Singapore venture firm Tin Men Capital has invested in enterprise AI startup Pints AI as part of a US$5.6 million funding round, underscoring growing investor appetite for practical, business-facing artificial intelligence across Southeast Asia.

The capital will be deployed to beef up Pints AI’s engineering bench, tighten governance and audit frameworks, and improve internal systems. These priorities reflect a broader shift among corporate buyers away from experimental AI pilots toward production-grade solutions that can be audited, scaled, and trusted.

A test of enterprise readiness

Pints AI positions itself in a crowded but increasingly differentiated market: vendors that pair machine learning capabilities with tools and processes designed for enterprise deployment. Unlike consumer-facing generative AI products that attract headlines, enterprise AI’s value is measured in reliability, ease of integration, and regulatory compliance — attributes that command premium attention from both customers and investors.

Also Read: Enterprise AI hits barriers as privacy, sovereignty demands grow

The fresh funding arrives at a juncture when businesses across Southeast Asia are accelerating digital transformation efforts to control costs and streamline operations. Companies, from logistics providers in Indonesia to banks in the Philippines, are seeking automation that can reduce manual workflows, improve decision speed, and surface insights from fragmented data. That demand has pushed investors towards startups that solve concrete operational problems rather than consumer novelty.

Tin Men Capital’s participation signals confidence in Pints AI’s ability to bridge that gap. The Singapore-based firm has been active across the region, focusing on technology companies that can scale within Southeast Asia’s diverse market landscape. The deal aligns with a wider trend among regional VCs to back B2B software and infrastructure companies that promise recurring revenue and enterprise-grade robustness.

Engineering and governance: where the money is going

According to Pints AI’s announcement, most of the capital will be allocated to hiring engineering talent and strengthening governance and audit systems. Those priorities reflect hard lessons from companies attempting to deploy AI at scale: models and pipelines need constant upkeep; data lineage, explainability, and compliance provisions must be baked into product design; and incident response and monitoring must be operationalised.

For Southeast Asian customers, those capabilities matter more than ever. Many markets in the region are still developing regulatory guardrails for AI-driven decision-making, while firms juggle cross-border data flows, varying security standards and tight budgets. Startups that can demonstrate strong governance frameworks and the ability to show auditors and regulators what happens inside their systems will have an advantage in winning larger contracts from enterprises wary of reputational and compliance risk.

This emphasis on “trustworthy AI” is reshaping product roadmaps across the sector. Investors are increasingly willing to finance not just model development but the scaffolding required to deliver AI reliably into complex enterprise environments: observability tooling, automated testing, role-based access, and audit trails.

Southeast Asia: fertile ground for practical AI

Southeast Asia’s startup ecosystem has matured considerably in the past decade, moving from consumer-focused plays to more diversified portfolios that include fintech, enterprise software, cybersecurity, and healthtech. That maturation is accompanied by a more selective investor base: with public markets and macro uncertainty tempering valuations, VCs are concentrating capital on companies with clear unit economics, defensible distribution channels and demonstrated product-market fit.

Also Read: The big flip: Why being “smart” isn’t enough for enterprise AI in 2026

Enterprise AI fits that profile for a number of reasons.

• First, the addressable market is large: SMEs and corporates across ASEAN carry vast amounts of unstructured data and manual processes ripe for automation.
• Second, enterprise contracts often translate into higher and more predictable annual recurring revenue compared with consumer apps.
• Third, Asian firms are under pressure to improve margins and efficiency, a secular driver for automation investments.

Yet the path to scale is not straightforward. Startups must navigate diverse languages, regulatory regimes, and legacy IT stacks. Winning in Southeast Asia frequently requires localised approaches and partnerships, something that investors like Tin Men with deep regional networks can help facilitate.

What the deal means for the sector

The Pints AI round is indicative of several converging trends. It reflects continued interest in AI beyond flashy, consumer-oriented use cases; it highlights the premium on engineering headcount and compliance tooling; and it underscores the growing role of Singapore-based investors in shaping the regional enterprise software landscape.

For founders in the region, the signal is clear: investors are ready to fund the work required to move AI from lab experiments into enterprise standard operating procedures. That work is expensive and often less glamorous than training large models, but it may deliver more durable returns as customers prioritise reliability and auditability.

What remains to be seen is how quickly the demand translates into large, repeatable contracts. Southeast Asian enterprises are still at various stages of digital maturity. Some will adopt packaged automation solutions readily; others will require bespoke integration and longer sales cycles. The companies that succeed will likely be those that combine strong engineering teams with pragmatic go-to-market strategies and an ability to prove outcomes.

