The rise of artificial intelligence is not simply changing how students learn. It may be fundamentally reshaping what expertise itself means.

A student recently presented an AI-assisted proposal that was technically polished, logically structured, and supported by convincing recommendations. Only a few years ago, producing work of that quality would likely have required substantial effort in research, synthesis, modelling, and technical writing.

But once the discussion moved beyond the proposal itself, the limitations became visible.

What assumptions had been embedded within the recommendation? Would the proposed solution still hold if manufacturing conditions shifted, ingredient behaviour changed, or commercial priorities evolved? How should decisions adapt once new constraints emerge across cost, sustainability, quality, or operational feasibility?

The challenge was no longer about generating technically plausible answers. It was about understanding how to interpret, contextualise, and adapt those answers once realities became dynamic, interconnected, and uncertain.

This distinction matters increasingly.

Across industries, AI tools are rapidly lowering the effort required to generate polished outputs. Analyses, reports, recommendations, coding support, technical summaries, strategic frameworks, and even research synthesis can now be produced at remarkable speed and sophistication.

Historically, the ability to produce coherent analyses and technically sound outputs often served as evidence of expertise. Much of higher education and professional advancement has been built around this premise.

AI is now compressing that advantage.

As informational and cognitive production becomes increasingly automated, the basis of differentiation begins to shift. The question is no longer simply whether individuals can generate answers. Increasingly, differentiation lies in the ability to frame meaningful questions, recognise hidden assumptions, interpret outputs within context, navigate ambiguity, and exercise sound judgement when conditions no longer remain stable.

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In other words, expertise is moving beyond informational mastery alone towards contextual intelligence.

This becomes particularly visible in applied manufacturing environments, where technically correct answers frequently prove insufficient once operational realities evolve.

In these systems, outcomes are rarely shaped by isolated variables alone. Product performance emerges from interactions across formulation behaviour, equipment variability, environmental conditions, process stability, regulatory requirements, workforce capabilities, supply constraints, commercial pressures, and sustainability considerations.

A recommendation that appears technically optimal in theory may become operationally impractical once real-world constraints begin interacting across the system.

AI can increasingly optimise within represented conditions. But real environments do not remain static long enough for optimisation alone to be sufficient.

This is not unique to manufacturing.

Across sectors, AI is increasingly handling structured synthesis, retrieval, formatting, and routine analytical generation. As this happens, human value shifts further towards interpretation, systems thinking, adaptive judgement, and the ability to make decisions under evolving conditions.

This has significant implications for education.

Much of today’s conversation understandably focuses on AI literacy: helping students learn how to use emerging tools effectively and responsibly. These are necessary foundations. But they are unlikely to be sufficient.

If AI increasingly lowers the barrier to producing technically polished work, then education can no longer derive value primarily from answer production alone.

The more difficult challenge is preparing students to operate meaningfully within increasingly AI-mediated environments — environments where outputs are abundant, but interpretation, prioritisation, and judgement become the true constraints.

This changes the kinds of learning experiences that matter.

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In applied learning environments, students increasingly encounter situations where decisions must account for incomplete information, competing priorities, shifting objectives, and operational uncertainty. They may begin with technically sound AI-assisted recommendations, but are subsequently challenged to reconsider those recommendations as realities evolve between quality, cost, sustainability, scalability, and feasibility.

The educational emphasis, therefore, shifts from producing answers towards interrogating them.

Students are assessed not only on the recommendation itself, but also on their ability to explain assumptions, justify trade-offs, identify blind spots, integrate contextual considerations, and adapt thoughtfully when conditions change.

These are fundamentally different capabilities from informational recall alone.

Importantly, AI itself can become part of the learning environment rather than simply a productivity tool. Used well, it creates opportunities to move beyond routine answer generation and place greater emphasis on interpretation, complexity management, and reflective decision-making.

This also challenges how capability is assessed.

Traditional assessments have often rewarded polished reports, technically correct answers, and well-structured presentations. While these remain useful, they become less meaningful as standalone indicators of understanding when AI increasingly assists with their production.

The more important question is whether learners can navigate ambiguity when no single optimal answer exists.

Can they recognise when technically correct outputs become contextually inappropriate?

Can they adapt decisions responsibly when systems evolve?

