share on
On day one of Learning & Development Asia 2026 – Malaysia, HR, L&D, and business leaders came together to explore one of the defining questions of the workplace today: As AI changes roles, skills, and expectations at speed, how can organisations build workforces that are not only more productive, but more adaptable, accountable, and human?
AI is changing the workplace faster than many organisations can update job descriptions, career paths, or development plans. But across the conversations at Learning & Development Asia 2026 – Malaysia, day one, one message remained consistent: technology may transform how work gets done, but it cannot replace the human judgement, context, relationships, and accountability required to make that transformation work.
The speakers challenged HR leaders to look beyond one-off training programmes, generic AI roll-outs and narrow productivity measures. Instead, they called for a more integrated approach – one that starts with business needs, redesigns work at task level, builds capability continuously, and creates the conditions for employees across generations to contribute meaningfully.
Priya Sunil distills nine connected learnings from the day.
TL;DR: 5 key takeaways
- Diagnose before you train: Workforce challenges often stem from processes, tools, role design or management – not a lack of skills alone.
- Redesign work task by task: Identify what AI can automate, simplify or remove, and where human judgement, creativity and relationships matter most.
- Turn AI use into business value: Connect AI tools to real business needs, effective workflows and measurable outcomes – not isolated experiments.
- Build capability across the workforce: Develop AI fluency, critical thinking and human accountability across generations, supported by managers who encourage employees to question AI outputs.
- Create a system for continuous renewal: Link business-led capability planning to talent pathways, sustainable productivity and a culture that balances flexibility with accountability.
Learning #1: It's time HR stops treating every business challenge as a training problem
The starting point for any workforce transformation is to understand the problem properly.
When performance declines, a new system is introduced or leaders identify a skills gap, the immediate organisational response is often to request training. But several speakers cautioned that a training programme may only address the visible symptom – not the cause.
An employee may appear to lack capability, for example, when the real issue is unclear priorities, an overly complicated workflow, insufficient tools, poor manager support, low motivation or an environment that makes good performance difficult.
This matters because workforce challenges are rarely isolated. Organisations are managing talent shortages, changing employee expectations, demographic shifts, technology disruption and greater pressure to deliver results – all at once.
The lesson was clear: before creating content, diagnose the context.
For HR and L&D, this means asking harder questions. What outcome is the business trying to achieve? What is preventing people from achieving it now? Is the answer truly a learning intervention – or does the work, process, role design or leadership approach need to change first?
Only by identifying the real barrier can organisations build solutions that improve performance rather than simply add another programme to the calendar.
Learning #2: How to redesign work before deciding which roles AI should replace
Once organisations understand the business problem, the next step is not to ask whether AI will replace a role. It is to examine the work inside that role.
A major theme across the discussions was the need to break work down into tasks. Which tasks are repetitive and suitable for automation? Which could be simplified with AI support? Which no longer add value and can be removed altogether? And, critically, which tasks must remain human because they require judgement, creativity, relationships or accountability?
This moves the conversation beyond the familiar – and often anxious – question of whether jobs will disappear.
Instead, it enables leaders to make more informed choices about how work should evolve. AI may take on administrative, repetitive or data-heavy tasks, allowing employees to spend more time on higher-value activities such as problem-solving, customer engagement, innovation, coaching and decision-making.
But this opportunity will only be realised if organisations deliberately redesign workflows around it. Simply giving employees access to AI tools may make individual tasks faster, but it does not automatically make the organisation more effective.
The real opportunity is not to automate people out of work, but to remove low-value work so people can create more value.
This emphasises the need for HR and L&D teams to partner more closely with business, technology and operational leaders to define how roles should change – and what employees will need to do differently once AI becomes part of everyday work.
For HR, that means partnering more closely with business, technology and operational leaders to define how roles should change – and what employees will need to do differently once AI becomes part of everyday work.
Learning #3: Why personal AI use does not automatically create enterprise value
Many employees are already using AI in their daily lives and work. They may use it to write emails, create presentations, generate ideas, search for information or summarise documents.
But individual usage does not necessarily translate into business value.
A speaker made the point that organisations must distinguish between using AI and applying it effectively. AI adoption becomes meaningful when it is connected to a specific business problem, embedded into an appropriate workflow and supported by the right data, governance and decision-making structure.
Without this, AI can create a patchwork of isolated experiments: one employee using a tool for one task, another team building an unrelated pilot, and different functions adopting technology without a shared view of where value should come from.
This is where HR can help shift the conversation from personal productivity to enterprise performance. If an AI tool saves an employee 30 minutes a day, what happens to that time? Is it redirected toward higher-value work, better customer outcomes, stronger collaboration or more strategic decision-making? Or does it simply disappear into an already fragmented system?
