Augmenting Intelligence: Shaping the Future of Work in South-East Asia

Artificial intelligence is disrupting not only the jobs traditionally classified as manual and routine, but also – and more profoundly – the specialised cognitive roles once seen as safe from automation. This threatens the traditional ladder of social mobility in both advanced and emerging economies. As AI reshapes both high-skilled and low-skilled jobs, the question becomes: how can societies strengthen the middle of the labour market?

This paper proposes a new strategy: an augmented-intelligence approach to job creation. Instead of simply automating tasks, this approach uses AI to enrich essential, practical work such as logistics, maintenance, care and customer service. These roles are not disappearing, but they remain low skilled and offer limited pathways for progression. By embedding intelligence into these jobs – and enhancing the opportunity for human judgement, adaptability and decision-making – we can elevate their value and rebuild a strong, inclusive middle.

Written as a collaboration between the Tony Blair Institute for Global Change and Singapore’s Institute for Adult Learning, this paper applies the augmented-intelligence lens to three South-East Asian economies, each with their own unique challenges:

• Singapore is an innovation leader but must confront the socioeconomic risks of automating high-skilled tasks and offshoring.

• Vietnam has strong ambitions for AI adoption, but its success depends on inclusive strategies and better coordination across sectors.

• The Philippines has laid out a comprehensive digital roadmap but needs targeted policies and infrastructure improvements to bridge existing digital divides.

The research adopted a participatory approach, engaging a wide range of stakeholders – including workers, employers, policymakers and global institutions – through interviews, focus groups and case studies. This method offers a nuanced understanding of AI’s real-world impact across industries and labour segments in the region.

To guide action, this paper outlines a four-part policy framework for augmented intelligence, focused on reforms across policy, the economy, the workforce and society. It also provides country-specific recommendations for Singapore, Vietnam and the Philippines, translating these principles into practical steps tailored to each country’s unique context.

While AI is a technological challenge, the real question is how to empower people and share its benefits widely. This paper offers forward-looking policy recommendations to foster inclusive innovation ecosystems, support small and medium-sized enterprises (SMEs), and prepare workers for human-machine collaboration. If South-East Asian governments take bold, people-centred action, they can unlock AI’s economic potential while advancing equity and social mobility.

AI is fundamentally reshaping global labour markets, creating a dynamic interplay between job creation, transformation and displacement. In previous industrial revolutions, the direct impact was concentrated on large numbers of lower-skilled jobs, from mechanisation in the 18th century to automation in the 20th century. Policy responses included raising literacy levels through basic schooling, expanding access to higher education and investing in upskilling enabled workers to gradually move into better paid jobs, often involving significant cognitive labour. This dynamic underpinned a promise of intergenerational mobility linked to economic development and the pursuit of greater levels of education to meet growing demand for cognitive labour.

Today, however, that pattern is shifting. As generative-AI capabilities have matured over the past two years and frontier firms enter the adoption phase, high-skilled work is poised to be disproportionately disrupted – a trend highlighted by global studies.^[_] As the dynamics of cognitive work itself begin to change, the very foundations of this promise are being called into question.

Generative AI symbolises a distinct aspect of this period of technological change. Its transformative role is part of a broader convergence of technologies – including cloud computing, advanced connectivity and increased processing power – that are collectively reshaping the supply of complex cognitive labour. Complex cognitive tasks, once considered beyond the reach of automation, are now increasingly enhanced by generative AI’s ability to rapidly analyse data, synthesise information and generate content – tasks traditionally carried out by highly skilled professionals who have honed their capabilities through extended periods of education and training. For instance:

• In software development, tens of thousands of JPMorgan Chase software engineers have experienced productivity increases of up to 20 per cent using a coding-assistant tool developed by the bank.^[_]

• In financial services, firms report average daily time savings of 57 minutes per employee from AI applications, which streamline tasks such as fraud detection and risk management.^[_]

• In customer-support roles, ride-hailing company Lyft reports an 87 per cent reduction in average resolution time for customer requests after deploying Claude.ai through its partnership with Anthropic.^[_]

Recent findings from the analysis of more than 4 million Claude.ai conversations for non-business users^[_] show that AI usage is concentrated in software development and technical writing tasks. While much attention has been placed on the risks of AI displacing low-wage workers through automation technologies such as autonomous driving and surveillance systems, the deeper and more enduring disruption may lie in the capacity of this generation of AI models to execute complex cognitive tasks – posing significant challenges to traditionally high-wage roles such as computer programmers, financial analysts, consultants and other knowledge-based professionals.^[_]

Indeed, the International Monetary Fund (IMF) has predicted that AI will impact nearly 40 per cent of jobs worldwide, with exposure rising to 60 per cent in advanced economies due to their high concentration of skilled roles.^[_] This aligns with other global studies suggesting that “knowledge work” is the most susceptible to automation, as discussed in an eight-country study on the Digital Futures of Work: Reimagining Jobs, Skills and Education for a Digital Age, in TBI’s previous piece on Artificial Intelligence and the Future of Work: A Focus on Asia, and in other publications.^[_] While the disruptive impact of AI will be most pronounced in advanced economies, emerging economies will also face significant effects. Entry-level and mid-level professional jobs, such as those in accounting, finance and marketing, may be at risk. These roles have historically been crucial for lifting large segments of the population into the middle class.

As the same IMF study suggested,^[_] there are further risks. As AI-technology adoption rises, the supply of cognitive work performed by machines will likely see a corresponding increase. Should such an increase in supply occur without a parallel increase in demand, the economic premium historically associated with human cognitive skills may decline. In other words, as more cognitive work is performed by intelligent software, employers are less willing to pay premium wages to workers for tasks that, with the help of AI, a much smaller number of human staff can carry out cheaply, quickly and reliably.

