163,000 workers, 37% training: Malaysia’s AI skills gap in focus

A government-commissioned study found that 24 per cent of Global Business Services roles in Malaysia are highly impacted by AI and 65 per cent are medium impacted. Nearly nine in ten GBS jobs are changing in some material way within the next three to five years. The same study put a number on the people involved: 59 per cent of the GBS workforce, around 163,000 employees, need upskilling to stay relevant in roles that are evolving faster than the job descriptions written for them.

That figure sits against a harder one. Business closures and downsizing have already cost Malaysia tens of thousands of jobs this year. The Human Resources Minister’s position has been measured: AI isn’t the primary driver of those losses today, and workers who build AI skills won’t be left behind. That’s reasonable. What it doesn’t settle is who’s responsible for building those skills, and whether companies are doing it.

The data suggests most aren’t. Only 37 per cent of organisations have active internal AI training programmes running, based on research AGOS Asia conducted with Roland Berger, published in September 2025. The other 63 per cent are leaving it to individuals or waiting for a better moment to invest. With 163,000 GBS roles already on a clock, that gap is a serious one.

It shows up in a specific place. The job descriptions companies are hiring against today were largely written before generative AI was a daily work tool. Most have been updated at the margins, a line about digital proficiency here, a mention of system experience there, and that has been treated as current. It isn’t. A job description built around a fixed set of tools is quietly signalling the wrong priorities to every candidate who reads it.

The bar isn’t that every person becomes a technologist. It’s that they have enough familiarity with the tools in their environment to work alongside them confidently, to know when an automated output needs questioning, and to contribute to conversations about how a process could work better. That’s a realistic expectation.

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But it doesn’t happen by accident, and it doesn’t show up in a job description that hasn’t been touched in three years.

The qualities that actually determine whether someone can work effectively alongside AI, learning orientation, adaptability, and willingness to question automated results, rarely appear as real evaluation criteria. They sit in a paragraph about culture, and nobody tests for them in the interview.

Three questions hiring managers can use now to surface whether a candidate has the mindset the next three years will require.

• One: “Tell me about a process you changed without being asked to. What prompted it, and what did you do?” This separates people who treat improvement as part of their job from those who wait for instruction.
• Two: “Describe a time you had to learn a new tool or system quickly. How did you approach it, and what would you do differently?” This distinguishes people who adapt by instinct from those who need a formal programme before they’ll move.
• Three: “How do you stay current with changes in your field? Give me a specific example from the last three months.” The three-month constraint matters. It makes vague answers visible immediately.

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Hiring is only half of it. The obligation runs in both directions. Rewriting job descriptions without investing in people already in the function creates a split: new hires arrive with the right profile while experienced team members find themselves measured against criteria they haven’t been supported to meet. That shows up in retention before it shows up anywhere else.

The same TalentCorp study names talent retention and development as one of its core recommendations for industry players 5i, not a nice-to-have. Russell Parry at AstraZeneca built that thinking into their modular AI training programme from 2023: “We have seen measurable gains in both productivity and retention since rolling out our modular training approach. People want to work where they are being invested in, and they want to work on things that feel like the future.”

Most companies are measuring productivity. Fewer are measuring whether their people investment is affecting whether people stay. The 163,000 figure is a policy problem and a company problem at the same time. What happens inside individual organisations, at the level of the job description, the hiring conversation, and the performance review, is still a corporate decision. One won’t solve the other.

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