Most frameworks for AI-driven talent reinvention are built on a Western assumption: that giving people access, training, and incentives will lead to organic adoption. But according to Marta Lajmi, Talent Reinvention Partner Lead, South-East Asia at Accenture, that assumption does not hold here.
“AI transformation across Southeast Asia follows the pace leaders set, not the pace at which tools are deployed,” she said.
In this region, employees look to senior leaders for permission, direction, and assurance that change is safe before moving forward.
Her perspective draws on Accenture’s Talent Reinventors research—the global study behind part one of this series—alongside the Wharton-Accenture Skills Index (WAsX), a labour-market benchmark published this January. The picture they describe is one where the same eagerness that should make the region a natural early adopter is being held back by what happens, or fails to happen, at the top.
In Singapore, Accenture’s research found that nearly half of employees (47%) cite a lack of leadership support as the single biggest barrier to upskilling with AI. For that reason, Lajmi argues, the top-down mandate is non-negotiable: senior leaders must own the redesign and set the vision for an innovation-led culture. But she frames it as a two-way effort—leadership commitment that empowers teams to redesign how they work from the bottom up. That bi-directional approach, in her account, is what separates genuine transformation from a simple technology upgrade.
See also: ‘Digital fluency’: What it is and why business leaders value it so much
Where the region diverges
What makes Southeast Asia distinct, Lajmi argues, is the proactive role governments are playing. She points to Singapore’s National AI Strategy 2.0, Malaysia’s ambition to become an “AI Nation” by 2030 and Indonesia’s National AI Strategy as signals that talent reinvention here is a shared enterprise between the public and private sectors. The practical implication for organisations, she says, is that they do not have to build AI talent pipelines from scratch—they can align with and leverage national movements already building that infrastructure.
The region’s demographics add to the case. Southeast Asia has a young, digitally native workforce, and the appetite to grow is high. In Singapore, Accenture’s research found that 79% of young employees prioritise skills development over their job titles, 85% expect to continuously renew their skills throughout their careers, and 81% say they are willing to be reskilled if their roles become automated. Two-thirds already use AI tools weekly.
Readiness and deployment, however, are not the same thing. The same research found that 46% of technology leaders have not begun redesigning job roles, and only 23% of employees trust their employer to act in their best interest when introducing AI. Without deliberate work redesign and visible leadership commitment, Lajmi warns, the region risks a “productivity trap”—where tools are deployed, but the work itself remains unchanged, leaving people unsupported.
Renegotiating the seniority contract
The harder structural problem, in Lajmi’s view, is that most HR systems across the region still reward tenure and role-based progression—and tenure here carries weight well beyond pay. It signals loyalty, confers authority, and underpins how many professionals read their own career security, particularly in Singapore, Malaysia and Indonesia. She describes it as a social contract around seniority, embedded not just in talent policies but in how teams operate and how decisions get made. Moving to a skills-based system, she says, asks organisations to renegotiate that contract—and “if you dismantle the old system before the new one is ready, you lose the people you need most.”
She argues that the goal is not to dismantle tenure but to refine it by clarifying which experience is actually worth more. This is where WAsX enters. The index found that the same skill can be worth markedly different amounts depending on the role: signalling strategic analysis is correlated with a validation lead’s salary—a technical execution role in pharmaceuticals—being over $10,000 lower, while a sales representative’s is nearly $8,000 higher. In this framing, the most valuable employee is not the one with the most experience but the one whose skills and experience best match the demands of a specific role.
Lajmi sets out three steps to make the transition workable. The first is diagnosis: using WAsX to identify which skills are in surplus, which are in deficit, and where real wage premiums sit at the role level, so that reskilling investment and pay can be aligned with the actual economics of skills.
The second is to reveal hidden pathways. She cites a Future Skills Pilot run by Accenture, Unilever, Walmart and the World Economic Forum, which found that when people self-report their skills, they identify an average of 11, but when AI analyses the same roles, the number rises to 34. The pilot also identified a 63% skills match between an inventory replenishment manager and an e-commerce manager—a transition, Lajmi notes, that most organisations would never have considered through traditional career planning, and one that could be bridged with reskilling in around six months. In a region where internal mobility is often underused, she argues that visibility is what gives mid-career professionals a credible future within the organisation.
The third is to make the value visible to employees—showing people what their skills are worth in dollar terms and where demand is growing. WAsX, she notes, points to rising value placed on judgment, coordination, compliance and domain expertise. “In this region,” she says, “delivery matters as much as strategy. People will move to the new system when leaders make the benefits visible and credible.”
The fluency gap
For Gen Z and millennial professionals reading this, Lajmi acknowledges that the anxiety in the market is real—but argues that the path through it starts with recognising the gap between widespread AI use and genuine AI fluency.
While 48% of young Singaporeans describe themselves as confident in AI, and usage rates are high, more than 80% report a beginner-level or no understanding of prompt engineering, AI ethics, process design and interpretive analysis. The system, in her words, is producing enthusiastic users who lack applied fluency—and it is that gap, she argues, that separates the average jobseeker from an indispensable hire.
Her recommendation is to adopt a skills-first mindset and become genuinely AI-fluent, which she says means going beyond another introductory course toward structured, hands-on experience. She points to national programmes such as the Young Talent Programme for AI in Finance and courses under the TechSkills Accelerator (TeSA) as ready-made paths, and offers a test for evaluating any of them: will this help me develop a skill I can apply in a real-world context tomorrow? If the answer is no, she says, keep looking.
The window to differentiate on applied AI fluency, in Lajmi’s view, is the next 12 months. The professionals who equip themselves now, she says, “will be the ones who are hardest to catch by 2027.”
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