This changes the economic calculus of cognitive roles and underscores the importance of finding new ways to create value in an era of apparent cognitive abundance. But the implications extend beyond wages: this shift also carries deeper social consequences.

Many existing models of social mobility are built on the assumption that cognitive labour – especially in professional, white-collar fields – offers a reliable pathway for upward socioeconomic movement. Education systems, career-progression structures and wage expectations are typically anchored in the belief that investing in cognitive skills yields higher returns in the long term. However, as AI systems increasingly take on tasks historically performed by knowledge workers, this foundation is being eroded.

Entry-level roles would be particularly vulnerable if simpler tasks were automated en masse, limiting the opportunities for new graduates and early-career professionals to gain essential employment experience and other training opportunities.^[_] In turn, this could lead to long-term skill gaps and a narrowing of the traditional pathways into higher-paying careers – undermining both individual advancement and broader social mobility.

As AI reshapes the job landscape, it is likely to challenge the idea that cognitive careers are the primary pathway for upward mobility. The issue is not a collapse in demand for cognitive work per se, but a dramatic shift on the supply side as such tasks could be performed cheaply and efficiently by AI tools. Therefore, existing assumptions that cognitive work (and the preparation for it) provide upward mobility are being upended. As traditional models of predictable education and skills pathways, linear career progression, and publicly funded, employment-based safety nets might no longer ensure equitable socioeconomic transitions, there is an urgent need for a fundamental shift in how policymakers approach education and skills development, economic growth and social mobility in the age of AI.

It must be recognised that this disruption extends far beyond the labour market and the economy: it strikes at the very heart of policymaking, elevating the issue beyond a question of mere technology. Any effective response must be no less than a complete re-evaluation of a state’s people strategy. This includes how the state thinks about the human capital at its disposal, how it helps to shape the labour market to deliver positive economic and social outcomes, and how it reacts when fundamental assumptions no longer apply. It is the people strategy that will determine whether a state succeeds or fails.

Rethink Policies to Unlock AI Dividends for All

Relying on market forces alone will not sustain socioeconomic mobility in an era when highly skilled, knowledge-intensive jobs are becoming scarcer. Nor will a knee-jerk response that seeks to restrict the use of technology in order to maintain the status quo. Government action becomes a crucial lever to actively stimulate demand by targeting practical, human-performed jobs that are embedded with AI – an idea at the heart of what can be described as an augmented-intelligence approach.

An augmented-intelligence strategy requires a more imaginative approach to job reinvention: one that shifts focus towards creating value through applied work, such as the job roles of technicians, nurses and allied health-care professionals. It also means upgrading essential but undervalued roles – in sales and customer service, logistics, maintenance and care – that are not disappearing, but remain stagnant despite a more educated workforce.

Such deliberate policy action ensures that human-performed work retains value, secures equitable socioeconomic mobility and safeguards economies against the erosion of their educated workforce’s value.

In this regard, AI fundamentally disrupts conventional policy thinking about the future of work. For decades, policy initiatives have been built on the assumption that high-skilled, knowledge-intensive work is the key driver of socioeconomic mobility. However, AI challenges this paradigm through its ability to both automate and reshape cognitive work. As described in the publication Digital Futures of Work: Reimagining Jobs, Skills and Education for a Digital Age, it is “the smartest workers who are now contending with the smartest technologies”.

Bold reimagination is required to manage this disruption. It demands a forward-thinking approach to shaping where future jobs will emerge; not just any jobs, but those that sustain the talents and capabilities of a highly educated workforce. This is vital, as higher-education reforms in both advanced and emerging economies over the past 30 years have produced a significant number of diploma- and degree-holding graduates with elevated aspirations.

AI dividends exist and can be unlocked in this period of disruption, but they must be pursued through non-standard approaches. Unlike robotics or infrastructure-heavy systems that require significant upfront investment, many modern AI tools, especially those delivered via cloud-based APIs or platforms, require minimal on-premise infrastructure and scale easily.

That accessibility is partly due to the current business models of leading AI providers, which have absorbed much of the capital burden upfront. For example, companies like OpenAI and Anthropic have taken on the high costs of model training and compute infrastructure, with the expectation of future returns through subscription pricing and market valuation. As a result, emerging economies can access powerful AI capabilities for a fraction of the cost.^[_] AI thus offers a more level playing field, particularly in terms of access and adoption, than past technologies could. While AI is about technology, it is important for policymakers to remember that this revolution is ultimately about people and how they can be empowered in a manner that ensures the benefits of AI are shared more equitably across the whole of society.

AI dividends will not be secured simply by investing in AI technologies or deploying them at scale. A conventional AI strategy, one focused primarily on efficiency gains through automation and cost reduction, risks shrinking the availability of jobs that have traditionally served as pathways to mobility, especially if it displaces or deskills knowledge workers without creating new opportunities elsewhere.

In contrast, the key to unlocking AI’s full potential lies in a deliberate people strategy of augmented intelligence. This approach means going beyond indiscriminate automation of existing cognitive work, for example by rethinking organisational processes and business models to maximise productivity and quality benefits. At the same time, it involves elevating the cognitive content of low- and mid-skilled jobs, transforming these into more attractive opportunities by integrating human expertise with cutting-edge technology. This dual approach ensures that AI delivers inclusive and sustainable benefits, not just productivity gains but broader economic and social dividends.