The question is not whether employees are using AI. It is whether the organisation is converting AI-enabled efficiency into better outcomes.
Learning #4: How to build an AI-native workforce without writing off experienced employees
As AI adoption grows, organisations are increasingly focused on building an “AI-native” workforce. But one of the strongest messages from the day was that AI-native capability should not be confused with age.
Younger employees may be more familiar with emerging technology, new digital behaviours or different ways of communicating. Yet being AI-ready is not about being part of a particular generation, using the latest tool or simply feeling comfortable with technology.
Instead, it is about building a set of capabilities across the workforce.
These include:
- AI fluency: Understanding what AI can and cannot do.
- Application orientation: Knowing how to apply AI to real work and business problems.
- Critical thinking: Checking outputs, questioning assumptions and recognising errors or hallucinations.
- Human judgement: Knowing when AI should support a decision – and when human expertise must take over.
- Accountability: Taking responsibility for the outcome, rather than blaming the tool.
A quote by one of the speakers – "AI is not artificial intelligence – it is your additional intelligence." was one useful framing from the day. In other words, AI should extend human capability, not replace human responsibility.
This is particularly important for HR. Employees must understand how to use AI safely and effectively, but they also need the confidence to challenge AI-generated outputs. A convincing answer is not necessarily a correct answer. An automated recommendation is not necessarily a fair or appropriate decision.
The aim should therefore be to build AI fluency across roles, career stages and generations – while preserving the experience, context and organisational knowledge that more experienced employees bring.
Learning #5: Managers must teach employees to question AI – not just use it
If AI is becoming an everyday work companion, managers have an increasingly important role to play.
Employees cannot be expected to develop responsible AI habits through a single policy document or introductory training session. They need practical guidance in the flow of work: when to use AI, what information should never be entered into a tool, how to verify sources, how to spot an unreliable response and when to escalate a decision.
This is especially important because AI can create an illusion of certainty. Outputs may be fluent, polished and persuasive – even when they are incomplete, inaccurate or biased.
The speakers therefore emphasised the importance of verification and human oversight. Leaders need to create a culture where employees are encouraged to test, question and improve AI outputs rather than accept them blindly.
That requires a shift in management behaviour. Managers must be prepared to ask: How did you arrive at this answer? What did the AI contribute? What did you verify? What evidence supports this decision?
The goal is not to turn every employee into a technical specialist. It is to help every employee become a responsible and critical user of AI.
This becomes even more essential in areas such as recruitment, performance management, customer communication, and decision-making, where errors, bias or poor judgement can have serious consequences.
Learning #6: How to turn capability development into a business-led workforce strategy
The discussions then moved from individual AI capability to a broader question: how should organisations decide what skills to build in the first place?
The answer offered was simple, but significant: start with the business need, not the development request.
“Understand the context before the content.” Rather than beginning with a generic request for an AI course, a leadership programme or a new capability framework, HR and L&D need to identify the capabilities that will be strategically critical to the business.
What is changing in the organisation’s market, customer base, technology landscape or operating model? Which tasks and roles will be transformed? Where are the most significant risks? What capabilities will the organisation need to compete, grow and adapt?
Only then can leaders decide the appropriate response. That response may include training – but it could also involve hiring, internal mobility, job redesign, automation, manager coaching, process improvement or new partnerships.
This is a major opportunity for HR to become more than a provider of development programmes. The function can become a strategic architect of workforce capability: helping leaders plan for what work will look like next, where talent will come from, and how people can be prepared to perform in a changing environment.
Learning #7: Why organisations need talent pathways, not standalone learning interventions
Once critical capabilities have been identified, they cannot be built through a single course or a one-off programme. One speaker described the limits of an isolated training approach: a programme may solve one immediate problem, but it rarely creates a sustainable workforce system.
A more effective approach connects the entire employee lifecycle – from employer branding and university relationships to internships, graduate programmes, professional hiring, learning, career mobility and succession planning.
This is particularly relevant for organisations seeking to build a long-term pipeline of future talent. Working with universities, offering internships and involving leaders in early-career development can help organisations identify potential earlier and shape the behaviours, skills and expectations that students will need in the workplace.
Internships also allow employers to assess something that CVs, grades and interviews cannot always reveal: how people operate in a real work environment.
Do they show curiosity? Can they learn the fundamentals? Do they take ownership? Are they willing to collaborate, ask questions and solve problems?
The real test, as one speaker affirmed, is when they deliver in the company.
This does not mean employers should expect graduates to arrive fully formed. It means organisations should take a more active role in co-creating workforce readiness, rather than treating universities as simply a supplier of talent.
