Tech‑Enabled HR Platforms & HCM Transformation

For many Canadian employers, the first months of AI adoption looked deceptively bright. Enterprise contracts were signed, vendors demoed sleek dashboards, and leaders promoted a vision of seamless HR processes – from automated hiring and self-serve workflows to smarter forecasting. 

Yet survey data is revealing a different picture: increasingly frustrated employees and ROIs falling far short of expectations. It’s becoming clear that inside many organizations, the honeymoon phase is over. 

That initial period of excitement often fades when employees begin using these tools at scale. Korn Ferry research finds that nine out of 10 AI users have abandoned workplace tools “at least once” and reverted to non-AI methods out of frustration, even as executives remain broadly optimistic – revealing a widening gap between C-suite perceptions and employee-level reality. 

That early optimism was reinforced by cultural and market forces that persist today. In a recent interview with Canadian HR Reporter, computing science professor Randy Goebel pointed to a small group of “technology oligarchs” and venture capitalists perpetuating an overhyped system built on “an enthusiasm that’s artificial in both investors and promoters.” At the same time, legal controversies over AI-generated errors and accusations of “AI washing” in high-profile layoffs are reshaping how employees and unions view the technology. 

For Canadian employers, the question is no longer whether to digitize HR but how to make tech-enabled HCM trustworthy, governable, and worth the investment. 

Another recent survey by AI platform WRITER reports that almost 48 percent of executives describe AI adoption as “a massive disappointment,” and 54 percent say it is “tearing their company apart,” fuelling internal power struggles.

Behind those numbers is a common pattern: organizations bolt powerful tools onto brittle processes and fragmented data, then discover that adoption and trust are far harder to engineer than a simple software rollout.

AI-enabled HR platforms are no exception. Nadim Kara, executive vice president and head of people and culture of GreenShield, describes what happens when HR tech is introduced into that kind of environment. As he explains, even the most sophisticated AI product will underperform if it is deployed into a system that is fundamentally misaligned. For him, it comes down to sign-on. 

“A recurring stumbling block for me has been when technology is implemented before the organization is truly aligned on process, data, and business processes,” Kara says. “You end up with a system that works ‘on paper,’ but in real life it forces workarounds: duplicate entry, inconsistent data, managers unsure what’s expected, and employees feeling like HR is complicated.”

The challenge, then, for Canadian HR leaders is to turn those lessons into a more realistic approach to human capital management (HCM) transformation – one that treats AI not as an autopilot but as a set of tools that only work when governance, training, and job design evolve alongside them.

“On any given day, I’m looking at headcount, vacancies, movement, and where we’re seeing pressure points, then translating that into action: staffing decisions, capability plans, and manager supports”
Nadim KaraGreenShield

At the centre of most platform journeys is a desire for a single, reliable view of the workforce. For Kara, that is also the most valuable day-to-day function. “For me, it’s the ability to get a single, reliable view of the workforce – the ‘one version of the truth,’” he says. “On any given day, I’m looking at headcount, vacancies, movement, and where we’re seeing pressure points, then translating that into action: staffing decisions, capability plans, and manager supports.”

When that core data layer works, it changes how HR shows up in the business. “In practice, it’s not glamorous, but it’s powerful,” Kara adds. “When data is consistent and accessible, leaders spend less time debating whose spreadsheet is correct and more time solving the actual people challenges.”

Jimmy Plante, CEO of Folks HR, has reached a similar conclusion from a different angle. He notes that one of the most common fracture points he’s observed in HCM transformation is the gap between how platforms are designed and how work is actually structured – particularly in payroll.

“One of the most persistent challenges I have encountered – both personally and through listening to our customers – is the disconnect between HR data and payroll data,” he says. “When these systems don’t communicate, HR teams end up re-entering the same information multiple times, which creates errors and wastes enormous amounts of time. A simple change like an employee address update or a salary adjustment requires manual intervention in two or three different platforms.”

That frustration shaped how Folks HR built its own offering. Plante emphasizes that the value of technology isn’t just in individual modules but in “how seamlessly those modules work together.” Without that forethought, adding new tools can increase complexity rather than reduce it – undercutting the promise of efficiency.

“That insight directly shaped the direction we took with Folks Payroll,” Plante adds. “We built payroll natively into the platform rather than as a bolt-on integration. When your HR and payroll share a single data layer, the friction disappears. That is the difference that truly changes the daily experience for HR teams.”

In effect, these perspectives point toward an HR equivalent of the “digital-native brain” now discussed in other industries: a consolidated, trusted data core and operating model that AI can plug into, rather than a series of isolated tools layered onto conflicting databases and shadow systems.

“What I learned from that challenge is that the value of HR technology is not just in each individual module – it is in how seamlessly those modules work together,” Plante says. “That insight directly shaped the direction we took with Folks Payroll: building payroll natively into the platform rather than as a bolt-on integration. When your HR and payroll share a single data layer, the friction disappears. That is the difference that truly changes the daily experience for HR teams.”

“One of our core beliefs at Folks is that organizations should make people decisions based on data, not instinct”
Jimmy PlanteFolks HR

Even as platforms become more capable, experts stress that AI should be treated as a decision-support layer, not an automatic pilot for HR. In a CHRR article, Markus Giesler, a marketing professor at York University’s Schulich School of Business, says the real challenge in AI-enabled compensation decisions is the ability to back them up with more than empty promises.

