Who Owns the AI Transformation When HR, IT, and the Business All Have a Claim?

The views and opinions expressed are those of Nico Orie and do not represent the official policy or position of any organization.

Companies spent the last two years treating AI adoption as a technology rollout, something to switch on and then manage change around afterward. That assumption doesn't hold up anymore since AI's payoff depends far more on how a company redesigns its operating model, its processes, and the shape of individual jobs than on which model or vendor it picks. That redesign work doesn't have a clean organizational home, and it falls somewhere between IT, HR, and the business. None of the three own it outright. The technology itself has also grown more complicated, leaving both the technical and the organizational questions wide open.

Nico Orie, VP People and Culture, Global Operations and AI Strategy at Coca-Cola Europacific Partners, has spent more than three decades inside this kind of large-scale operational change. He led the global rollout of Workday across 70 countries and roughly 120,000 employees during his time at Philips, and he's held a series of senior People & Culture roles at CCEP since 2017, taking on an expanded mandate covering global operations and AI strategy earlier this year. That background in global systems rollouts, process redesign, and change management at scale, is exactly the muscle most companies are now discovering they need and don't have.

"Now, a couple of years into the AI revolution, it's clearer and clearer that the true success of AI lies much more in the adoption, in how you change your operating models, and how you adapt your processes, than the technology itself," Orie says. 

The war for talent left a gap in work design

The idea that companies need to understand work at the task level isn't new. More than a century ago, scientific management treated the design of work as a discipline in its own right, breaking jobs down into their component tasks to understand how each one should be performed. "So in the time of Taylorism, it was HR who did the design of work," Orie says. That changed in the 1990s, when the war for talent pulled HR out of organizational design and into talent management, skills, and people analytics. 

What got left behind was a detailed understanding of the work itself. "What are the actual tasks that people do in work, what type of tasks are they, and how can we automate them? That's a big gap," Orie explains. Job descriptions and process maps, the tools most companies still rely on, describe work at too high a level to answer that question. In Orie’s view, closing the gap should start with HR, but it cannot happen there alone. IT has to define what the technology can do, while the business has to identify the problem worth solving. He sees those three functions converging into “something new, which is probably a new department.”

AI is giving knowledge work its own version of Taylorism

AI now gives companies ways to recover the missing task-level visibility. Orie points to AI-led employee interviews and digital “breadcrumbs” left in company systems as ways to capture how work gets done, then use AI itself to analyze and redesign it.

A century ago, Taylorism broke industrial work into its component motions and timed each one to optimize execution. AI-era task mapping does something structurally similar for knowledge work, using system logs and structured interviews instead of stopwatches. Part of what that data reveals, in Orie’s account, is how inefficiently AI often gets slotted into existing workflows, alternating with human steps in a way that adds friction rather than removing it. The redesigned version should compress that sequence into what he calls “AI, AI, output, human, human, AI.”

Orie is explicit that this shouldn’t go as far as the original did. “There’s also risk there that you go back to Taylorism where you have everything sort of mapped out and you don’t leave room for the grit,” he says. The goal is visibility into what tasks exist and where AI can take them over, without stripping out the judgment and improvisation that make people effective in the first place.

That caution has a direct precedent in the one domain that has automated longest. Wael Sabra is founder and CEO of the global talent platform SiiRA, which places remote talent and builds AI agents for the ad tech, media, telecom, and broadcast industries. Sabra believes the factory floor is the cautionary tale knowledge-work leaders should study. "Anyone who has designed a manufacturing process will tell you: do not discount the human element," he says. "Every automation will create an exception, which will need supervision and human handling along the way." Sabra's point sharpens Orie's: even as factory automation has advanced for decades, it never eliminated the need for people to catch what the machines can't, and knowledge work will be no different.

Automation exposed the limits of job-level thinking

The need for a task-level view becomes clearest once automation lands. By Orie’s account, CCEP has deployed roughly 170 robotic process automations, which is enough that the headcount impact should be measurable if the theory held. It hasn't been. "I've not been able to take out one FTE as a result of my 170 RPAs," he notes.

Two forces are behind that, in his telling. People are, as he puts it, inclined to fill freed-up time with something new almost immediately, "so they end up just as busy as before." At the same time, the work itself keeps moving. Employees take on new responsibilities and shift into higher-value tasks even as older ones get automated out from under them, which makes the whole picture harder to read than a simple before-and-after headcount comparison suggests.

