UK survey finds 95% of HR leaders carrying heavier workloads
“Cautious optimism.” That’s how Lola Obomighie describes her approach to AI.
As vice-president of people, culture and organizational effectiveness at Northumberland Hills Hospital, she says she is “actively exploring” the newer tech while being mindful of the implications for privacy, confidentiality and data governance, and the guardrails needed to ensure it is used responsibly.
But does that mean an increase in workload for HR? Obomighie said AI hasn’t had a noticeable impact in creating more or less work.
"Rather than dramatically changing my workload today, AI is changing how I think about the work and where there may be opportunities to work differently and more efficiently in the future,” she says.
“What it has created is a lot of excitement and curiosity about the possibilities; for instance, how AI might improve access to policies and information for users, support more efficient scheduling, or reduce some of the administrative burden that comes with our work.”
HR reports rising workloads
That measured approach contrasts with a recent survey out of the U.K. that finds 95% of HR leaders report rising workloads, and 91% say their responsibilities have expanded over the past year as AI workforce planning, employee upskilling, governance and recruitment redesign pile on top of existing obligations.
Despite this expanding mandate, only 24% of organizations have a documented AI workforce strategy.
"It's not overly surprising," says Parbudyal Singh, professor of human resource management at York University.
“It can lead to work intensification and overload for HR professionals, and managers — but more so when you don’t plan for it and you don’t manage it well.”
Often, HR doesn’t understand some of the programs such as ChatGPT but they are asked to use the systems to help the organization transform work, he says.
“IT would normally be in charge of the technical side of it, but we in HR, we're normally expected to train the employees, to decide whether or not work has to be redesigned and whether workflows need to be changed.
“So, a lot is expected of us, and the resources and training are not there.”
Using AI for analysis
Nita Chhinzer, associate professor in human resources management at the University of Guelp, says burnout among HR professionals is coming from two distinct sources.
The first is a shift in the nature of work itself. When HR professionals spent hours manually updating databases or reviewing files one by one, that work carried what she calls a lower cognitive load.
“It's the stuff that's not overly fulfilling, it's just tasks. And when we shift to AI and have AI do the tasks for us, such as updating spreadsheets, such as updating the data, it didn't reduce our workload — it actually increased our cognitive load about how we were going to interpret that information.”
For example, if HR is looking at performance reviews to see patterns and trajectories in certain departments, they’re familiar with the data and have more confidence in speaking about the work, she says.
“When we use AI for data analysis, our familiarity with the data actually drops… I have to now go and reverse engineer the justification, the rationale and the understanding. And that’s a greater disconnect between us and our work,” says Chhinzer.
“So, that is one of the big reasons why we’re experiencing that burnout.”
The need for human judgment
That shift in the nature of work is highlighted by Heba Farid, director of HR at Second Harvest. She says AI has meaningfully cut the time she spends on administrative tasks such as drafting emails, summarizing documents and developing first drafts of policies.
But it hasn't reduced her overall workload.
"Instead, it has changed the nature of my work. I still need to review, validate and tailor AI-generated content to ensure it is accurate, compliant and reflects the organization's values and culture, she says.
“In many ways, AI has become a helpful partner that improves productivity rather than replacing the need for human judgment."
The human skills that AI cannot replicate — empathy, judgment, strategic thinking — are the ones HR should be leaning into, says Farid.
"AI has helped me be more productive," she says, "but it has also reinforced the importance of the uniquely human skills that are essential in HR."
Pressure from above
Chhinzer says expectations have soared as AI has made it easier to produce data-rich deliverables — and upper management has responded by demanding more of them.
"Without the use of AI, a recruiter might be able to identify what their selection ratio is, how many people applied and how many were selected for the job," she says.
"But now, with the use of AI, there's this increasing demand that we would have to identify multiple ratios… how many people made it through each of the levels, what was the average score for a cutoff, what were the demographic splits, what were the educational splits?"
The result, she says, is that HR professionals are being turned into repositories of information — whether or not that information is actually needed.
“Just because you were able to get a pretty dashboard doesn't mean it's actually conveying anything of value.”