Implications for founders and investors

For founders building enterprise AI, the takeaway is to prioritise the plumbing: observability, testing, data governance and customer success functions that can demonstrate ROI. For investors, Pints AI’s funding round is another data point suggesting that capital will follow companies that can reduce operational friction for customers and withstand the scrutiny of auditors and regulators.

Also Read: The psychology of AI adoption: How familiarity bias is quietly slowing finance down

As Southeast Asia’s economies digitise and the regional adoption of AI grows, there will be more room for companies that deliver measurable efficiency gains. The Tin Men-Pints AI deal shows that investors are willing to back the less glamorous but essential work of industrialising AI for business, a necessary step if the region’s AI ambitions are to translate into sustained commercial impact.

The post Pints AI raises US$5.6M to chase automation wins as SEA firms embrace operational AI appeared first on e27.

Most Indonesian millennials who grew up in the 1990s remember the sentence from their elementary Bahasa Indonesia textbook: “bapak pergi ke sawah, ibu masak di dapur.” In English, it is translated as “father goes to the field, mother stays home to cook.” Decades later, that sentence has quietly become a cultural script. The term bapak tani, the male farmer, remains the dominant image attached to Indonesian agriculture, celebrated in policy, portrayed in media, and assumed in market design. Ibu tani, the woman farmer, exists in the field but rarely in the narrative.

Across Indonesia’s agricultural landscape, women make up a significant share of the farming workforce, planting, harvesting, and processing, yet remain systematically excluded from the decisions, markets, and profits. As Prof. Anna Fatchiya of IPB University’s Faculty of Human Ecology has noted, women farmers carry significant roles in the agrifood system, yet receive disproportionately little recognition for it. They are present in the field, but absent from the value chain. This is not a cultural accident. It is a structural one, and no agritech platform is designed to fix the root cause of this gender problem.

The numbers make this concrete. Data from Syngenta Indonesia reveals that 37.8 per cent of Indonesian farmers are women, yet only 13.61 per cent hold formal rights over the land they work. In Jambi province, Sumatra, women farmers earn approximately US$3.53 per day compared to US$6.48 for their male counterparts, for the same work and on the same schedule. According to Widyani (2023) of Universitas Negeri Makassar, these disparities are sustained by deep-rooted patriarchal norms, belief systems inherited from the past that continue to structure present agricultural relations.

The problem is not the app — it is the system behind it

In rural Indonesia, patriarchal structures are active and embedded in how agricultural markets function. Male farmers are treated as the default market actors; mostly, they negotiate with middlemen, secure supplier relationships, and are recognised as the primary decision makers. Female farmers, meanwhile, are limited to planting and harvesting, while domestic responsibilities such as cooking and taking care of the family fill the rest of their hours.

This is not a matter of attitude or culture. It is how informal market rules have been built. Middlemen seek male farmers because male farmers are considered the leaders. Price information circulates through networks that women are excluded from. Nobody wrote these rules down. They did not need to. The norms came first, and the market followed.

Also Read: Women in tech: It’s time to reframe the conversation

Land ownership in rural Indonesia is dominated by men, aligned with data from FAO (2018) that globally, less than 15 per cent of women own agricultural land. Further, access to agricultural credit typically requires proof of land rights. Women farmers without formal legal assets cannot participate in programs such as Kredit Usaha Rakyat (KUR) arranged by national banks and the government in Indonesia. 

This is where the gaps in agritech become visible. Agritech ventures enter the market with two goals:

• To grow, through user acquisition, partnerships, and revenue, and 
• To solve problems in the agricultural supply chain. 

Both goals are reasonable. But both are designed to operate within the current system, not to challenge it. When the existing system already excludes women farmers at the level of norms, informal rules, and formal structures, a platform that adapts to that system will digitise the exclusion that was already there.

Technology is not the problem. The foundation it is built on is.

What fixing the system actually looks like 

Fixing the market system does not start with building a better app or AI tech. It starts with changing who gets to participate in the market. By design, this is where civil society organisations (CSOs) play a role. 

CSOs are often treated as service delivery channels, organisations that distribute aid, run training programs, and report impact numbers. But in agricultural market systems, they are more important than that. They operate inside communities, build trust over years, and are positioned to shift the informal rules that formal institutions and tech platforms cannot do.

Also Read: AI could redefine women in the workplace—and companies must act now

Perempuan Sumatera Mampu (PERMAMPU) and Pemberdayaan Perempuan Kepala Keluarga (PEKKA) are two examples of CSOs that aim to enhance women’s economic independence and leadership, aligned with the needs of women farmers in Indonesia to have access to a business and market environment. 

However, to strengthen and widen the impact of CSOs, there must be clear collaboration between stakeholders, such as:

• Collaboration with agritech startups to make women farmers become reliable producers. 
• Collaboration with local authorities to enable them to connect with national banks in order to access credit for enterprises such as KUR. 
• Collaboration with universities to access knowledge transfer from academia and researchers. 