Learning #8: How to make productivity sustainable by improving the system around people
Productivity was another central thread across the sessions. But the speakers challenged the idea that productivity can be measured only through headcount, hours worked or short-term output.
A more useful view considers whether people have clear priorities, efficient processes, appropriate tools, relevant skills and a healthy culture.
If priorities are unclear, employees may work hard on the wrong things. If systems are fragmented, they may spend too much time on manual tasks, duplicative reporting and unnecessary approvals. If managers do not coach or support their teams, performance problems may remain unresolved. And if the culture rewards constant activity over meaningful outcomes, productivity can quickly become burnout.
This brings the discussion back to work design. AI may help remove administrative work, but leaders still need to decide what work should be removed, what work should be simplified and what employees should do with the capacity that is created. More efficient processes are valuable only if they allow people to focus on outcomes that matter.
For HR, this means challenging automatic requests for more headcount or more training. Sometimes the answer is to redesign the system around people: simplify a process, clarify accountability, improve the employee experience or equip managers to have better conversations.
Learning #9: Why future-ready organisations will combine accountability with compassion
The final lesson was that the future of work cannot be built through capability and technology alone. It also depends on leadership, trust and culture.
Employees are navigating uncertainty about AI, jobs, flexibility, career progression and changing performance expectations. Some may be excited by the opportunity; others may feel anxious or excluded. Organisations need to be clear about what is changing, why it is changing and what support will be available.
But transparency should not mean lowering standards.
Across the day, speakers returned to the importance of balancing accountability with compassion. Leaders must be willing to address performance issues, set clear expectations and make difficult decisions. At the same time, they should remain respectful, curious and attentive to the conditions affecting an employee’s performance.
One practical suggestion was deceptively simple: ask one more question.
Instead of only asking why an employee is underperforming, ask what is getting in the way. Instead of assuming resistance to AI is a lack of willingness, ask what the employee does not understand or fears losing. Instead of assuming that a generation wants less accountability, ask what conditions would help them contribute at their best.
This is particularly relevant in hybrid work environments. Flexibility can support attraction, wellbeing and engagement, but it must be paired with clarity about outcomes, communication and performance. Trust does not mean the absence of accountability; it means creating fair, transparent conditions in which accountability can work.
The overall lesson
As concurred across the sessions, the future-ready organisation will not be the one that adopts the most AI tools or runs the most development programmes. It will be the one that renews capability fastest – and does so without losing the human strengths that technology cannot replicate.
For HR and learning leaders, that means moving from training provider to workforce architect. It means beginning with business needs, redesigning work at task level, building AI fluency alongside human judgement, creating integrated talent pathways and measuring outcomes rather than activity.
Ultimately, what we can all agree on is this: AI can accelerate work. But people provide the context, accountability, compassion and creativity that turn transformation into genuine progress. That human contribution is what makes the difference!
Stay tuned for part two of our coverage where we will highlight the key learnings from day 2 of the conference!
Human Resources Online would like to thank all speakers, moderators, panellists, and attendees for being valuable contributors to this event. Shoutout to our day one speakers whose sharing formed the basis of this article:
- Viviantie Sarjuni, Chief Executive Officer, National Entrepreneurship Institute (INSKEN)
- Amberlicia Anthony Thane, Senior Consultant, Talogy
- Dominic Bohan, Co-founder, StoryIQ
- A. Sathiyan, Founder and Lead Facilitator, Engage Consultancy
- Eric Lim, Vice President, Learning Excellence | Group Business & Transaction Banking, Alliance Bank Malaysia
- Guna Govin, Founder and CEO, Nixfon Learning
- Alicia Yip, Head, Organisational Development & Culture, Proton
- Alvin Toh, Co-founder and Chief AI Officer, Straits Interactive
- Farha Burhan, Talent Development Lead, PETRONAS
- Joachim Ooi, Head of People, CoinGecko
- Andy Muniandy, Regional Director, Global Human Resources, and General Manager, Dell Technologies Malaysia
- Elvira Moey, Chief People Officer, NEXT Ventures
- Johnny Loke, Head of Learning & Development, UOB Malaysia
- Shawn Lim, Global Head, Emerging Talent, Infineon Technologies
We would also like to extend our gratitude to our sponsors & partners for making this conference possible:
GOLD SPONSORS
ENGAGE CONSULTANCY
StoryIQ
Talogy
Think Codex
SILVER SPONSORS
Nixfon Learning
Straits Interactive
Wordsburg
EXHIBITORS
Culture Partners
Disprz
SRKK AI & Automation Academy
EVENT PARTNER
Pigeonhole Live
Photo / HRO
share on
