HR practise on collision course with enterprise AI

The convergence of HR technology and enterprise AI infrastructure is no longer a theoretical promise in Asia—it is a pressing operational reality. Across Southeast Asia and Hong Kong, the rapid scaling of AI infrastructure, driven by data centre expansion, national AI strategies in Singapore and Malaysia, and fintech AI initiatives, has exposed a critical blind spot: workforce capability is not keeping pace with technological deployment.

In this environment, the role of the Chief Human Resource Officer (CHRO) has shifted dramatically. As Brenton Smith, vice president for Asia Pacific & Japan (APJ) at Cornerstone, notes in a recent interview with FutureCOO, CHROs are no longer just guardians of culture but critical agents of economic transformation. They are being asked to quantify the ROI of culture and close capability gaps in real time.

However, Asia presents a paradox. While adoption is high, strategic alignment is lagging. A 2026 report from the Asian Development Bank (ADB) warns that a widening "AI readiness divide" could see advanced economies like Singapore and Japan pull further ahead of developing neighbours, driven by disparities in digital infrastructure and human capital. In this fragmented landscape, CHROs must tailor strategies that bridge the gap between boardroom expectations for AI-driven growth and the lived reality of an often-sceptical workforce.

From efficiency to economic impact

One of the clearest trends in 2026 is the move away from using AI for simple cost-cutting. According to the PwC Financial Services AI Survey (March 2026), 57% of institutions in Mainland China and Hong Kong are now using AI to augment employees' roles rather than replace them. This represents a fundamental shift in the CHRO's mandate: they are now architects of business outcomes, not just administrative functions.

Smith reinforces this economic lens in his interview. Citing the Cornerstone Culture and Capability Index, he advises his sales teams to stop selling features and start selling economic benefits. "43% of cost savings is coming from reduced attrition… 21% of that cost is also reduced through reducing absenteeism… 13% of their cost savings is being driven by more efficient hiring," he states.

At a HR conference in Singapore, Singapore's senior parliamentary secretary for workforce, Shawn Huang, voiced a clear message: "The future of work isn't coming – it's already here." He emphasised that market leaders have moved beyond automating processes to redesigning work itself. When AI handles routine tasks, it frees people to do "what matters most – mentoring, creating and innovating".

This reframing—from "man versus machine" to technology as a complement to human capital—underscores Smith's call for CHROs to focus on economic outcomes rather than just operational metrics.

The trust deficit and governance gap

Despite the technological push, a significant cultural barrier remains trust. Employees across Asia are increasingly self-teaching AI skills without formal organisational support, creating shadow IT risks.

Smith identifies this as a critical governance issue. He admits that even he had to adjust his behaviour after taking Cornerstone's internal AI governance course.

"They've taught themselves in the absence of any corporate learning… but they haven't really stopped and thought about everything that they're putting into that large language model on their work system is now public knowledge," he warns.

Smith points to ISO/IEC 42001 and the EU AI Act as benchmarks, but notes that governance is "being undercooked in the market."

The consequences of weak governance extend beyond data security to financial exposure. Smith provides a striking example: "Last week… a very large organisation had to stop their users from using Claude, I think it was, because their bill was just too high. And another organisation received, I think it was, a $400 million token bill."

This tension—using AI to save costs while incurring massive, ungoverned expenses—has sharpened executive attention on usage controls, which models employees are accessing, and how they are being deployed.

For Smith, governance is not merely a compliance burden but a competitive differentiator. He argues that one of the benefits of the Cornerstone Workforce AI platform is that it is "highly governed and very secure." This stands in contrast to the fragmented, departmental-led adoption of AI tools that mirrors the early days of SaaS.

"When people implemented SaaS solutions, we found each department… would implement their own systems because they weren't getting what they wanted out of the centralised IT department," Smith recalls. "That's happening now at scale. It's great for innovation, but it's very scary when it comes to governance and security in general." Brenton Smith

Addressing the trust deficit, therefore, requires more than technical controls. It demands cultural transformation: transparent governance frameworks, certified training programs that bring employees out of the shadows about their AI use, and a deliberate effort to make AI a visible, accountable partner in people's decisions rather than a mysterious gatekeeper.

He underscores the importance of formal learning in this process, noting that the in-house governance course he took as a new leader revealed practices he "wasn't doing properly." His conclusion is direct: training modules on responsible AI use are "super important" for organisations seeking to protect their data, control costs, and build the employee trust necessary for sustainable AI adoption.

Protecting investment vs building the "people graph"

A unique challenge for large enterprises in Asia is the presence of decades-old legacy systems. Unlike greenfield start-ups, large banks and insurers cannot simply rip out their core infrastructure. Smith advocates for a pragmatic "protect and converge" strategy.

He positions Cornerstone's "Workforce AI" and "People Graph" (enhanced by the SkyHive acquisition) as the connective tissue.

"Protect that investment, but don't slow down… We are going all headless, so whatever your way of interacting in your business… we absolutely live with that," he says. This approach uses APIs to ingest data from competitive solutions such as Workday and ServiceNow, creating a dynamic, real-time snapshot of talent.

This is crucial for internal mobility. Instead of firing staff to hire "AI-ready" talent, Smith argues for retraining: "We could put a group of people into a completely new role with some training and development and keep them in the business."

This strategy is vital in markets like Hong Kong, where start-ups struggle to find technical talent, and where the government is being pushed to include talent development in its first five-year plan.

Hyper-localisation and the "graduate infusion" strategy

The convergence strategy cannot be uniform across the region. The ADB notes that countries like Singapore and Japan sit near the global frontier in AI infrastructure, while many developing economies lag significantly.

Cornerstone's Smith acknowledges this balancing act. He suggests a "safe core, fast edge" strategy: "Let the innovation go crazy within reason… but the core of your business… needs to be treated carefully and cautiously."

Interestingly, Smith points to a countertrend in hiring. Despite fears that AI would kill entry-level jobs, he references a podcast discussion about increased entry-level hiring in Silicon Valley. He notes that his own CEO has increased graduate hiring to bring "AI natives" into the business to teach the existing workforce.

This strategy is being replicated across Asia through large-scale initiatives such as the AVPN AI Opportunity Fund, which targets 720,000 workers and 100,000 MSMEs for AI upskilling by 2027, ranging from farmers in Indonesia to caregivers in Japan.

Recommendations for CHRO

Given the noise in the market, how does a CHRO choose the right path? Smith offers a pragmatic checklist for evaluation in 2026:

1. Stay with established vendors (for now): "If they have established vendors… I would strongly suggest they stay with them." Do not throw out good investments.
2. Demand outcome-based proof: Look for vendors who can show two-year implementations with proven business outcomes, not just feature lists.
3. Prioritise integration over replacement: Ensure that any new AI layer can ingest data from existing legacy systems.

"To me, the test — if you're out in the market making a big decision about what to buy to take you to the next level — look for organisations that have been doing it for years." Brenton Smith

In 2026, the convergence of HR tech and AI in Asia is ultimately a story of realism over hype. CHROs are moving away from the fear of job losses and towards the practical, often difficult, work of integrating AI into legacy environments while managing human trust.

As Smith concludes, the winners will be those who listen to their employees, protect their investments, but move decisively to close the capability gap through continuous, agile learning. "I don't believe in the doom and gloom of everyone losing their job," he says. "I always have faith in this industry reinventing itself."

He may be right there. Kaelyn Lowmaster, director analyst in the Gartner HR practice, says: "As AI changes how work gets done, organisations must rethink how employees gain expertise and experience, or they will find themselves without ready talent for the jobs AI helps create."