HR Chiefs Take Over AI Transformation as Companies Rethink Work

Three major companies have handed their top human resources executives responsibility for artificial intelligence transformation, a move that signals a fundamental shift in how organizations approach AI adoption. Atlassian, Moderna, and Lumen Technologies have each expanded the role of their chief people officer to oversee AI enablement, suggesting that the hardest part of AI is not deploying the technology but reimagining the work and the workforce that will use it.

In April 2026, collaboration software company Atlassian gave Avani Prabhakar a new assignment. She had been running a 700-person HR team. Now she oversees 3,500 people responsible for AI transformation across the company's 14,000 employees. Her new title is chief people and AI enablement officer. She was not the first to take on such a role. In May 2025, biotech company Moderna merged its HR and IT departments and placed the combined organization under Tracey Franklin, its chief human resources officer, now chief people and digital technology officer. In March 2026, communications services company Lumen Technologies expanded the role of its chief people officer to chief people and AI enablement officer.

The common thread is a recognition that the job—the fundamental unit of organizational structure for a century—is no longer a stable building block. Companies hire into jobs, pay by job title, promote from job to job, and plan head count using the job as the basic measure. That system works as long as jobs map closely to the units of work the company needs done. But AI is breaking that connection. A job is really a bundle of tasks, and AI does not affect every task equally. Some tasks can be automated outright. Some can be delegated to an AI agent with a human checking the result. Others become more valuable because they require judgment, context, or accountability. When most human jobs are decomposed, it is rare to find that an AI system can straightforwardly take over every task. This means the human job can no longer serve as the lens for integrating AI into the workforce.

The World Economic Forum projects that 39% of the skills workers rely on today will be transformed or obsolete by 2030, largely because of AI. The skill demands of 2030 cannot be predicted with certainty because AI transformation is both radically quick and profoundly uncertain. As models improve, work that required a person last year may not require one next year, while entirely new tasks will appear around the technology. The result is that the skills a job requires, the shape of the job itself, and the value of its outputs are all moving targets. This is why the people function must be fundamentally reimagined, as Atlassian, Moderna, and Lumen have realized.

Hiring practices are already shifting. Traditionally, hiring followed credentials and experience as evidence of competence. But those signals matter less when roles are constantly changing. The most important asset a hire can have is adaptability—the ability to repeatedly change what they are good at. Recruiting and training need to be rebuilt around that ability. IBM has moved partway in this direction with a skills-first approach that looks at candidates' skills and their ability to learn rather than relying only on formal qualifications. Instead of simply asking what skills someone has, it is more instructive to ask how they got those skills: How quickly did they learn their last new skill? What did they do when the demands of their job changed? The best predictor of talent may not be what someone is good at today, but how quickly they can become good at something else tomorrow.

The question for HR used to be how many people are needed in which roles. Most AI restructuring still starts with head count: set a target for roles to cut, then look for technology that can replace them. But if the job is no longer a stable unit, designing AI transformation around head count reduction is a mistake. Organizations should instead ask what work needs to get done, what capabilities that work requires, and what combination of humans and AI can provide them. Citigroup, for example, began with 50 processes flagged for greater automation, examined where technology and process redesign could change each one, and then made staffing decisions on that basis. The analysis came first; the restructuring followed. That is the new discipline HR needs to build: decompose the work and decide what AI should do, what humans should do, and where oversight belongs. Head count effects should be the consequence of redesigning work, not the objective.

Performance management also faces disruption. AI-assisted work can be excellent without revealing much about the excellence of the person who contributed to producing it. Speed and volume of output are easy to measure and traditionally rewarded. But AI allows vast production at high speed, and the value of human contribution lies increasingly in judgment—setting direction, catching mistakes, and deciding what context matters and how. What gets rewarded gets done, and companies must now decide how to reward judgment rather than mere output.

Author

Connor Quincy

Technology Reporter

Connor Quincy covers public affairs, politics, business, culture and daily news for Core Memo. The role focuses on verification, context, and clear explanations for readers.

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