Almost every applicant today says they know AI, and in most cases that means ChatGPT or Gemini. For Malaysian SMEs, HR leaders and corporate decision-makers, that answer no longer separates one candidate from another. The capability businesses actually need is the ability to apply AI to research, workflows, automation, programming and prototypes that solve real operational problems. This guide explains the difference, why it matters for cost and competitiveness, and how to redesign your hiring process so it rewards evidence of capability instead of claims of familiarity.
Introduction: The Question Every Interviewer Now Asks
A few years ago, asking a candidate about artificial intelligence was unusual. Today it is routine. Interviewers in Penang, Kuala Lumpur and Johor Bahru are adding a simple question to their list: do you use AI?
The reply is nearly always positive. Fresh graduates mention chatbots on their résumés. Staff attend AI workshops. Companies describe their teams as AI-ready.
On the surface, this looks like progress. Underneath, a different picture is forming. Ts. Lukas J. Tan, Founder and CEO of OPERiON, has observed this gap first-hand through organising PDX (Penang Digitalisation-AI Conference & Exhibition) and the AI Future Makers Bootcamp 2026. His conclusion is direct: businesses are not short of people who can open an AI application. They are short of people who can turn AI into something useful for an organisation.
For employers, this changes what a good hire looks like.
The Current Problem: Plenty of Tools, Too Few Builders
Conversations with business owners, educators, technology professionals and corporate leaders at PDX kept returning to one theme. Technology itself is not scarce. Software platforms, AI applications and digital solutions are widely available.
What is scarce is the person who can take those tools and connect them to a business outcome.
It is tempting to label this a general shortage of digital talent. The problem is narrower than that. The missing profile is someone who can combine AI with:
- Research and development
- Programming and basic software development
- Process redesign and workflow automation
- Prototyping and testing
- Business innovation grounded in a real problem
Lukas describes these people, half in jest, as a rare species. Many employers say they want them. Far fewer can find a candidate who can actually demonstrate the capability.
Meanwhile, the usual hiring signal has lost its value. When nearly everyone lists the same two or three chatbots, the line on the résumé tells an interviewer very little.
The Strategic Framework: AI Users Versus AI Builders
A clear definition helps. For hiring purposes, it is useful to separate two levels of AI capability.
An AI user interacts with an AI application to complete an individual task faster: drafting an email, summarising a report, preparing slides or generating ideas.
An AI builder applies AI to a business process or problem: studying how work flows today, identifying where AI can help, building or assembling a working prototype, connecting systems and checking whether the result delivers measurable value.
Both are legitimate. Both have a place in a modern organisation. The difference lies in the depth of contribution.
Recruitment offers a simple illustration. One person asks a chatbot to write a better job advertisement. Another examines how applications arrive, how candidate details are organised, how recruiters spot relevant qualifications, and where automation can remove repetitive administration while keeping human judgement and fairness in place.
The first saves minutes. The second may improve an entire function.
The same split appears in marketing, finance, operations, customer service and software development. The framework for employers is therefore straightforward: stop measuring familiarity with tools and start measuring what candidates have done with them.
Deep Breakdown: Five Practical Shifts for Assessing AI Capability
1. Replace the Tool Question with a Problem Question
Asking which AI tools a candidate knows invites a list. Asking which problem they have tackled with AI invites a story, and stories reveal thinking.
Useful prompts in an interview include:
- Describe a workflow you designed or improved using AI.
- What problem were you trying to solve, and who was affected by it?
- How did you decide AI was the right approach?
👉 Ask about problems solved, not applications recognised.
2. Invite a Practical Demonstration
For roles involving digital transformation, innovation, operations improvement or AI adoption, a short demonstration can be more informative than a polished CV. A candidate might walk through a prototype, a small automation or a documented workflow.
The prototype does not need to be production-ready. It may have gaps, need more testing or require substantial improvement. What it shows is the ability to observe a problem, research options, experiment and produce something tangible.
👉 A rough working prototype often says more than a perfect résumé.
3. Test How Candidates Judge AI Output
AI can produce confident answers that are wrong. A capable hire knows this and has a method for checking.
Ask how they evaluated the reliability of an AI-generated result. Ask what went wrong during development and how they fixed it. The answers expose critical thinking, honesty and practical judgement, qualities that conventional résumé screening rarely captures.
👉 The ability to question AI matters as much as the ability to use it.
4. Value Domain Knowledge Alongside Technical Skill
Not every AI builder needs to be a software engineer. A business graduate may understand an operational bottleneck better than a programmer. An HR student may notice weaknesses in a recruitment process that a developer would miss.
