IT leaders make AI skills a top workforce priority

Demand for technology talent remains strong

Tech unemployment fell from 3.5% in April to 3.1% in May, according to the CompTIA Tech Jobs Report. The market added around 6,700 tech jobs in May. The strongest demand included software developers and engineers, systems engineers and architects, tech support specialists, cybersecurity engineers and analysts, and AI engineers.

The longer-term numbers point in the same direction. U.S. Bureau of Labor Statistics projections show that the tech workforce is expected to grow twice as fast as the overall U.S. workforce. Between 2024 and 2034, tech occupations are expected to have an annual replacement rate of 6%, representing about 323,000 workers.

For executives, the constraint  is access to the right skills at the right time. AI adoption increases this pressure because companies need people who understand both established technology operations and newer AI systems. Hiring alone is unlikely to solve that requirement. Companies must decide which capabilities to recruit, which to develop internally, and which to obtain from external partners.

This makes workforce planning a business issue rather than an IT staffing exercise. Software, infrastructure, cybersecurity, data, and AI increasingly support revenue, operational efficiency, risk management, and customer experience. Gaps in these areas can delay technology programs even when capital and executive support are available.

The practical response is to connect workforce plans to the technology roadmap. Organizations should identify roles where shortages would block important projects, determine the skills required over the next several years, and build internal development programs before those gaps become urgent. The labor data supports a clear conclusion: demand for technology capability is structural.

Organizations are prioritizing skills development and increasing investment in AI training

83% of IT leaders and HR professionals say their organizations place a high or moderately high priority on addressing skills concerns, according to CompTIA’s State of the Tech Workforce 2026 report. AI is a major part of that investment. The report found that 62% expect their AI training budgets to increase over the next year.

This spending has a broader purpose than teaching employees how to use individual AI tools. Companies need strong operational technology skills before they can deploy AI, data, and cybersecurity practices at scale. Seth Robinson, VP of Industry Research at CompTIA, described the sequence clearly: “More than ever, business success relies on technology. Our research has shown a desire to build capability in core operational functions, which then allows companies to build advanced practices in AI, data, and cybersecurity.”

That distinction matters for executives. AI training will deliver limited value if employees lack the data, security, and operational skills required to use AI safely and effectively. Training budgets should therefore follow business capability requirements rather than the popularity of a specific technology. The goal is measurable improvement in how people perform work.

Organizations also expect training to affect retention and engagement. CompTIA reports that 83% of surveyed IT leaders and HR professionals expect skills investments to have a high or moderate impact on employee morale and engagement. That creates a second source of value. Employees gain opportunities to develop relevant skills while the business builds capabilities that may be difficult or expensive to acquire through hiring.

Higher budgets, however, do not guarantee stronger capabilities. Executives need to define what employees should be able to do after training and how those skills connect to operating priorities. AI training should be treated as capability development with clear outcomes. Companies that make that distinction will be better positioned to turn training expenditure into practical AI adoption.

Rapid AI development is widening the technology skills gap

AI is changing job requirements faster than many companies can update their workforce. At the same time, employers face a shortage of professionals with the skills needed to deploy and manage AI systems. This combination is pushing IT leaders toward internal training rather than relying only on external hiring.

The skills problem extends beyond specialist AI roles. Organizations need employees who can work with AI tools, prepare and analyze data, understand security risks, automate processes, and apply AI within existing business functions. As these requirements spread across teams, companies must develop AI capability among a much larger share of the workforce.

The main constraint is therefore the speed at which organizations can turn existing employees into effective AI users. Recruiting experienced AI specialists remains important, especially for advanced engineering and security work. But hiring cannot address every capability gap. Internal development gives companies a way to expand AI knowledge while retaining employees who already understand their systems, processes, customers, and regulatory requirements.

There is also a resource allocation problem. According to CompTIA, 48% of IT leaders say AI is crowding out other important needs, including the move toward skills-based hiring. Skills-based hiring evaluates candidates on demonstrated capabilities rather than relying primarily on degrees, job titles, or conventional career histories. It can expand the available talent pool when specific technology skills are scarce.

Executives should avoid treating every workforce problem as an AI problem. AI needs investment, but cybersecurity, data management, software engineering, infrastructure, and broader hiring reform remain critical. A workforce strategy that concentrates too much funding and management attention on AI can create gaps elsewhere.