Research by Anthropic found that AI usage varies among non-business users: 43 per cent rely on AI to fully automate tasks, while 57 per cent use it to enhance their work, treating AI as a collaborative tool that amplifies their capabilities.^[_] This distinction is significant for both workforce dynamics and productivity, as several research papers have highlighted that, when AI functions as an augmentation partner rather than a substitute, it boosts productivity while preserving individuals’ meaningful engagement with their work.^[_] However, despite the demonstrated benefits of augmentation, prevailing economic incentives often steer decision-makers towards automation, revealing a deeper tension between short-term efficiency and long-term societal value.

A deliberate augmented-intelligence strategy is an exercise in political will. Global capital markets favour cost-efficient, low-risk automation over more complex and perhaps yet-unproven AI augmentation strategies.^[_] The former approach may boost efficiency and lower costs in the short term but risks hollowing out knowledge work and firms’ capacity without creating a sufficient amount of alternative, professionally satisfying work that can drive future productivity and innovation. The eight-country study on Digital Futures of Work: Reimagining Jobs, Skills and Education for a Digital Age, jointly written by one of this paper’s co-authors, Sahara Sadik, explores these tensions in greater depth.

A case in point is fintech firm Klarna, which prioritised cost-efficient automation – even at the expense of displacing human jobs. To reduce labour costs, Klarna replaced around 700 customer-service staff with AI-powered chatbots. While the move cut costs and improved some metrics, customer satisfaction declined due to the lack of personalised support. As a result, Klarna recalibrated its approach and reinvested in human customer-service agents to balance efficiency with quality.^[_]

In this context, policymaking stewardship becomes critical to avoid mirroring conventional market logic and chart a different course: one that leverages AI for inclusive productivity, job quality and social resilience for long-term economic and social gains.

South-East Asia offers valuable insights on the intersection of AI disruption and policymaking. The region is emerging as a pivotal player in the global AI landscape, with six out of ten Association of Southeast Asian Nations (ASEAN) economies having already launched national AI strategies. Countries in South-East Asia are also ramping up their digital infrastructure to attract foreign investment and expand their digital economies, setting the stage for AI-driven economic transformation.

We selected three South-East Asian countries for analysis in this report, each reflecting different opportunities offered by AI: Singapore, an advanced economy aggressively embracing the technology; Vietnam, an emerging economy using AI to leapfrog its development; and the Philippines, a newly industrialised economy taking a gradual and comprehensive approach to adoption.

In each of these countries, the strengths of their policy approaches are clear, but so are the limitations where policies fall short of recognising the potential of an augmented-intelligence strategy. As a result, the countries are not fully capitalising on AI’s transformative potential and are missing out on the opportunity to reap its dividends at scale.

Singapore: Confront the Socioeconomic Risks of Automating High-Skilled Tasks and Offshoring

Singapore is a global leader in AI innovation, punching well above its weight through proactive government policies that are shaping the global AI landscape. The country aims to develop AI specialties to address key challenges such as population health and climate change. Its vision is for AI to serve as a great equaliser, empowering individuals and businesses with the capabilities and resources they need to thrive in an AI-enabled future. The country’s aspiration is to empower “individuals, businesses, and communities to use AI with confidence, discernment, and trust”.^[_]

To support its AI vision, Singapore is building a robust AI ecosystem through major investment in talent development, research centres and responsible AI adoption across industries. Singapore is also actively attracting global AI expertise to cement its status as a leading hub for AI innovation and deployment, with a focus on strategic sectors such as health care, smart cities and education, while it maintains a strong emphasis on ethics and public trust in AI technology.^[_]

In the private sector, corporate giants are matching the ambition of the Singapore government with large investments of their own. Companies such as Amazon, Google, Microsoft, Infineon and Hyundai have all announced large-scale programmes dedicated to accelerating AI innovation and adoption in Singapore.^[_]

While Singapore’s strategy positions it as a leader in AI innovation and ecosystem development, it has yet to fully confront the fundamental shifts AI is bringing to the nature of work and the future of social mobility. As AI increasingly automates knowledge tasks, the number and quality of such jobs will not be available at the scale they once were. The government’s focus on developing centres of excellence and attracting global expertise is commendable, but it overlooks the growing need for policies that prepare the high-skilled workforce for a world where the supply-demand curve for large swathes of cognitive jobs looks very different. The ambition to equip people and businesses with AI capabilities must be matched by strategies that focus on re-skilling and adapting to a new economy that no longer relies heavily on knowledge work as we know it today. In fact, if Singapore’s AI strategy is solely about scaling innovation in high-tech sectors, it risks leaving behind workers whose jobs will inevitably be redistributed to economies with lower labour costs through AI-enabled workflow redesign.

Data from the national skills survey, conducted in 2021,^[_] show that complex, thinking jobs were not protected from technological disruption. They were just as likely to leave the Singapore economy as non-complex jobs. Crucially, technological change had an uneven impact across occupational groups. Compared with managerial roles, professional jobs were weakened: the work of professionals became as complex as that of managers, yet professionals reported having less decision-making power over how to carry out their work.

For other occupational groups, such as technicians and clerks, job quality did not improve with technological change. These roles were less likely to be impacted by technological advancement, but when they were, the changes did not lead to greater task complexity or autonomy compared with managerial roles.

Looking ahead, the survey projected that professional jobs have the highest likelihood of being digitally offshored, as they are increasingly performable remotely. This reflects a broader business reality: as AI enables the unbundling of knowledge work, cognitive tasks can be outsourced by highly credentialed labour elsewhere in the region at lower cost.

Indeed, the Singapore government’s emphasis on AI as an equaliser might falter if it does not recognise the socioeconomic challenges of AI-induced changes to the labour market. While investment in talent development and responsible AI adoption is critical, Singapore’s future prosperity hinges on a broader policy framework that ensures AI benefits are distributed equitably. Simply building AI hubs and research centres will not be enough if the impact of AI on labour markets is not addressed head on. The strategy must expand to include policies that actively create new job opportunities and pathways for its highly educated workforce. Without this, the promised AI-driven prosperity could become concentrated in the hands of a few, exacerbating inequality rather than promoting shared growth.

