tackle AI without realignment, of people, skills, and structures.
Some companies are upskilling their existing developers to work with AI tools, like using machine learning models to generate code. Others are reassigning entire teams from repetitive, manual tasks to managing and optimizing AI agents that can handle that work. These agents don’t fully replace people. They need oversight, someone to manage how they operate, improve their performance, and ensure they’re aligned with business outcomes.
But AI’s impact doesn’t stop at immediate workforce changes. We’re also seeing indirect but significant shifts in infrastructure, data management, and security. Companies repatriating data from the public cloud to private environments are now hiring data center operators again, roles many had stopped recruiting for years ago. Increased AI activity is also driving up computational demands and widening the cyber-attack surface. That’s pushed many firms to rethink their entire data and security strategy, from where they store information to how they protect it.
The reality is, there’s no one-size-fits-all model here. You’ll find companies building internal AI expertise, hiring external AI leaders, or going full-speed in both directions, train who you can and hire as fast as you can. No one has a perfect roadmap. But what’s true across the board is that success depends on adaptable people and leadership that’s comfortable making decisions in ambiguity.
For executives, it means you need to prioritize workforce fluidity. Build teams that can flex across functions. Because the roles you need next quarter may not exist today, but your company still has to be ready for them.
AI technologies can enhance IT operational efficiency
If you’re running enterprise IT, you already know inefficiency costs you, fast. A recent Forrester study puts the number between $1.5 million to $3 million in lost productivity for every 1,000 employees, all due to delays from IT-related bottlenecks. That’s a drag on progress. And more importantly, it’s unnecessary.
AI can relieve that pressure by transforming how IT departments handle daily operations. Not all of this is happening yet, it’s early days for many organizations. But the direction is clear. Companies are starting to train AI models to manage support requests, prioritize incident tickets, and resolve routine tasks without needing a human on the other end. These are tasks that eat up resources but don’t move the business forward. Automating them frees up time and energy for the things that do.
This doesn’t mean removing the human element. It means using AI to handle volume so people can be inserted where their judgment matters, on strategy, mission-critical diagnostics, and high-risk decisions. That’s where real value comes from, matching the speed of machines with the insight of skilled professionals.
Right now, most of this work is seen as tactical. But it’s also one of the fastest ways to get ROI from your AI investments. You don’t need a full AI overhaul to start unlocking efficiency. You can run pilots in IT support, gradually scale what works, and track improvements in speed and resolution rates. Once results build confidence, deployment scales itself.
If you’re in the C-suite, adopt a bias toward action. Start with high-friction areas of IT that are slowing your company down. Use AI to move fast on those fronts. Then reallocate your human capital to where intuition, experience, and business knowledge actually move the dial.
The question of AI ownership within organizations remains unresolved
AI is moving faster than most organization charts can adapt. That’s creating a growing question in the enterprise: who actually owns AI? IT? Business units? A new AI division? There’s no consistent answer, and that’s exactly the issue.
In some companies, IT is leading the charge, deploying generative and agentic AI to make existing systems faster, cheaper, and more automated. In others, business units are driving their own AI initiatives, often focused on launching new revenue streams, optimizing customer engagement, or reimagining core products. Both are valid, but they pull from different resources and follow different priorities. This creates tension, especially when tech and business teams move without a unified strategy.
The bigger picture is that AI is a driver of business model transformation. That means IT can’t just be a backend function anymore. It needs a voice in the bigger decisions. It’s the difference between treating AI as a tool and recognizing it as infrastructure that touches everything, from operations, to growth, to risk management.
This governance gap has consequences. Without clarity, you slow execution and increase friction between departments. You also risk duplication of effort, budget confusion, and missed value across the portfolio. So organizations need to define ownership up front. Not to pin it down to a single team, but to set the conditions for alignment between technical execution and business outcomes.
For C-level leaders, this is about enabling the teams that can deliver with it. That means bringing IT and business teams into shared planning cycles, aligning incentives, and making AI experience part of your leadership bench. You don’t need to centralize every initiative, but you do need to centralize the vision.
IT leaders are experiencing increased pressure during rapid AI-driven transformation
Right now, the demands on IT leadership are heavier than they’ve ever been. Many CIOs are reporting rising stress levels and shorter tenures. That’s not surprising, AI is forcing change at a rate most organizations aren’t structured to handle. It’s not just about technology anymore; it’s about transformation, velocity, and constant decision-making under uncertainty.
This pressure isn’t coming from one direction. Digital transformation was already a high-stakes responsibility. Add the complexities of AI, plus macro pressures like geopolitical instability and volatile markets, and what you’ve got is a role with very little margin for error. Leaders are expected to deliver on innovation, handle technical debt, modernize infrastructure, and now, orchestrate AI strategy. All at once.
The biggest shift is that AI isn’t something you can delegate or delay, it’s moving too fast. It’s not a project with an endpoint. It’s a capability that will keep iterating. That makes leadership more intense. CIOs, CTOs, and IT directors are leading in a space where the technology is evolving in real time, and the implications aren’t always obvious yet. That’s not a normal operating environment.
Executives need to take this seriously. Sustaining leadership capacity over time requires investment in the people doing the work, not just the systems. That includes better support systems for CIOs, clearer decision rights, and giving tech leaders a stronger role at the strategic table. The organizations that succeed won’t just be the ones with the smartest AI tools; they’ll be the ones with the strongest leadership stamina to focus, adapt, and keep pushing through unpredictable terrain.
If you’re in the C-suite, prioritize leadership stability. Give your CIOs room to lead, resources to move quickly, and alignment across the board. Otherwise, you’re betting on transformation with no one qualified left to run it.
Key takeaways for leaders
- Build adaptable, AI-ready teams: Leaders should invest in upskilling existing talent while hiring strategically for emerging roles across AI, data, and security. Workforce flexibility is critical as new requirements surface quickly in AI-driven operations.
- Use AI to unlock IT efficiency gains: Automating high-volume IT tasks with AI improves speed and cuts costly delays. Prioritize areas like support ticket triage to free up human talent for high-impact decision-making.
- Clarify AI ownership to reduce friction: Organizations must define AI governance early to align business and IT on strategy, budget, and execution. Clear responsibility prevents wasted effort and accelerates enterprise-wide adoption.
- Support leadership under AI pressure: CIOs and IT leaders face rising stress and shrinking tenures due to transformation demands. Executive teams should reinforce leadership capacity with better resourcing, decision authority, and strategic alignment.
