HR and IT AI alignment is becoming a harder operational question as companies move from pilots to changes in day-to-day work. Gartner said on October 7 that only 38% of chief human resources officers and chief information officers share an understanding of how artificial intelligence affects the future of work. The finding was released during Gartner HR Symposium/Xpo in London and points to a gap that cannot be fixed by giving either function a larger seat at the table alone.
The result is a Gartner survey finding, not a census of every employer. Its announcement does not disclose the survey sample, geography, or methodology, so the 38% figure should be read as a signal from Gartner’s research rather than a universal estimate. Even so, the issue it raises is concrete: a company can have an AI roadmap and still lack agreement about which work will change, which skills will matter, who owns adoption, and how success should be measured.
Why HR and IT AI alignment is an operating-model issue
AI programs often begin in technology teams because they involve model choices, security, data access, integrations, and procurement. But those decisions quickly affect job design, training, performance expectations, hiring, and internal mobility—areas where HR has essential expertise. Gartner’s release argues that workforce readiness, adoption, and organizational change are now part of the CIO agenda, while the pace of AI projects can force people decisions into technology initiatives before conventional workforce-planning processes catch up.
That framing matters because it shifts the question away from whether HR should be “consulted.” If a team deploys an AI tool that changes how work is assigned, reviewed, or escalated, the rollout is already an organizational-design decision. The technology may function as specified while the implementation still fails because managers lack time to coach people, employees do not understand what is expected of them, or responsibility for a bad outcome is unclear.
Gartner’s response is practical rather than abstract. It recommends embedding HR staff in AI delivery teams for real-time workforce questions, or having HR coach cross-functional teams on tools such as skills maps and literacy programs. It also calls for a shared view of responsibility and shared indicators of success instead of separate functional measures. Those are management choices, not a substitute for proving that a particular model or workflow is useful.
The hidden cost in a technology-only business case
One of the sharper points in Gartner’s release concerns the costs that do not appear in a narrow technology budget. A business case can include software, infrastructure, and implementation work yet undercount the time needed for training, the challenge of retaining relevant talent, the effect on employee engagement, or changes to the structure of a team. That can make an AI project look more efficient on paper than it is in practice.
Unhyd’s recent reporting on Workday’s workforce data described a separate but related shift: employers are placing more weight on skills connected to building, automating, evaluating, and improving AI-enabled workflows than on basic prompting alone. That does not validate Gartner’s percentage, and the two pieces of research measure different things. Together, however, they underline why a skills plan cannot be bolted onto a technical rollout after decisions have already been made.
For CIOs, the implication is to treat workforce questions as delivery risks from the start. Which teams will have authority to change a process? Who will review AI-generated outputs? What training is necessary before a tool is used in a consequential workflow? For CHROs, the implication is not simply to create an AI course catalogue. It is to help define the roles, capability expectations, feedback loops, and accountability that make a deployment workable.
What to watch next
The useful follow-up is not a generic pledge to collaborate. Readers should look for whether organizations name a shared executive owner, publish a clear division of responsibility, and track outcomes that connect technical performance with adoption and work quality. Measures will vary by use case, but a dashboard that reports only model usage or cost savings is unlikely to capture whether a change is actually sustainable for the people doing the work.
Gartner’s own London conference program frames AI alongside workforce skills, development, and enterprise change management. That is a useful description of the work in front of leaders. The real test is whether HR and IT can turn that shared language into joint decisions before an AI rollout becomes someone else’s problem.