Weaning the Singapore economy off its reliance on transnational corporations is becoming increasingly crucial, as these entities may no longer deliver socially desirable jobs at scale in the way they once did.^[_] With soft capital markets increasingly averse to high-risk investment, global corporations are focusing more on automation and consolidation, and are likely to reduce the number of entry-level and mid-level jobs they offer in Singapore.^[_] As a result, local businesses and the small-and-medium-sized-enterprises (SME) sector must take on greater importance in driving economic growth and quality job creation.

Insights from Singaporean business owners, workers, researchers, civil servants and policymakers interviewed in this study indicate frustration that a lot of companies are really talking about “small ‘i’ (innovation), which is on being more efficient. Not many are rethinking the big systems, the big ‘I’”. In finance, for example, banks aiming to be ‘smart’ are placing focus on automating processes to cut costs and improve outcomes (that is, “small i”) as opposed to having the appetite and imagination needed to augment jobs (that is, “big I”).

Stakeholders stressed the need for a more curated strategy that could identify how certain jobs, such as zookeeping, could be augmented using AI. Such a strategy could open up new categories of niche jobs that are not easily offshored.

There is growing opportunity to generate new forms of value in tradeable sectors through customised, AI-based solutions. These roles tend to require human participation and are less susceptible to automation or outsourcing. Stakeholders pointed to several examples, including:

• the use of social robotics to support dementia patients through human-like interaction.

• AI-enabled green solutions for urban landscaping, tailored to Asian environmental conditions.

• AI-driven systems for circular-economy logistics and local waste optimisation.

These applications not only support sustainable innovation but also open up skilled, context-specific work – roles that rely on human judgement and collaboration with intelligent systems.

Singaporean stakeholders interviewed for this paper recognised that being at the frontier now presents the country with a profound challenge: navigating a future for which there is no clear precedent. It is harder to see “what bets can be taken”. Singapore is seen as “careful”, whereas it needs to do things that are “special”. It is “not good at making things up”, yet that is precisely what is needed. Regional competition is intense, so it is crucial that Singapore ups its game.

Meanwhile, signs of overeducation are emerging. Education leaders interviewed in this study highlighted complaints from some architecture graduates who feel “overtrained” in using advanced design software that is not yet widely adopted by industry. Additionally, while Singapore’s SkillsFuture initiative is ambitious, its offering remains standardised compared to the more tailored, work-integrated learning models found in high-performing innovation economies. Stakeholders highlight that the challenge in navigating labour-market disruptions is not a simple case of acquiring skills; it’s about flexibility in work identity. They say that without the ability to adapt skills, experience or knowledge, navigating the changing job landscape becomes incredibly difficult. One promising approach is a professional-development system built around flexibility and dynamic interaction. It encourages adaptation, reflection and practical experience in new environments – not just conventional upskilling through courses.

Indeed, the ability of Singapore to foster a more diverse job market beyond reliance on professional jobs as the source for social mobility is crucial. AI’s ability to unbundle professional jobs into narrow tasks and offshore them to other regions makes this challenge particularly urgent. For example, a social-media manager’s graphic design and copywriting tasks could be offshored to digital-labour platform workers located in lower-cost regions, an arrangement that would provide the same outputs at lower costs. This makes alternative job creation through augmented intelligence crucial. Singapore’s policymakers must evolve their AI strategy to a higher-order focus on augmentation, adopting a more distributed approach that blends AI with human capabilities across sectors – whether it’s social robotics in elder care, tree doctoring or emergency-response roles augmented by real-time decision support. The way in which Singapore creates AI-augmented niche sectors and the jobs within them will determine the future of the Singapore story.

Vietnam: Success Depends on Inclusive Strategies and Better Coordination

Vietnam has undergone rapid economic transformation to emerge as a dynamic middle-income country in South-East Asia. With a vision to achieve high-income status by 2045,^[_] the country is pursuing a sustainable and inclusive growth strategy, pledging to reduce methane emissions by 30 per cent, halt deforestation by 2030 and reach net-zero carbon emissions by 2050.^[_] AI and industry 4.0 technologies, such as internet of things, AI-related adoption and additive manufacturing, are central to this ambition. Vietnam is actively working to enhance digital infrastructure, industrial productivity and human capital to maintain competitiveness in a rapidly evolving global landscape.

As part of its national industrial policy,^[_] Vietnam is prioritising high-tech industries to increase value creation and labour productivity. Industries such as automotive, mechanical engineering and electronics are expected to drive significant growth, supported by incentives aimed at strengthening related sectors and reducing reliance on imports. AI plays a crucial role in this industrial transformation: projections indicate a 12 per cent increase in GDP by 2030 due to AI adoption, particularly in supply-chain management and marketing.^[_]

Vietnam’s national AI strategy aims to position the country in the top four ASEAN countries and the top 50 globally in AI research, development and application by 2030.^[_] Key goals include establishing ten reputable AI brands, developing three national big-data and high-performance computing centres, and launching 50 open data sets to support AI research. Investment in AI computing capabilities has also gained momentum, with institutions such as FPT University partnering with NVIDIA to open a factory equipped with supercomputers,^[_] reinforcing Vietnam’s AI-research ecosystem. These initiatives have propelled Vietnam to fifth place in South-East Asia in the 2024 Government AI Readiness Index,^[_] underscoring its progress in digital transformation. AI’s impact on the labour market remains a double-edged sword. While automation threatens routine jobs – especially in manufacturing and back-office operations – AI is also expected to create new opportunities in high-skill fields such as software engineering and advanced manufacturing. In Vietnam, this shift reveals a critical tension: even as AI increases the effective supply of cognitive labour by automating knowledge tasks, the country continues to face a real shortage of human talent that has the skills to guide, complement and govern these systems. The country has an estimated annual shortfall of 150,000 to 200,000 engineers and programmers,^[_] and the shortage of AI talent is particularly acute: currently, there are only around 300 AI experts in the workforce.^[_] To bridge this gap, the government has partnered with companies such as Google to expand training programmes and scholarships.^[_]