Can they integrate competing considerations across technical, operational, ethical, environmental, and commercial domains?

These capabilities are difficult to cultivate through learning environments designed primarily around predictable solutions. They are developed through exposure to complexity, iteration, uncertainty, and authentic situations where decisions carry real consequences across interconnected systems.

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The implications extend beyond classrooms.

As AI continues to reshape work, organisations may also need to rethink how talent is evaluated. Credentials, technical fluency, and polished outputs may no longer function as sufficient proxies for capability when many of these can increasingly be augmented by AI systems.

The future value of talent may lie less in producing information and more in exercising discernment.

Those who thrive may not necessarily be individuals who can generate the fastest answers, but those who can understand which questions matter, identify what is missing, recognise shifting constraints, and make responsible decisions amidst uncertainty.

In many ways, the talent reset driven by AI is not reducing the importance of human expertise. It is redefining where human expertise becomes most valuable.

As AI capabilities continue to advance, human differentiation may increasingly reside in qualities that are deeply contextual and difficult to automate fully: systems thinking, adaptive judgement, ethical reasoning, contextual interpretation, and the ability to navigate complexity across evolving environments.

The future will not belong simply to those who know how to use AI tools.

It will belong to those who can work meaningfully with AI-generated knowledge while still understanding how to interpret reality when systems, priorities, and conditions inevitably continue to change.

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Last year, I drew the org chart of the risk team I would hire if I were building from zero. I drew four boxes. Fifteen years earlier, when I first joined a risk team inside an Indonesian bank, the chart I worked under had eleven boxes — analysts feeding into officers feeding into heads feeding into a Chief Risk Officer, with parallel ladders for credit, operational, market, and compliance risk. The shape of the function was a pyramid. The shape I was now drawing was a flat trapezoid.

The pyramid is collapsing — not from above, where most of the attention goes, but from the middle. And the people working their way up through it are the ones who will feel the shift first.

The middle is going first

The analyst tier, the layer where most of us learned this craft, is the one being automated first. Risk dashboards that used to take a team a week to compile now generate themselves overnight. Reconciliation work that used to require three people now requires a workflow. The young analyst who used to spend three years learning the function by doing the compilation is no longer being hired into the seat that taught them.

Two roles that did not used to exist are quietly emerging in the space the analyst tier used to occupy. The first is the model overseer — someone who can read what a model is doing, validate its outputs against domain knowledge, and produce the evidence a regulator will accept. The second is the cross-functional translator — someone who can sit between engineering, risk, and product, and arbitrate between the languages each side speaks.

The Chief Risk Officer role is changing too. Three years ago the CRO’s job was largely to defend the function inside the organisation — to argue for risk constraints against revenue pressure, and to package the function’s work for the board. Today the CRO is increasingly an integrator. They must understand how AI models touching credit, fraud, and compliance interact with each other, and how the firm’s risk posture shifts as each of those models is retrained or replaced.

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What I should have changed sooner

I did not flatten the teams I was hiring for soon enough. I kept hiring analyst-tier roles into 2023 because the previous pyramid was familiar, because I worried about losing the apprenticeship pipeline the analyst tier represented, and because the alternative team shape was still genuinely uncertain. Twelve months of those hires turned out to be redundant within two years — not because the people were not capable, but because the work they were hired to do had already been automated underneath them. I should have spent that budget on the model overseer roles that turned out to matter.

The CRO pipeline problem

The flattening creates a new problem nobody in the industry is solving yet. The traditional pyramid was, among other things, a training apparatus. Analysts grew into officers, officers grew into heads, heads grew into chiefs — and each layer taught the person above it some of what the next layer needed to do.

Without that ladder, the function depends entirely on lateral hires. The supply of people who have already learned to be a CRO without going through the pyramid is small, and most of them are sitting in their current jobs at large institutions that built them. The next generation of CROs in ASEAN will either be poached or invented. There is no obvious third path yet.

What good team design looks like now

The teams I am helping build today look nothing like the pyramid I came up through. The shape is closer to a small, senior cell than a hierarchy. Two or three model overseers. One or two cross-functional translators. A small, very senior decision layer at the top. Apprenticeship happens through rotation rather than ladder — people move between the model side, the policy side, and the engineering side, picking up the domain by exposure rather than by promotion.

