Tech pay growth pulls ahead of other industries

More than 600,000 open tech jobs point to sustained demand

Open job postings for technology roles exceeded 600,000 in June for the second month in a row, according to CompTIA. At the same time, unemployment among IT professionals fell below 3% for the first time this year. These numbers point to continued demand for technology skills, even as the wider hiring market remains uneven.

The underlying shift is not simply a return to broad-based tech hiring. Companies are changing the mix of skills they need as they pursue AI strategies. Demand is rising in areas that support those plans, while other categories are losing ground. The main constraint is increasingly access to the right skills.

The wage picture reinforces this trend. Technology led all other industries in wage growth during the second quarter, although it does not provide a specific growth rate. Combined with more than 600,000 vacancies and sub-3% IT unemployment, this suggests that companies still face meaningful competition for valuable technology talent.

For executives, aggregate hiring numbers can therefore be misleading. A company may reduce headcount in one technology function while paying more to recruit or retain specialists elsewhere. AI can automate some tasks without reducing demand for the people needed to design systems, integrate tools, manage complex technology, and guide transformation.

This changes the workforce question for the C-suite. The priority should not be whether to increase or decrease technology headcount in general. Leaders need to identify which technical capabilities directly support their AI plans, determine where those skills are scarce, and allocate compensation and hiring budgets accordingly.

The near-term outlook is constructive for people with skills aligned to these priorities. Companies are still hiring at scale. But the benefits will not be distributed evenly across technology occupations. The strongest position belongs to organizations that can define the capabilities they need before competition for those skills pushes costs higher.

AI is changing which tech roles companies value

AI-driven restructuring is putting more pressure on lower-level technology roles while increasing the value of experienced professionals. Companies may be automating lower-level roles while rewarding positions that require more experience and tenure.

This does not mean AI is simply replacing people. Instead, automation changes the work assigned to people. Tasks that are repetitive, standardized, and easier to specify can increasingly move to AI systems. Work that requires judgment, business context, technical oversight, or responsibility for complex outcomes remains harder to automate.

That shift has an important consequence for workforce structure. Junior technology jobs often provide employees with the experience needed to develop advanced skills. If companies remove too many entry-level tasks and positions, they can weaken the internal path that produces future senior employees. Immediate efficiency gains can therefore create a longer-term talent problem.

Executives should separate tasks from jobs when making automation decisions. A role may contain work that AI can perform alongside work that still requires human judgment. Automating selected tasks can raise productivity without requiring the entire position to disappear. It can also give employees more time for higher-value work, provided the company invests in the skills needed for that transition.

The more useful question for the C-suite is which capabilities become more valuable after automation. Experienced employees who can validate AI output, manage exceptions, connect technical decisions with business requirements, and take responsibility for results can become more important as AI use expands. Compensation and retention strategies should reflect that change.

Companies also need to protect their future supply of skilled workers. That means redesigning junior roles rather than automatically removing them. Entry-level employees can learn to supervise AI-assisted processes, verify outputs, handle less standardized work, and build deeper technical knowledge. Done well, automation reduces low-value work while accelerating skill development.

AI is therefore changing the composition of technology teams, not producing a simple choice between humans and machines.

AI-generated code could cost more than human-written code by 2028

Gartner projects that AI-enabled software development will become more expensive than human-written code by 2028. That forecast challenges a central assumption behind many AI investments: automating more development work will not necessarily reduce total software costs.

AI coding tools can help developers generate, modify, test, and document software faster. But generated code still needs review, security controls, testing, integration, maintenance, and governance. Faster code creation can also increase the volume of software an organization must manage.

This matters because software costs continue after code is written. Applications must remain secure, reliable, compliant, and compatible with changing systems. AI-generated output can require experienced employees to verify that code behaves as intended and meets company standards. Productivity gains at the development stage have limited value if they create additional work later.