AI development is supported by Vietnam’s central leadership, as reflected by the recent merger of the Ministry of Science and Technology and the Ministry of Information and Communications to form a new ministry that oversees AI and digital-transformation efforts. But the government can go further to improve inter-agency coordination across other ministries, such as education, law and industry, to realise more effective implementation of the country’s national AI strategy. There is also potential for policymakers to adopt a more proactive stance on policymaking to focus on the long-term economic and social value creation that could be driven by AI, rather than on short-term disruptions.

In addition, Vietnam faces structural challenges in capturing the full value of AI-driven economic growth. Many SMEs and rural enterprises struggle to access the technology, exacerbating economic inequalities between urban and rural areas. While foreign direct investment plays a vital role in industrial development, local firms still depend heavily on foreign technology, which limits domestic value capture. In 2023, the share of locally added value in manufacturing declined by 0.37 per cent,^[_] reflecting Vietnam’s continued reliance on imported components and fragmented supply chains. The government is actively addressing these challenges through policies that incentivise domestic innovation and encourage foreign firms to transfer AI knowledge and technology to Vietnamese enterprises.

Insights from stakeholders highlight that industries such as textiles, human resources and manufacturing have operational and creative demands that significantly differ from tech-driven sectors. Generic AI tools often overlook these specific needs, making them less effective in solving industry-specific problems – from quality control in manufacturing to fostering creativity in design-focused sectors. As one stakeholder explained, “we need to create sector-specific guidelines [...] for logistics, aviation, health care and so on [...] A one-size-fits-all approach won’t work, as each sector has its own specific needs.” This reinforces the need for tailored AI tools designed to address the unique requirements of non-tech sectors, helping to reduce operational inefficiencies, optimise workflows and enable more innovative, context-specific solutions. In turn, this helps businesses unlock productivity gains and overcome persistent challenges that hinder their global competitiveness.

Indeed, there is too much focus on the ICT sector rather than on the broad-based penetration of AI across various industries. This can be attributed to Vietnam’s digital divide, which remains a pressing concern as rural areas face limited access to AI education, affordable devices and reliable internet connectivity. Addressing this issue requires more than just targeted investments in digital infrastructure and training; it demands a complete reimagining of how rural communities can actively participate in the AI-driven economy.

Vietnam’s approach aims for fast growth by concentrating on ICT at the expense of other sectors (for example, textiles and agriculture) that make up its economy. As one stakeholder noted, “just like my [electronics] company, when they started applying AI in the production chain, a large number of staffs were cut down. As a result, this will lead to job redundancy and affect the development of society.” The comment reflects growing concerns that that rapid AI development is not being matched by efforts to stimulate sustainable demand for cognitive roles beyond mere automation. Given AI is rapidly expanding the availability of cognitive services, wages for traditionally secure cognitive roles could also stagnate or decline. To sustain its socioeconomic-mobility goals, Vietnam should explicitly create and incentivise AI-augmented jobs, particularly in sectors such as textiles and agriculture, enhancing the value and resilience of cognitive skills in these industries. Vietnamese policymakers should therefore prioritise an augmented-intelligence strategy that ensures that AI’s benefits extend to all segments of society. Given the relatively low cost of AI technologies today, the potential to uplift disadvantaged communities is significant. However, achieving this will require a more inclusive, holistic approach that ensures no one is left behind.

The Philippines: Bridge Infrastructure Gaps to Address Digital Divides

The Philippines has made remarkable progress towards economic modernisation, positioning itself as one of South-East Asia’s fastest-growing economies. Under the Philippine Development Plan 2023–2028, the country aims to reinvigorate job creation and reduce poverty, with AI and digital transformation playing a central role in driving inclusive and sustainable growth.^[_] The Philippine government has committed to leveraging AI and industry 4.0 technologies to address societal challenges, boost economic productivity and create high-quality jobs.

The country’s National AI Strategy Roadmap 2.0 outlines a comprehensive plan for integrating AI into key industries, fostering economic growth and upskilling the workforce. Central to this roadmap is the Philippine Skills Framework, which was updated in 2024 to address AI-related skills development. This framework aligns educational outcomes with labour-market demands, ensuring that workers acquire the necessary competencies to thrive in AI-driven industries.

The government has recently taken significant steps to establish dedicated institutions for AI. The Education Center for AI Research, which was launched by the Department of Education, is currently prototyping use cases to improve the department’s processes (for example, optimising the distribution of education vouchers).^[_] The National Innovation Council, headed by the Philippine president, has established an inter-agency AI think-tank^[_] to inform AI policies and enhance the country’s global competitiveness. These efforts are complemented by active campaigns to attract foreign investment in high-tech sectors, with the broader goal of building an innovation-driven economy.

However, a more unified AI policy and a clear action plan are still needed to address infrastructure gaps, skills mismatches and policy fragmentation. These remain key challenges as the country seeks to establish itself as a regional leader in AI adoption through the country’s robust National AI Strategy Roadmap and increased collaboration between the public and private sectors.