The stronger approach is to identify capabilities from business requirements first. Leaders can then decide whether each gap should be addressed through recruitment, employee development, automation, or external expertise. That keeps AI investment connected to operating needs rather than allowing it to displace other high-value workforce priorities.

Effective AI upskilling requires more than training courses

Companies already have a clear view of the skills they want to develop. CompTIA reports that IT leaders are targeting AI fundamentals, data analysis, AI threat awareness, automation, data preparation, securing AI systems, building inputs and prompts, and creating AI agents. These areas span basic AI literacy through more advanced technical work.

The challenge is execution. CompTIA identifies training costs, training fatigue, employee turnover, limited executive support, difficulty measuring return on investment, and outdated training material as major obstacles. These problems share an underlying constraint: employees have limited time and attention. Adding training without changing workloads can reduce participation and make sustained learning difficult.

AI also creates a curriculum problem. Tools and practices can change faster than conventional training programs. Content that focuses heavily on one product or interface can lose relevance as technology changes. Organizations need to teach durable capabilities alongside tool-specific skills. These include working with data, assessing AI output, identifying security risks, selecting appropriate use cases, and understanding when human review remains necessary.

Training should also reflect differences between roles. An executive deciding where AI can improve a business process does not need the same depth as an engineer building an AI agent or a security specialist protecting an AI system. Uniform training can waste employee time while failing to provide specialists with enough technical depth.

Measuring results is equally important. Course attendance and completion rates show participation. Leaders should connect training to observable outcomes, such as adoption in approved workflows, reduced process time, stronger security practices, or the ability to perform specific AI-enabled tasks. The appropriate metric will depend on the role and use case.

Executive support ultimately determines whether employees have the time and permission to apply what they learn. Leaders need to define which skills matter, protect time for development, keep curricula current, and establish clear expectations for AI use in daily work. Done well, upskilling becomes part of workforce and operating strategy rather than a separate training initiative.

AI training must become part of daily work

The main test of AI training is not course completion. It is whether employees change how they work. Organizations can spend heavily on training and still see limited value if employees return to established processes once a program ends.

Maruf Ahmed, CEO of IT solutions provider Dexian, identifies this implementation gap as a critical problem. “The gap between ‘I attended the training’ and ‘I’m actually using this differently in my job’ is where companies lose people, and closing it takes more than a single training cycle.”

Dexian’s approach focuses on continued use after initial training. Ahmed recommends embedding AI into employees’ workflows and pairing them with colleagues who are further ahead in using the technology. This gives employees a practical reason to apply new skills and access to support when they encounter problems.

For executives, the key constraint is the design of work. Employees already have defined responsibilities, deadlines, and performance targets. Adding AI training without giving them time or approved use cases can make learning another demand on their workload. Leaders need to specify where AI should be used, what employees are allowed to do with it, and where human review is required.

Peer support can also accelerate adoption. Employees with more AI experience can help colleagues select appropriate tools, improve prompts and inputs, identify unreliable outputs, and apply company policies. This support should complement formal controls for security, data protection, and responsible AI use rather than replace them.

Measurement should focus on behavior and business results. Training attendance is an input. More useful indicators include how often approved AI tools are used for relevant tasks, whether processes become faster or more effective, whether employees can complete new AI-enabled work, and whether security and governance requirements are followed.

The objective is sustained capability. Initial instruction gives employees knowledge, but repeated use develops practical competence. Companies that integrate training, workflow design, peer support, and governance have a stronger path from AI investment to measurable operating value.

Key takeaways for leaders

• Plan for sustained tech talent demand: Tech unemployment fell to 3.1%, while long-term demand continues to grow. Leaders should link hiring and workforce development directly to technology roadmaps and critical skill gaps.
• Treat AI training as capability investment: 62% of IT leaders and HR professionals expect AI training budgets to rise. Tie that spending to specific business outcomes and strengthen core data, security, and operational skills alongside AI.
• Keep AI from crowding out other priorities: 48% of IT leaders say AI is displacing other important needs. Balance AI investment with cybersecurity, engineering, data capabilities, and skills-based hiring.
• Design training around practical outcomes: Cost, outdated content, limited executive support, and employee fatigue can weaken upskilling programs. Define role-specific skills, protect learning time, and measure changes in performance rather than course completion.
• Embed AI skills into daily work: Training creates value when employees consistently apply what they learn. Integrate approved AI tools into workflows, provide peer support, and measure adoption, business results, and compliance.