Automating away 30% of a role is, relatively speaking, the tractable part. Figuring out what the remaining 70% should become, with no fixed target and a workforce that keeps redefining its own job in real time, is a bigger challenge. Orie's answer centers on building a different operating rhythm. "We need a more detailed management of the actual tasks and work that people are doing," he says. "We need to develop platforms and the way we manage teams, instead of just leaving it and hoping that it will happen. Because it will not happen that easily."

Philip Samuelraj, Founder and CEO of AI transformation company Techjays, sees the same pattern when companies hand out AI tools and wait for the gains to show up. "A 10 or 20% bump in individual productivity is not going to meaningfully change the company if the workflows around it stay the same," Samuelraj says. "The question is whether the process itself is fit for the agentic era. The best process is no process, so what is the bare minimum it takes to get the same output, with AI doing the work first and people stepping in where judgment and creativity are needed?" In his experience, that redesign is where most companies stall. "Reimagining the workflow is the hardest part. It's a business process problem, not a technology problem."

AI is forcing companies to redefine performance

Ernst & Young recently committed €100 million to an incentive program built around non-technical skills, a move that caught Orie’s attention because it points to a broader problem companies are only beginning to confront. As AI takes on more technical and administrative work, the human contribution becomes harder to isolate and measure. That raises a basic question about what good performance looks like when one person using AI well can multiply what they used to produce alone. Orie sees it as a combination of the outcome someone delivers, the human judgment, expertise, accountability behind it, and how effectively they use AI along the way. “No one really knows how to do this. But it’s inevitable that we start looking at another definition of performance,” he notes.

Business focus still matters most, but so do investments in people’s critical thinking and creativity, along with enough technical understanding of AI to apply it effectively. That requirement extends to managers. "A lot of managers really lack a bit deeper knowledge of AI," Orie warns. "They never admit it, and they always make statements." If AI keeps absorbing the lower-level tasks people used to learn on, and managers aren't deep enough in the technology to guide what replaces that learning, the pipeline for building the human skills starts running dry from both ends at once.

The layer between HR, IT, and the business

Resolving any of the above needs a home, and Orie’s answer is a Work Intelligence Center at the intersection of HR, IT, and the business. Its authority, in his view, comes less from a mandate on paper than from the credibility of the people. “You need to staff it, first of all, with credible people who have gravitas in the business,” he says.

The Work Intelligence Center’s job is to provide resources the business usually cannot produce on its own, such as reliable data, a methodology for analyzing work, and a current view of how work gets done. The business owns the transformation, while the center gives leaders the evidence and structure they need to make informed decisions about it. “That center has to be built on AI too,” Orie argues. “You need AI to do the analysis, and to think of the redesign.” Gartner’s research also projects that by 2029, 30% of organizations will create blended HR-IT teams specifically to accelerate AI enablement.

Samuelraj cautions that a new structure only earns its place if it produces something measurable. Too many companies, he says, stand up AI committees that slide into governance theater. "The pace of the committee becomes the pace of its laggards," he says. His test is deliberately tactical: "What were we doing manually that is fully done by agents by the end of the quarter? What workflows did we remove because they're no longer relevant? Are we getting more work done, at better quality, in a fraction of the time? If you can't clearly answer those, you may just be wallowing in governance."

The CHRO and CIO can no longer run separate transformations

The last open question is who makes the call when the technology and the workforce start pulling in different directions. “The Chief HR Officer has to make the call, in my view,” Orie answers. He’s just as direct about why that hasn’t happened much in practice: “I’ve been in HR for a long time in my career, and in all honesty, we lack a bit of the boldness to do that. We lack a bit of skill sometimes, but also boldness.” His resolution keeps HR in the lead but pairs it with IT as a co-owner. “I think the CIO and CHRO have to team up, basically, and then say, ‘Okay, jointly we will bring this forward.’”

It's a conviction Sabra shares from his vantage point leading a platform that both places people and deploys agents. He frames the current moment as a genuine first for knowledge work. "For the first time in history, leaders of knowledge workers have an option to change how work gets done," Sabra says. "Previously the only way to get work done was to throw more humans at it. Now technology is an option. But the true design is people plus tech working together." His warning is about which way companies lean when they build that design. "Companies that are people-first, and not tech-first, are the ones most primed to make workers collaborate with machines."

The gap between what leaders know and what they fund remains the clearest evidence that this is still an organizational problem rather than a technical one. “We should invest $1 in technology and then $3 in people,” Orie argues. “And now it’s the reverse. That’s $3 in technology and probably 20 cents in people in most companies. We need to start where the critical element of success lies. And that’s with people,” he concludes.