The 'review and verification burden'
For Singh, a significant share of the workload increase facing HR stems from what he calls the review and verification burden — the time spent checking AI-generated material not just for accuracy, but for embedded bias.
"Normally we would trust our employees. They're well-trained, they're educated to use practices they've historically used to help them with document production and dissemination," he says. "But now they're getting stuff that's sometimes more easily acquired, and sometimes from sources that are questionable. So, we have to double-check that. And that's where part of the problem is with the overload."
On the governance side, the research found that 62% of HR professionals worry about AI governance and compliance risks, while 54% of organizations have no formal policy governing how employees use AI tools.
Singh notes that legal exposure is becoming a real factor. "We're beginning to see court cases where AI was used inappropriately and it opens the organization up to liability," he says.
‘FOBO’ and the Grim Reaper problem
Chhinzer identifies two psychological stressors that are rarely named but widely felt in the HR community.
The first is what she calls “FOBO” — fear of becoming obsolete. "We're doing the work with this recognition that we're training systems that could tomorrow replace us," she says.
The second is a dynamic she describes as the Grim Reaper problem: HR's position as the face of workforce restructuring and job loss means professionals in the field are often isolated.
"Management thinks we represent the employees, employees think we represent management," she says. "We're the bad guys in everyone's story during periods of decline, during periods of job loss, during periods of restructuring. And it's hard for us as HR professionals to recognize that that is a commonality in our profession. It's an occupational risk."
That dynamic intensifies when AI is involved, because HR is being asked to lead transformation while simultaneously managing the anxieties that transformation creates. Singh notes that employees are increasingly bringing concerns to HR about job security and the handling of their personal data — conversations that didn't exist at this volume before.
"That's also causing some of the anxieties around work," he says.
Three in four organizations without a plan
One of the more striking findings in the Coders Guild research is the gap between AI ambition and AI readiness. While 78% of HR leaders expect AI to significantly change workforce skills requirements within the next three years, only 24% of organizations have an AI workforce strategy.
Singh says that number may be generous.
"I actually think 24% might be on the high side in terms of integrated workforce strategy," he says. "A lot of organizations — especially those outside of the technology sector — are depending on their employees to actually use some of the free software and free systems to help them with work. It's almost incremental to their central plan, rather than being central to the plan."
Singh frames the strategic gap in terms of two distinct uses of AI that organizations are conflating.
"One is how AI influences current work to make it more efficient. But more so, how we can use AI to transform our work. That strategy is missing because we're just zooming in on the first part."
Chhinzer points to a structural problem with AI policies even where they exist: they go stale almost immediately.
"Every time you change policy or a regulation at work, you need to have a concession or agreement from your employees," she says. "But these kinds of things need to be live documents that change as things change — and they're not being treated that way."
She gives a concrete example: a policy governing where private information can be stored may become outdated the moment a platform updates how it handles links. "We have to go and update the policy and retrain people on that."
The productivity conundrum
On productivity gains — another frequently cited rationale for AI investment — Singh points to Statistics Canada data that finds the productivity gains can be quite low
The challenge is employees are taking a lot of time to learn AI.
"They're spending four hours learning something and saving two hours of time," he says. "So, they're not seeing the gains as yet."
Chhinzer adds that the rapid pace of change compounds the training burden. She completed a full 40-hour intensive AI training program in June. By September, three of the six tools she trained on had already been updated enough to require retraining.
"We're constantly in a learning cycle," she says, "and it feels like a lot of us are on a rat race to try to stay abreast with all of the different ways to do work."
Farid agrees on the importance of learning how AI works exactly.
“I've made a point of learning how to use AI effectively and responsibly. The more comfortable I become with the technology, the better I can leverage it to support my work while maintaining quality and oversight.”
What HR needs
Singh is direct about what would make a difference. AI needs to be embedded in organizational and HR strategy — not treated as a side project. Leadership has to provide resources and support for training on organizational time, not as extra work employees are expected to absorb on their own.
And organizations need to actively address anxieties about job displacement rather than leaving them to fester.
"Training needs to be on organizational time," he says. "It shouldn't be extra work for employees… We need to offer them training as part of their work.
“And we need to address anxieties and insecurities about AI in terms of replacing employees.”