“These algorithms are very efficient,” he says. “But on explainability, on justification, I need to be in a position to legitimately explicate and communicate to the workforce why I have made this assessment … when there’s transparency, there’s explainability.”

Resume Now survey data suggests workers share that ambivalence: 68 percent of employees say they are “cautiously optimistic” and believe AI will increase fairness in pay decisions, and the same proportion say they would trust pay decisions involving AI more than those without it. Yet 65 percent worry about algorithmic bias, 54 percent cite a lack of transparency, and 45 percent fear AI replacing human judgment entirely.

The insistence on a human in the loop is not just about ethics – it is also about organizational control. As Folks HR’s Plante puts it, data and AI sharpen judgment; they do not replace the need for judgment in the first place.

“One of our core beliefs at Folks is that organizations should make people decisions based on data, not instinct,” Plante says. “When leaders have reliable, up-to-date information at their fingertips, HR moves from being a reactive function to a strategic one. That shift – from gut feel to data-driven decision-making – is one of the most meaningful outcomes we see in organizations that fully embrace their HR platform.”

If there is one theme that cuts across case studies and research, it is that AI will not rescue broken HR processes. It may have taken some time for leaders to fully recognize this, but the message is now clear.

Kara is blunt about this. “The lesson I’ve learned is simple: transformation is 20 percent configuration and 80 percent clarity,” he says. “If you don’t standardize the fundamentals – job architecture, approvals, data definitions, and the employee journey – you’re just digitizing yesterday’s friction.”

Canadian labour market research on generative AI points in the same direction. In a study on GenAI’s impact on Canada’s labour market, Matthias Oschinski estimates that key tasks in about half of Canadian jobs could technically be automated over the next five years. However, he stresses that this does not automatically translate into productivity gains – and it certainly does not mean HR will see relief in training pressures. According to Oschinski, Canada “has always done a really bad job in training its own workforce,” and he warns this is “a big problem now with AI, because jobs will change at a much faster pace than the last 20 years or so.”

Oschinski cautions against assuming that algorithms understand workflows better than frontline staff. This is where HR needs to step in. Workers, he argues, need support “in understanding how their expertise interacts with AI,” including how to design prompts and workflows and how to push back when algorithmic recommendations do not make sense.

In practice, that means HCM transformation has to start with process and capability, not software. Without rethinking how recruitment, onboarding, performance, and employee relations cases flow end to end, AI risks becoming a narrow optimization tool that improves individual steps while the overall employee experience remains fragmented.

Another recurring pattern in stalled AI and HR platform programs is the urge to “do everything at once.” In the HR context, that often looks like multiyear transformation plans that promise to rewire talent acquisition, core HR, learning, and performance in a single sweep – only to bog down in competing priorities and change fatigue.

In an interview with CHRR, tech policy researcher André Côté describes companies “scrambling to purchase access” to expansive AI-enabled suites without clearly linking them to business problems or sequencing changes in a manageable way.

Vendors and practitioners who see more sustained success tend to adopt a modular strategy: define a target operating model for a specific domain, then build it piece by piece.

At Citation Canada, CEO Dave Lacey frames this in terms of context and focus. “The most consistent stumbling block we see through our client relationships and through our own experiences as leaders is the gap between purchasing software and actually using it well,” he says. “A lot of businesses invest in an HR platform and consider the problem solved, when in reality adoption is low, the content is too generic to rely on, and the team doesn’t have the confidence to use it when something real comes up.”

That insight led Citation Canada to concentrate on a narrower but deeper slice of the problem: Canadian compliance and employment relations. Each well-designed module – whether a jurisdiction-specific policy library, an integrated HR payroll data core, or a robust analytics dashboard – becomes a self-contained success story that builds organizational trust and capability.

Those wins, in turn, make it easier to expand into adjacent domains without promising an all-or-nothing transformation. “The learning that tends to follow is that context and support matter as much as the software itself,” Lacey says. As an example, he points to Weight Watchers Canada’s HR lead, Corry McKenna, who was managing Canadian compliance within a larger global role.

“She didn’t need another broad platform designed for multiple markets. She needed something built specifically for Canada, with tools she could trust and people she could call,” he says. “The turning point for her, like many of our members, was realizing she could go to Citation Canada first instead of going straight to external legal counsel. That shift in how she worked, and the confidence that came with it, is exactly what we set out to deliver.”

“A lot of businesses invest in an HR platform and consider the problem solved, when in reality adoption is low, the content is too generic to rely on, and the team doesn’t have the confidence to use it when something real comes up”
Dave LaceyCitation Canada

Taken together, the recent Canadian evidence points to a clear diagnosis. Tech-enabled HR platforms and AI are not underperforming because the algorithms are weak. They are underperforming because, too often, they are introduced into misaligned operating models: fragmented data, static processes, ad hoc governance, limited training, and cultures that prioritize speed over learning.

Kara’s experience – “transformation is 20 percent configuration and 80 percent clarity” – captures one part of the answer. Plante’s insistence on seamless data layers and Lacey’s focus on Canadian-specific content and support round out the picture.

For HR leaders, the path forward is not to abandon AI or platforms but to reframe them as tools that depend on the fundamentals of sound management and employment practice. If they do, AI and tech-enabled HCM can still deliver on their promise: freeing HR from transactional firefighting, sharpening people decisions, and helping Canadian organizations navigate a labour market in flux. But that outcome will hinge less on which platform is selected and more on whether HR is empowered to redesign how work gets done – with data, governance, and people at the core.