The Gartner projection also gives executives a reason to challenge simple ROI models for AI-assisted development. Measures such as lines of code produced or development time saved do not show whether AI lowers the cost of delivering and maintaining a working system. CIOs and CFOs should measure the full lifecycle: tool and model costs, developer time, testing, security, infrastructure, integration, maintenance, and remediation.

The forecast does not establish that every AI development project will cost more than conventional development. Outcomes will differ by application, engineering practices, AI tools, and the level of human oversight required. It does show why executives should not assume that greater automation automatically produces lower costs.

The better objective is to use AI where it improves measurable business outcomes. For some development tasks, that may mean faster delivery. For others, human-written code may remain more economical once quality and maintenance are included.

This also reinforces the value of experienced technical talent. As AI produces more software, companies still need people who can assess architecture, validate output, manage security risks, and take responsibility for production systems. AI can change how development capacity is used, but it does not remove the need for engineering judgment.

For the C-suite, the investment standard should be clear. Evaluate AI-assisted software development against total lifecycle cost, software quality, delivery speed, and business value. Gartner’s 2028 projection suggests that companies that focus only on near-term coding productivity may miss the larger cost implications.

AI workforce planning now requires joint ownership from CIOs and HR

AI is changing the skills companies need faster than traditional hiring plans can adjust. That makes workforce design a core part of technology strategy. CIOs cannot make these decisions alone. They need HR and talent leaders involved when deciding which skills to build internally, which people to hire, and which work to automate.

CIOs need to work with HR and talent leaders to identify where talent is required, where skills will change, and which capabilities the business will need in the future. The goal is a precise plan for specific skills and jobs rather than a broad target for technology headcount.

The first task is to translate the AI strategy into workforce requirements. Leaders should identify the business processes AI will change and then determine what employees must be able to do after that change. Some existing skills will become less important. Others will need to develop. New roles may be necessary where the company lacks expertise in areas such as AI implementation, technical oversight, security, data, or governance.

This analysis should happen at the skill and task level. AI may automate part of an employee’s workload without eliminating the role. In other cases, several responsibilities may change enough to justify redesigning the position. This level of detail helps HR decide whether reskilling, internal transfers, external recruitment, or role changes offer the best response.

The main constraint is execution. A company can approve an AI strategy and buy the required technology relatively quickly. Developing experienced people takes longer. Recruiting externally can also become expensive when many employers pursue the same scarce skills. Workforce planning therefore needs to start alongside technology planning.

Clear ownership is also important. CIOs understand the technical capabilities and operating changes required. HR leaders understand workforce supply, compensation, recruiting, career development, and retention. Combining these views gives management a stronger basis for deciding where to hire and where to develop existing employees.

Executives should also avoid treating AI workforce planning as a one-time restructuring exercise. The required skills will continue to change as tools improve and employees learn to use them. Organizations need to monitor which tasks are actually being automated, where productivity is increasing, which skills remain difficult to source, and where new capability gaps appear.

The objective is not maximum automation or minimum headcount. It is having the right capabilities for the company’s operating model. CIOs and HR leaders that connect AI investment with detailed workforce planning can direct hiring and training budgets toward the roles that matter most while giving existing employees clearer routes to develop the skills the business will need next.

Key takeaways for leaders

• Tech talent remains competitive: Tech job postings exceeded 600,000 for two straight months while IT unemployment fell below 3%. Leaders should target hiring and compensation at skills directly tied to AI and business priorities rather than expanding tech headcount broadly.
• AI is shifting the value of skills: Automation is putting more pressure on lower-level tasks while increasing demand for experienced professionals. Automate at the task level while preserving pathways that develop junior employees into future senior talent.
• Measure AI development by total cost: Gartner projects AI-enabled software development will cost more than human-written code by 2028. CIOs and CFOs should assess lifecycle costs, quality, security, maintenance, and business value rather than coding speed alone.
• Make AI workforce planning a joint mandate: CIOs and HR leaders should translate AI plans into specific skill and role requirements. Decide early which capabilities to build internally, recruit externally, or redesign through automation.