AI is expected to be a major driver of economic transformationin the Philippines, with generative AI projected to contribute 2.8 trillion Philippine pesos ($50.7 billion) to the economy by 2030.^[_] The National AI Strategy Roadmap focuses on seven key areas, such as improving data access, upskilling the workforce, fostering ethical AI ecosystems and accelerating AI R&D.

Despite these advances, the Philippines faces significant challenges in scaling AI adoption, including high investment costs, limited government incentives and slow adoption outside major urban centres. Stakeholders in the Philippines shared concerns around the need for greater economic inclusion, as SMEs and rural enterprises often lack the resources to adopt AI technologies, which further exacerbates existing inequalities. Stakeholders suggested having a “support system” where incentives and subsidies are tailored to the unique needs of SMEs. In addition, policies should address the urban-rural digital divide by expanding access to affordable digital infrastructure, such as connectivity, and training programmes.​

To strengthen its position in the global AI economy, stakeholders suggest that the country should pivot away from low-end AI tasks, such as data cleaning, and develop more homegrown digital solutions as competitive national exports. Stakeholders emphasised the need for stronger government support, clearer regulatory frameworks and increased funding for AI research to accelerate adoption and ensure long-term competitiveness.

Workforce readiness remains a key challenge in the country. While the Filipino workforce is highly adaptable, persistent skill mismatches and wage stagnation pose barriers to AI-driven transformation. Approximately 40 per cent of workers are overqualified for their jobs,^[_] while a separate 40 per cent lack basic ICT skills,^[_] making it difficult for them to benefit from AI-driven opportunities. Furthermore, the country’s learning crisis adds to the challenge, with a staggering 18 million high-school graduates in the Philippines found to be “functionally illiterate”.^[_]

Addressing these workforce challenges will require more than traditional upskilling efforts. Facing a future economy that is looking increasingly digital, and with the traditional social-mobility ladder being upended in the age of AI, urgent reforms are needed to promote a strategy of augmented intelligence: one that focuses on supporting and elevating low- to mid-skilled workers. Targeted upskilling and education initiatives to transition more workers into knowledge-based positions are especially crucial for countries like the Philippines that have a significant proportion of the workforce in the service, agriculture and manufacturing sectors.

In addition, the country’s labour market is heavily reliant on informal employment, complicating efforts to implement structured upskilling programmes. Such persistent mismatches between cognitive-skill availability and job opportunities may worsen as AI continues to proliferate, further lowering the premium on cognitive skills and exacerbating wage stagnation.

In parallel, public perceptions and regulatory readiness will also shape how AI is adopted in the Philippines, where AI is perceived positively, as a tool to improve work efficiency and operations, by private-sector groups, national-government agencies and local-government units. However, despite the Congress’s proactive approach pushing an AI regulatory framework to mitigate AI risks, our research with workers revealed that their optimism is accompanied by ethical and security concerns. Data breaches, algorithmic bias and a lack of transparency in AI decision-making remain persistent concerns that undermine public trust and limit AI adoption.

The IMF estimated that nearly four in ten jobs in the Philippines will be affected by AI, with more than half of these roles expected to be augmented rather than replaced.^[_] While this presents an opportunity for workforce adaptation, policymakers must ensure that AI-driven transformation does not disproportionately disadvantage lower-skilled workers.

For example, business-process outsourcing (BPO) is a key sector in the country, employing about 1.82 million workers in more than 1,000 facilities such as call centres, knowledge-process outsourcing units and back offices, generating more than $38 billion a year (or 8.5 per cent of GDP).^[_] While digitalisation can create jobs and raise productivity and competitiveness, there are concerns around generative AI’s potential to displace jobs in the BPO sector. One stakeholder captured this fear from the frontline: “There are some clients [who] want to replace their actual salespeople. But of course, as a human [...] you need to pitch not really on the script, but you can pitch the way you can relate [to customers because] you have emotion. AI doesn’t have an emotion.”

To effectively address skill mismatches and wider concerns around job security, policymakers should not only increase digital literacy but actively stimulate and support demand for new AI-augmented roles. As one stakeholder explained, however, the challenge isn’t a lack of talent – it’s a lack of opportunity: “The talent is here in the Philippines, but the problem is there aren’t enough opportunities locally ... Our issue in the Philippines is the lack of investment, particularly in R&D. If you look at places like Singapore, it’s a smaller city, but they are far more advanced. They have significant investment in R&D, with people willing to fund technology development and the creation of new products.”

Addressing these challenges will require a balanced approach that prioritises job creation, workforce upskilling and equitable AI adoption.

Stakeholders emphasised the need for a shift towards more proactive policies in which governments do not merely mitigate risks, but actively shape AI adoption to create economic and social value. This shift will include fostering public-private partnerships, setting clear regulatory frameworks, and incentivising local innovation ecosystems and entities to flourish.

From Artificial Intelligence to Augmented Intelligence

To realise the transformative potential of AI, organisations first need to prepare for an initial drop or stagnation in productivity^[_] – a pattern economists call the “productivity J curve” – before significant long-term gains begin to materialise. Insights from the World Economic Forum highlight that the successful deployment of generative AI depends as much on people as on the technology itself – if not more. To reap AI dividends, organisations will need to first understand the technology’s value for their specific needs, identify appropriate use cases and thoroughly test solutions. By having a people-centred AI strategy that aligns strategic goals with employee needs, organisations can then create environments where generative AI adds value, enhances job quality and allows employees to engage in more meaningful work.^[_]

Similarly, workplace consulting firm Gallup also emphasises that successful AI integration requires a long-term management strategy that focuses on human-AI collaboration over mere automation. It argues companies should “play the long game” with human-AI collaboration, as AI is most productive when supporting employees, not replacing them.^[_]

Therefore, AI policymaking must take a similarly proactive lead to pursuing a people-centred AI strategy by steering markets towards productivity gains and ensuring AI’s benefits are more equitably distributed. Just as organisations must identify relevant use cases and align their strategies with broader goals, policymakers must focus on co-creating knowledge-intensive industries that leverage AI to improve decision-making, optimise production processes and elevate workforce capabilities. This approach avoids a zero-sum game where labour loses to capital.

