In April 2026, the collaboration software company Atlassian gave Avani Prabhakar a new job. 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: chief people and AI enablement officer.
She was not the first. In May 2025, Moderna, the biotech company, merged its HR and IT departments and put the combined organization under Tracey Franklin, its chief human resources officer (CHRO), now chief people and digital technology officer. In March 2026, Lumen Technologies, a communications services company, expanded the role of its chief people officer to chief people and AI enablement officer.
Three companies in 12 months have put the head of Human Resources in charge of artificial intelligence transformation because they grasped something that most leaders still have not: The hard part of AI is not deploying the technology. It is reimagining the work and the workforce that will be doing it.
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The job is no longer a stable unit
For a century, the job has been the fundamental unit of the organization. We hire into jobs, pay by job title, promote from job to job, and plan head count using the job as the basic unit of measure. And this is absolutely fine as long as the jobs in question map closely to the units of work that the company needs its employees to perform. But AI is breaking that connection.
How? Well, a job is really a bundle of tasks, and AI does not affect every task in every bundle 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 precisely because they require judgment, context, or accountability. When we decompose most of the jobs that have traditionally been done by humans, it is rare to find that an AI system can straightforwardly take over every task. And this means we cannot use the human job as the lens for understanding how to integrate 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. And we cannot predict with any certainty right now what the skill demands of 2030 will be, 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. And what this means—as Atlassian, Moderna, and Lumen have realized—is that the people function must be fundamentally reimagined.
From credentials to adaptability
Traditionally, hiring has followed credentials and experience, because these were taken as evidence of competence. But they matter less and less now. When roles are shifting, the most important thing to hire for isn’t a Harvard degree or work experience at Apple. As my friend Alec Litowitz argues in his new book, The Adaptability Quotient: Rewiring Your Mind for Success in the Next Human Era, the most important asset a hire can have is adaptability.
Employees who can repeatedly change what they are good at will be the gold that companies pan for in the new era of work. Recruiting and training need to be rebuilt around that ability. IBM has already moved partway in this direction. Its skills-first approach to hiring explicitly looks at candidates’ skills and their ability to learn, rather than relying only on formal qualifications.
The lesson for anyone concerned with recruitment and development is that instead of simply asking what skills someone has, it’s much 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? In a word, the best predictor of talent in this new era may not be what someone is good at today, but how quickly they can become good at something else tomorrow.
From layoffs to redesigning work
The question for HR used to be, how many people do we need in which roles? And indeed, most AI restructuring still, for now, 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, then it is a mistake to design AI transformation around the goal of head count reduction.
Instead, as my coauthors and I discussed in a recent article for Harvard Business Review, organizations should ask what work needs to get done, what capabilities does that work require, and what combination of humans and AI can provide them? For example, Citigroup 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. Leaders need to decompose the work and decide what AI should do, what humans should do, and where oversight belongs. This may well have an effect on head count, but these effects should be the consequence of redesigning the work, not the objective.
From measuring output to judging contribution
As I argued recently, AI-assisted work can be excellent without it telling you much about the excellence of the person who contributed to producing it.
That creates a problem for performance management. Speed and volume of output are easy to measure, and traditionally, this is what we have rewarded. But AI allows vast production at high speed, and the value of the human contribution lies increasingly in what people broadly call judgment—things like setting direction, catching mistakes, and deciding what context matters and how.
What gets rewarded gets done, goes the old leadership maxim, and it applies here too. If companies keep rewarding the old proxies for performance, that’s what employees will optimize for. Incentive systems must therefore change, and an early pioneer is the professional services firm EY. Just last month, the firm announced $100 million in bonuses for employees who demonstrate qualities including judgment, business acumen, adaptability, and leadership, alongside technology adoption.
That is the shift HR needs to make: Keep judging the quality of the work, but start rewarding the quality of the human contribution to it.
4 Moves for the CHRO
Here are four moves that CHROs can make right now to start adapting to the new world of work.
1. Put adaptability into the rubric. Pick one high-volume role and rewrite the hiring and promotion criteria so that demonstrated learning carries explicit weight alongside existing expertise.
2. Require a work map before a head count decision. Before approving an AI-driven restructuring, require the responsible individuals to show what tasks are changing, what AI can actually do in that context, where human oversight and accountability remain necessary, and what capabilities would disappear with the people being removed.
3. Separate output from contribution. Add one question to performance reviews for AI-assisted work: What did this person contribute to the result? Managers should be able to identify the judgment, framing, context, error-catching, or accountability the employee provided rather than equating greater volume with better performance.
4. Build routes between jobs, not just ladders within them. If roles are going to keep changing, employees need ways to move toward emerging work before their current job disappears. Mastercard’s Unlocked, for example, matches employees to projects, mentors, and roles based on the skills they have and want to build.
HR in the Age of AI
For most of its history, HR has operated on an assumption that the organization knew what a job was. AI changes that starting point. And that makes the people function responsible for something much bigger than managing the workforce. It must also help the organization decide how human and machine capabilities should fit together as both keep changing.
This is not ultimately a question of how much work AI can take over. It is a question of how to build an organization in which people and AI each do the work they are best suited to do, and can keep adjusting that division as the technology changes.
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