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The teams are smaller. The seniority per head is higher. The compensation envelope per role is bigger. And the work done by each seat is more cross-domain than any single seat used to require.

The shape you draw next matters more than the tools you buy

If you are building or reshaping a risk function in 2026, the question is not how to digitise the pyramid you have. The question is whether the pyramid was ever the right shape for the work you now need to do. The teams that will sit inside ASEAN’s regulated institutions five years from now look nothing like the teams that staffed them when most of us learned this craft.

That gap will not close by adding tooling on top of the old structure. It will close by drawing a different shape, and being willing to hire against it before the rest of the industry catches up.

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Editor’s note: e27 aims to foster thought leadership by publishing views from the community. You can also share your perspective by submitting an article, video, podcast, or infographic.

The views expressed in this article are those of the author and do not necessarily reflect the official policy or position of e27.

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Singapore’s business leaders are the least optimistic about international expansion among the markets surveyed in Kreston Global’s latest Interpreneur Report, underscoring a more cautious approach to cross-border growth amid rising geopolitical friction, supply-chain fragility, and tariff pressure.

The report, based on a survey of 1,100 “interpreneurs” operating businesses with annual revenue between £10 million and £300 million (roughly US$12.7 million to US$381 million) across 11 countries, gives a rare window into how mid-market firms that have already expanded overseas are thinking about the next stage of foreign growth. Singapore respondents averaged a 7.2 out of 10 score for the current business climate for expansion, compared with 8.2 globally.

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The more muted sentiment coexists with guarded optimism. Two-thirds of Singapore interpreneurs, or 66 per cent, expect the environment for international business expansion to become more favourable in the next two to three years. However, that optimism lags the 86 per cent seen among their global peers.

A city-state acutely exposed to global trade dynamics, Singapore unsurprisingly flags geopolitical instability, supply-chain disruption, and tariff-related costs as the most significant threats to overseas operations. In each case, the city recorded some of the highest concern levels among countries polled: 52 per cent cited geopolitical instability (versus a 45 per cent global average), 43 per cent named supply-chain disruption (global average 31 per cent), and 42 per cent pointed to tariff-related cost increases (global average 40 per cent).

“These are direct and profound impacts on the economy and business confidence,” said Helmi Talib, managing partner at Kreston Helmi Talib, Singapore. “As a city that heavily relies on trade, global headwinds such as geopolitical tensions and supply-chain disruption shape a more cautious, selective approach to international expansion.”

The Singapore context: still open, but choosy

Singapore’s priorities when assessing expansion destinations remain rooted in traditional fundamentals: future economic growth prospects (46 per cent), favourable tax policies (44 per cent), trade agreements (44 per cent), and alignment with long-term strategy (43 per cent). Access to skills and talent, and to digital infrastructure, was each featured by 38 per cent of respondents.

That emphasis signals how Singaporean firms are treating overseas expansion more as a calculated extension of their domestic strategies than as a leap into unfamiliar technology frontiers. Access to new customer markets (52 per cent), strategic partnerships or joint ventures (51 per cent), and the ability to lower production or operating costs (43 per cent) were cited as the most significant opportunities for international growth.

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For Southeast Asia, that spells an opening for deeper commercial ties built on partnerships rather than purely technology-driven propositions. ASEAN economies offering talent, lower operating costs, or attractive trade pacts may attract Singapore capital and management expertise, but likely on a more project-by-project basis than in the boom years of rapid outbound deals.

AI and technology: embedded, not transformative

One of the report’s more revealing findings concerns the role of artificial intelligence. While 97 per cent of Singapore respondents say AI influences their expansion strategy to some extent, only 52 per cent describe that impact as “significant” or “very significant”, well below the 74 per cent global average. Singapore interpreneurs are almost twice as likely as global peers (45 per cent versus 24 per cent) to regard AI’s impact as moderate or minor.

That pragmatism extends to technology more broadly. Just 25 per cent say access to digital technologies and innovation was a primary motivator for expanding overseas (global average 40 per cent), and only 37 per cent view advanced technology adoption as a major future opportunity (global average 52 per cent).

“Singapore is one of the most mature and hyper-connected digital markets globally,” Talib said. “Access to technology is less of a limiting or motivating factor in expansion decisions; AI appears to be embedded, business-as-usual rather than a transformational driver.”