The goal of greater efficiency should not come at the cost of workforce displacement. Instead, it should aim for better outcomes through workforce augmentation – enhancing human productivity and decision-making while ensuring that economic gains are distributed equitably. This shift is particularly crucial for South-East Asia’s large informal workforce, where nearly 80 per cent of workers operate outside formal employment structures, a figure that is higher than both the Asia-Pacific and global averages.^[_] Those workers, who rely on gig work, micro-tasking platforms and low-end service jobs, are highly vulnerable to AI-driven disruptions if policies do not proactively address their needs.

Additionally, given AI’s disruptive impact on cognitive labour markets – in which increased supply pushes down the economic premium for cognitive work – policymakers cannot afford passive adaptation. Instead, they must actively foster the creation of new roles that allow AI to complement rather than substitute human capabilities. Through targeted policy interventions, governments can stimulate sustained demand for cognitive skills, preserve the economic value of cognitive work and ensure these benefits reach a broader, more inclusive segment of society.

Rather than displacing workers, augmented intelligence offers a way to maximise benefits by enabling humans and machines to complement one another, creating a workforce with superior capabilities compared to either humans or AI alone. For example, an on-site technician working in manufacturing could be augmented by an AI assistant, improving the accuracy, efficiency and effectiveness of their decision-making. Similarly, micro-tasking workers – who are currently engaged in data annotation and AI training – could transition to higher-value roles through AI-assisted upskilling initiatives.

To realise the transformative potential of AI, policymakers need to create the right conditions for human-machine collaboration – augmented intelligence – to flourish. This requires more than technological progress; it requires strategic action across four domains.

• Policy: Develop transformative national AI strategies for augmented intelligence.
  Governments must establish comprehensive, forward-looking national AI strategies that explicitly foster human-machine collaboration. This includes setting national goals for augmented intelligence, aligning regulatory frameworks with ethical standards and creating agile oversight mechanisms that can evolve with the pace of technological change. Strong, centralised leadership will be critical to ensure trust, safety, accountability and coordination across sectors and regions.

• Economy: Equip businesses to adopt AI in ways that enhance – rather than replace – human work.
  Rather than using AI to replace human labour for short-term financial gains, governments should support businesses in leveraging AI to augment human capabilities. This means investing in the know-how for augmented intelligence, alongside building digital infrastructure, incentivising adoption among SMEs, and fostering innovation ecosystems that align with both productivity and equity goals. Public-private partnerships and inclusive access to AI tools will help economic actors – from startups to large corporations – leverage AI for value creation and meaningful workforce transformation.

• Workforce: Build broad-based AI literacy and capabilities to enable inclusive workforce transformation.
  Countries must go beyond training AI specialists and promote AI literacy across the entire workforce. This includes equipping workers with the skills to work alongside intelligent systems, adapt to changing roles and participate in AI-driven innovation. Education systems, vocational pathways and lifelong learning must be reimagined to emphasise both technical skills and the “4Cs” – critical thinking, creativity, communication and collaboration – outlined in Ending the Big Squeeze on Skills: How to Futureproof Education in England. Public-private partnerships would play a crucial role in this process, ensuring that training programmes remain aligned with evolving industry needs and that workers are prepared for roles in AI-augmented environments as they emerge.

• Society: Foster inclusive and human-centred AI ecosystems.
  AI should serve all members of society. Governments must cultivate inclusive ecosystems where communities are empowered to help shape how AI is used. Similar to Singapore and Finland’s foresight sessions, such initiatives ensure that bottom-up insights inform national AI policies. Supporting citizen-led projects can also foster a sense of ownership and ensure AI is used to deliver socially meaningful outcomes.

At a country level, these high-level principles could be applied in the following ways:

Singapore:

• Policy:

  • Develop a National Augmented-Intelligence Strategy that anchors intelligent augmentation as the national direction for AI initiatives to foster machine-augmented human intelligence.

  • Build operating know-how for integrating augmented intelligence into job design, including voluntary guidelines, such as checklists and risk markers, to signal the importance of preserving human cognitive development in AI-enhanced workplaces and to discourage the overreliance on AI for “cognitive offloading”.

• Economy:

  • Identify global and local players who have an appetite to use intelligent augementation to create value-added growth, and create attractive tax cuts and national recognition for such enterprises. Identify local SME players who are primed for value-creating growth.

  • Establish sandboxes to allow enterprises to pilot workforce augmentation in a controlled, low-risk environment that enables experimentation without immediate compliance pressure. Insights from these pilots should be systematically captured and embedded into Singapore’s industry- and job-transformation maps to inform sector-level change.

• Workforce:

  • Nurture AI literacy across the workforce by embedding co-intelligence competencies in sectors such as health care, law and education.

  • Pivot the SkillsFuture initiative, shifting from mass training programmes to deeper, more strategic approaches that equip the workforce with the competencies needed to perform highly skilled roles that add value in an AI-enhanced economy.

• Society:

  • Seed “Generative Dialogues”, to encourage ground-up participation in AI policymaking to trigger a creative reimagination of the future.

  • Develop and scale operating methods that embed intelligent augmentation know-how into everyday workplace practices and social interactions.

