As AI moves from experimentation into everyday work, HR’s challenge is becoming less about adopting another technology and more about redesigning work, building workforce confidence and deciding where human judgment must remain central.
Artificial intelligence has moved quickly from being an emerging technology discussed by specialists to something employees encounter in the everyday flow of work. In HR, that shift is already visible across recruitment, employee queries, analytics, learning, workforce planning and administrative processes. But greater access to AI has also brought a harder set of questions: Which processes should actually change? What skills will people need? Where does automation create value, and where might it weaken trust, judgment or the employee experience?
These questions framed The New Role of HR: Leading Business Change in the AI Era, a roundtable hosted by People Matters in partnership with HONO in Jakarta. Bringing together leaders from sectors including media, hospitality, pharmaceuticals, technology and other industries, the conversation moved beyond whether organisations will use AI towards how HR can shape its adoption responsibly. The opening reflections themselves captured the breadth of the shift: leaders spoke about empowerment, simplification, acceleration, adaptation, collaboration and, importantly, preserving what remains distinctly human.
A rapidly changing AI landscape
Setting the context for the discussion, Amandeep, Head of Operations-EMEA at HONO, began by pointing to the collective experience in the room: hundreds of years of HR practice now confronting a technology whose capabilities are evolving in months rather than decades.
For HR leaders, the rise of AI is significant: decisions being made today about adoption, workforce capability and process redesign could shape how effectively their organisations operate in the years ahead.
Amandeep also introduced HONO’s technology context. He described the company as a full-stack HRMS and payroll technology provider with operations across Southeast Asia and other international markets, and said its payroll engine supports payroll and compliance requirements across more than 25 countries. He also introduced ERA, HONO’s conversational HR interface, designed to allow employees and HR teams to interact with HR systems through natural-language conversations rather than conventional dashboard navigation.
The larger point, however, was not simply technological capability. Amandeep highlighted a series of indicators presented during the session showing the pace of AI adoption, including growing interest in agentic AI among HR leaders, high levels of AI usage among Indonesian workers and forecasts that a substantial share of AI initiatives may ultimately be discontinued. The contrast created an important tension for the discussion: experimentation is accelerating, but sustainable adoption requires organisations to be much clearer about the problem they are solving, the capabilities they need and the value they expect to create. From there, three priorities emerged.
Change problem, not technology problem
One of the clearest messages from the discussion was that organisations cannot simply insert AI into existing ways of working and expect transformation to follow.
Leaders spoke about beginning with the process itself: understanding where work is inefficient, which tasks can be redesigned, what skills the new process requires and only then considering the implications for structure. This is particularly important because AI transformation is occurring alongside concerns about data security, privacy, workforce capacity and the quality of AI-generated outputs.
The bigger barrier, however, may be psychological rather than technical. Employees are inevitably asking what AI means for their jobs. Creative professionals may question whether AI-generated output undermines originality. Others may hesitate because they do not understand how the technology works or what information is safe to share with it. Even leaders can encourage teams to “use AI” without defining the business problem, desired outcome or boundaries for use. That makes AI literacy a change-management capability.
Foundational education needs to go beyond teaching employees how to prompt a tool. It should help people understand where AI is useful, how to validate its outputs, when additional research is required and what organisational or personal data should never enter public models. More advanced learning can then be tailored to leaders, managers and specific functions.
Organisations are also experimenting with AI champions: people embedded within functions or business units who can demonstrate relevant use cases, address concerns and help colleagues translate general AI capabilities into the realities of their work. As the discussion highlighted, change champions have long helped organisations navigate major technology implementations; AI adoption may require the same local advocacy and peer-to-peer learning.
The lesson is straightforward: adoption cannot be measured by licences purchased or tools made available. It has to show up in how confidently and responsibly people change the way work gets done.
Automate the transaction, protect the human moment
As organisations identify more activities that AI can perform, another question becomes increasingly important: just because something can be automated, should it be?
The roundtable drew a distinction between transactional efficiency and human experience.
Routine policy queries, administrative workflows, reporting, forecasting, information retrieval and repetitive analysis may increasingly be handled or supported by technology. That can remove friction for employees while giving HR teams more time for higher-value work. But there are moments where efficiency is not the only outcome that matters.
Performance conversations, sensitive employee issues, coaching, difficult feedback, creative judgment and relationship-building depend on context, empathy and trust. Employee listening provides another example. Technology can collect, classify and synthesise feedback at scale, but employees may still need an HR professional who is prepared simply to listen.
Participants discussed practices such as HR clinics and multiple feedback channels as ways of preserving that connection, even as more of the surrounding HR infrastructure becomes automated. The broader principle was to use technology behind the scenes to create a better human experience at the point that matters.
This reframes productivity as well. The value of saving an hour through automation is not merely the hour itself. The more important question is what the organisation does with the time it gets back.
That capacity could be reinvested in developing talent, coaching managers, engaging employees, strengthening customer relationships or solving more complex business problems. AI creates leverage only when organisations deliberately redirect human attention towards work where people create greater value.
HR must connect AI to the business, not just the workforce
The final shift is perhaps the most important. If AI is changing jobs, workflows, organisational structures and capability requirements simultaneously, HR cannot approach it as a standalone people initiative. It has to understand what the business is trying to achieve and work backwards from there.
The discussion repeatedly returned to questions of customer needs, growth, speed, decision quality and organisational capability. From that perspective, the starting question is not, “Where can HR use AI?” It is, “Where does the business need to perform differently, and what combination of technology, skills and work design will enable it?”
That requires HR to operate across traditional functional boundaries. It may mean working with technology teams on governance and infrastructure, with business leaders on workflow redesign, with learning teams on capability building and with employees on adoption. It also requires HR leaders themselves to continue learning. Participants described formal AI education, internal learning platforms, peer sharing and simple experimentation with tools as part of their own efforts to understand what the technology can and cannot do.
The expectation, therefore, is not that HR becomes the technical owner of AI. Its opportunity is to become the connector between technology, workforce capability and business intent. The conversation positioned HR increasingly as a strategic partner responsible for linking AI investments to business growth, customer needs, organisational design and workforce readiness.
From adoption to orchestration
For HR, that creates a wider mandate. The function has an opportunity to move beyond administering the workforce or facilitating technology adoption and become an architect of how people, processes and AI work together. The organisations that make that transition will not necessarily be those that automate the most. They will be those that are clearest about what technology should do, what people should continue to own and how the two can combine to create better business outcomes.