For Southeast Asia, this has two implications. First, Singapore firms may look to regional markets for conventional expansion levers (market access, cost efficiencies, and partnerships) rather than leveraging a native technology advantage for exports. Second, local Southeast Asian startups and service providers that can offer targeted operational capabilities or support localisation will remain valuable to Singaporean entrepreneurs looking to scale abroad.

Practical responses: governance, processes and partnerships

Faced with a volatile, uncertain, complex and ambiguous (VUCA) environment, Kreston advises SMEs to prioritise internal alignment and operational readiness so they can move quickly when opportunities appear. “SMEs that strategically invest in strengthening governance, refining processes and establishing robust operating frameworks will be better equipped with the resilience and agility needed to act decisively as expansion opportunities regain momentum,” Talib said.

That message resonates across Southeast Asia, where mid-market firms often encounter regulatory complexity, talent gaps and fragmented supply chains. The report suggests Singapore-based firms may increasingly favour joint ventures, strategic alliances, and local partnerships to navigate those hurdles, a trend that could bring capital, governance standards and managerial know-how into the region.

A nuanced picture beneath a broadly positive headline

Liza Robbins, chief executive of Kreston Global, described the overall mood as resilient despite the challenges. “The finer details in the data tell a more nuanced story of businesses grappling with the challenges of AI, tariffs and geopolitical instability,” she said. “In spite of this, the resilience, drive and adaptability of interpreneurs have once again been underscored.”

For Southeast Asia, the Kreston findings emphasise a recalibration rather than retreat by Singapore firms: more selective, partnership-led expansion focused on market access, cost efficiency and regulatory alignment, with technology treated as an enabler rather than the primary rationale for outbound investment.

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In practice, that could translate to more Singapore capital flowing into targeted manufacturing hubs, logistics nodes and consumer markets within ASEAN, accompanied by operational and governance expertise, rather than the headline-grabbing tech acquisitions of previous cycles. For regional startups and policymakers, the opportunity lies in aligning incentives, easing market entry and demonstrating reliable local capabilities that complement Singapore’s cautious but persistent outward push.

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National GRIP is a venture creation and commercialisation platform designed to help science entrepreneurs and researchers transform breakthrough innovation into scalable businesses. Operating as a high-performance venture builder, National GRIP brings together founders, operators, investors, corporates, universities, and ecosystem partners to accelerate commercialisation outcomes across Singapore’s deep tech ecosystem.

By combining venture execution, market validation, commercialisation strategy, and investor readiness within a structured framework, National GRIP helps founders bridge the gap between research and real-world market adoption. At Echelon Singapore 2026, National GRIP showcased promising startups emerging from Singapore’s innovation ecosystem while creating opportunities for meaningful collaboration between founders, investors, corporates, and ecosystem leaders.

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National GRIP is jointly run by Nanyang Technological University, Singapore (NTU Singapore) and the National University of Singapore (NUS), supported by the National Research Foundation, Singapore (NRF Singapore).

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The e27 team produced this article.

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Image Credit: Echelon Singapore 2026

The post Meet the deep tech startups National GRIP brought to Echelon Singapore 2026 appeared first on e27.

National GRIP is a venture creation and commercialisation platform designed to help science entrepreneurs and researchers transform breakthrough innovation into scalable businesses. Operating as a high-performance venture builder, National GRIP brings together founders, operators, investors, corporates, universities, and ecosystem partners to accelerate commercialisation outcomes across Singapore’s deep tech ecosystem.

By combining venture execution, market validation, commercialisation strategy, and investor readiness within a structured framework, National GRIP helps founders bridge the gap between research and real-world market adoption.

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National GRIP is jointly run by Nanyang Technological University, Singapore (NTU Singapore) and the National University of Singapore (NUS), supported by the National Research Foundation, Singapore (NRF Singapore).

Want updates like this delivered directly? Join our WhatsApp channel and stay in the loop.

The e27 team produced this article.

We can share your story at e27 too! Engage the Southeast Asian tech ecosystem by bringing your story to the world. You can reach out to us here to get started.

Image Credit: Echelon Singapore 2026

The post National GRIP enables deep tech ventures from lab to market appeared first on e27.