Vietnam:

• Policy:

  • Create a high-level AI advisory body comprising public, private and international stakeholders to guide Vietnam’s AI-governance framework, with a specific focus on steering AI development and deployment towards augmenting human capabilities.

  • Strengthen coordination among different public stakeholder agencies, such as the ministries of finance and law, by establishing a dedicated steering committee under the Government Office that has a clearly defined mandate to implement and coordinate national AI initiatives. It could provide support on issues such as enhanced productivity, workforce resilience, wider public-service delivery and more.

  • Set aside a dedicated AI budget to support national AI initiatives that prioritise intelligent augmentation and pilot public-private partnerships focused on transforming low- and mid-skilled work.

• Economy:

  • Establish a scalable, multi-stakeholder panel of AI experts from the technology, education, labour, business and civil-society sectors to advise how AI can be leveraged to augment human capabilities across sectors.

  • Encourage companies to establish clear, structured AI career pathways for workers that emphasise progressive skill development, mentorship and role redesign that empowers workers to grow into higher-value roles focused on human-AI collaboration.

• Workforce:

  • Support the private sector to adopt change-management frameworks that provide practical guidance on job redesign, human-AI task allocation and retraining to augment workers and ensure none are left behind.

  • Partner with training institutes and private-sector companies to design AI training programmes that are modular, stackable and tailored to real-world industry needs. These programmes would focus on using AI tools to enhance workers’ judgement, productivity and creativity.

  • Prioritise AI integration across all education levels to equip students and workers with the ability to work alongside AI systems by, for example, interpreting outputs, applying critical thinking and contextualising AI tools in real-world decision-making.

• Society

  • Promote high-impact community initiatives that invest in digital inclusion – such as affordable internet and access to digital devices – to ensure rural and underserved communities can participate equitably in AI-powered economies.

The Philippines:

• Policy:

  • Mandate the newly established AI think-tank under the National Innovation Council to steer national augmented-intelligence initiatives across the bureaucracy.

  • Develop and operationalise augmented-intelligence outcomes under the recently harmonised National AI Strategy.

• Economy:

  • Support trade bodies to develop sector-specific AI guidance.

  • Work with trade bodies on innovation initiatives to encourage companies to build and test their own AI solutions and accelerate AI adoption and R&D capacities across various sectors.

• Workforce:

  • Establish a one-stop resource portal to guide workers towards relevant information on AI-related jobs and training – including competency frameworks, upskilling roadmaps, job listings and available training programmes.

  • Expand existing national “skilling-to-employment” programmes, such as the Department of Labor and Employment’s JobStart programme and the Technical Education and Skills Development Authority’s Special Training for Employment Program, to focus on augmented-intelligence-related employment.

• Society:

  • Seed and scale “AI-for-social-good” projects. For example, policymakers could develop “AI-lands” initiatives to fund AI programmes across the country’s vast number of islands.

  • Encourage citizen participation in identifying local challenges and co-creating solutions to cultivate a sense of community ownership and empowerment.

Without deliberate policy intervention, AI’s impact on labour markets could exacerbate economic inequality, widen the digital divide and lead to job displacement, without providing clear pathways to shared prosperity. However, with proactive policymaking that prioritises augmented intelligence – in which AI enhances rather than replaces human capabilities – the region can chart a course towards inclusive growth. Without such an approach, it risks deepening economic and societal polarisation.

By adopting policies that prioritise intelligent augmentation over automation, South-East Asia can avoid mass job displacement, unchecked inequality, skills mismatches and the under-utilisation of talent. Bold and forward-thinking policies will also enable the region to elevate workers and create a resilient, AI-empowered workforce – one accompanied by new, higher-value jobs, such as an AI-powered precision-agriculture technician in Vietnam, an autonomous farm-operations specialist in the Philippines and a social-robotics care technician in Singapore. Doing so will unlock greater economic growth, lift up society and drive inclusive growth for the region.

The authors would like to thank the following people for sharing their expertise, guidance and support in producing this report:

Institute for Adult Learning at the Singapore University of Social Sciences

• Associate Professor Renée Tan

ThinkPlace

• Arvin Bo

• Debbie Ng

• Hui-Min Rae Teh

• Rodrigo Alarcón

Singapore

• AI Singapore

• Centre for Strategic Futures, prime minister’s office

• Infocomm Media Development Authority

• Maritime & Port Authority of Singapore

• Northern Trust

• Ministry of Digital Development and Information

• Singapore University of Technology and Design

• Temus

Vietnam

• Baker McKenzie

• Diplomatic Academy of Vietnam

• FPT Education

• Hyperion Fintech

• Indochina Research

• Institute for Policy Studies and Media Development

• Vietnam Leather and Footwear Association

• Vietnam General Confederation of Labour

• Ministry of Foreign Affairs

• Vietnam National Innovation Center

• VinBrain

The Philippines

• Aboitiz Data Innovation

• Analytics & Artificial Intelligence Association of the Philippines

• Connected Women

• Department of Information and Communications Technology

• Department of Trade and Industry

• Department of Labor and Employment

• National Economic and Development Authority

• Trade Union Congress of the Philippines

Global

• Blavatnik School of Government, University of Oxford

• Commonwealth Scientific and Industrial Research Organisation

• United Nations Development Programme

• Organisation for Economic Co-operation and Development

• World Bank

TBI

• Policy & Politics (Alexander Iosad, Angelo Leone)

• Philippines team (Joy Caneba, Leonardo Camacho, Carlo Enrico Santiago, Reini Azriel Evangelista)

• Vietnam team (Richard McClellan, Nguyen Khoi, Ha Bao Tram, Bạch Ngoc, Nguyen Ly)

• Research & Data Unit (Brianna Miller, Emily Jolliffe, Harriet Coombs)