The strongest candidates often connect what they know about a field with the right AI capability. Their willingness to learn and experiment is the real asset.
👉 Look for people who pair business understanding with AI experimentation.
5. Keep the Fundamentals in the Scorecard
AI capability should sit alongside, not above, the qualities that have always mattered. Communication, ethics, teamwork, business understanding and responsible decision-making remain essential.
A candidate who can produce impressive software but cannot explain its risks or understand its users is not automatically a strong hire.
👉 Hire for technology, critical thinking and execution together.
Business Implications
For SMEs
Smaller firms rarely have room for hires who cannot contribute quickly. A person who can redesign one manual process with AI may create more value than several staff who only use chatbots for individual tasks.
- Prioritise candidates who show initiative on real problems.
- Use a small, practical task during interviews rather than long theoretical tests.
- Remember that AI amplifies what a business already has. Unclear processes become faster confusion, so pair new talent with a clear view of how your operations should work.
For HR Leaders
HR teams sit at the centre of this shift, both as recruiters and as a function that can itself benefit from AI.
- Update job descriptions to request evidence: projects, prototypes or workflows, not just tool names.
- Train interviewers to ask follow-up questions about reliability, mistakes and lessons learned.
- When exploring AI in your own recruitment workflows, keep fairness and human review in place, and handle candidate data with care under Malaysia's personal data protection requirements.
For Corporate Decision-Makers
Leaders approving AI investments should recognise that the return depends heavily on people, not only platforms. Licences and subscriptions deliver little if no one can connect them to how the organisation actually works.
- Treat AI capability as a workforce planning question, not only an IT purchase.
- Create internal pathways for staff who show builder potential to experiment safely.
- Measure AI adoption by outcomes delivered, not by the number of tools rolled out.
Ecosystem Layer: How Industry, Universities and Talent Can Meet Earlier
No single company can close this gap alone. It requires closer links between employers, universities and young people entering the workforce.
This is the thinking behind the AI Future Makers Bootcamp 2026, which concludes with its final competition on 7 November 2026. The programme encourages students to move beyond classroom knowledge and apply AI to genuine industry challenges.
One challenge area focuses on human resource management: recruitment, talent attraction, candidate screening and employee retention. Participants are asked to research the problem, understand the people affected, build a prototype and show how their approach might support a real HR department.
A survey of participants offered a telling insight. Many students were familiar with ChatGPT and Gemini, while their exposure to other AI development tools and approaches appeared much more limited. This is not a criticism of students. It reflects how AI education has been framed so far: strong on introducing applications, lighter on building with them.
Prompting, image generation and productivity tricks are a reasonable starting point. They should not be the finish line.
For employers, initiatives like this offer something practical: a chance to observe emerging talent through evidence of capability rather than qualifications alone. HR Directors, Talent Acquisition Managers, Learning and Development leaders and CEOs who are rethinking how they assess AI-capable people can learn more at aifm.asia.
At OPERiON, we see the same pattern in digital transformation work: the technology is rarely the hardest part; finding people who can apply it well usually is.
FAQ
What is the difference between an AI user and AI talent?
An AI user operates AI applications to finish individual tasks faster. AI talent, in the sense employers need, applies AI to business problems by researching, designing workflows, building prototypes and validating results.
Is knowing ChatGPT enough to be considered AI-ready?
No. Familiarity with ChatGPT or Gemini is now common and no longer distinguishes candidates. Employers should look for evidence of what a candidate has built or improved with AI.
How can HR assess AI skills during an interview?
Ask candidates to demonstrate a prototype or explain a workflow they designed, describe the problem it solved, explain how they checked AI output for reliability, and share what went wrong and how they fixed it.
Do AI-capable hires need to be programmers?
Not necessarily. People with strong domain knowledge in areas such as HR, operations or finance can be highly valuable when they combine that understanding with AI experimentation.
Should AI capability outweigh other hiring criteria?
No. Communication, ethics, teamwork, business understanding and responsible decision-making remain essential. AI capability should complement these qualities, not replace them.
Conclusion: Hire for What Candidates Can Show
The AI talent conversation in Malaysia is moving from awareness to application. When nearly every applicant claims AI knowledge, the claim stops being useful.
What still stands out is evidence: a workflow redesigned, a prototype tested, a problem understood and a lesson learned along the way.
For SMEs, HR leaders and corporate decision-makers, the practical step is to change the interview itself. Replace "Do you know AI?" with a request to see what the candidate has built and why.
The organisations that make this shift early are likely to find the people others are still searching for.
And the people who learn to turn AI into real value, rather than simply knowing its name, are likely to be the ones employers remember.