These efforts will not only produce results on paper alone, but also revolutionise the system to put women farmers as reliable producers and partners. 

For founders and investors in Southeast Asian agriculture, the question is no longer whether women farmers are underserved. The real question is whether the ecosystem is willing to measure success by equal opportunities, not just equal access. 

—

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 post Agritech does not empower women farmers, until the system is fixed appeared first on e27.

We are living through a grand paradox. On one hand, global summits like COP and the G20 scream that trillions of dollars are needed to hit the UN Sustainable Development Goals (SDGs) by 2030. On the other hand, I sit in meetings with brilliant founders in Jakarta, Ho Chi Minh City, and Colombo who are building physical, atoms-not-bits solutions, and they are starving for capital.

Why? Because the funding gap isn’t just about a lack of money. It’s a fragmentation of intelligence. After reviewing 10,000 pitch decks and holding 5,000 founder meetings, I’ve realised that we’ve built a system so bureaucratic and dysfunctional that the very people we need to save the planet are spending 40 per cent of their time navigating a capital labyrinth instead of engineering solutions for goal 7 (clean energy) or goal 13 (climate action).

The three invisible barriers killing hardtech impact

When you build a SaaS platform, the path is linear: Seed, Series A, Series B. But when you build a physical solution—like a botanical biorefinery in Bali or a blue carbon platform in Vietnam—the software playbook fails. Here is why:

• The compliance tax and the reporting trap

Many impact funds, especially those tied to Multilateral Development Banks (MDBs) or large NGOs, come with strings that would choke a late-stage corporate, let alone a five-person startup.

I’ve seen founders win a US$150k grant only to realise they need to hire a full-time compliance officer just to manage the quarterly reporting metrics required by the donor. This is a resource drain that favours consultancy-style startups over engineering-style startups. We are inadvertently funding people who are good at writing reports, not people who are good at fixing the ocean.

• Geopolitical and sector-specific information deserts

The capital stack is not a ladder; it’s a spiderweb. To survive, a founder needs to weave together:

• Technical assistance (TA): Like the Blue Carbon Accelerator Fund (BCAF) for feasibility.
• Non-dilutive grants: Like the USAID-funded Climate Solutions or the IUCN’s biodiversity calls.
• Concessional debt: From foundations like Beneficial Returns or The Rockefeller Foundation.
• Equity: From VCs who actually understand hardtech lifecycles.

The dysfunction: Currently, there is no single source of truth. A founder in Vietnam building a regenerative aquaculture system has to search through 1,000 PDFs and closed-door networks to find these sources. If you don’t have a sherpa or an expensive consultant, you simply don’t find the money.

• The atoms vs bits valuation friction

Standard VCs want 10x returns in five to seven years. Physical science-led solutions (deeptech) often need 10 years just to reach commercial scale. Because founders don’t understand the capital stack, they often take VC money too early, get diluted into oblivion, and the company collapses under the weight of software-speed expectations.

Also Read: Why I’m trading bytes for atoms: The 65-year-old investor breaking the climate tech silos

The real-world friction: Two scenarios

To show how broken this is, let’s look at the theoretical paperwork mountain founders face today:

• The blue carbon play (Indonesia/Vietnam): A founder building an IoT-verified mangrove restoration needs US$2M. They find a potential grant from the Global Environment Facility (GEF). But the GEF requires a government endorsement letter. The founder spends six months in ministerial waiting rooms in Jakarta, only to find the grant window has closed. They then pivot to a CVC (Corporate Venture Capital) play, but the CVC won’t move until there is a first-loss guarantee from an NGO. The founder is now a full-time diplomat, not an entrepreneur.
• The agtech engineer (India/Sri Lanka): A founder has a low-cost, solar-powered biorefinery. They look at the Asian Development Bank (ADB) funds. The ADB is massive, but the entry point for a startup is invisible. They end up chasing impact-linked loans where the interest rate drops if they hit SDG targets. It sounds great, until they realise the verification audit costs more than the interest savings.

The solution: A call for information infrastructure

We are architecting capital, but we haven’t yet architected the information portal to deliver it.

I am calling on my fellow fundraising angels, investors, and the tech community: We must build a global impact portal. We need a searchable, AI-driven command centre where a founder can type: “I am a startup in Vietnam, building a seaweed-based plastic alternative. Show me every grant, technical assistance provider, NGO loan, and Hardtech VC active in my region right now.”

If we can build complex algorithms to predict what movie you want to watch on a Friday night, we can certainly build a directory that helps a climate-tech founder find a grant in Bali or a lab in Colombo.

Also Read: Why perfect carbon audits could cripple climate finance — and what to fix instead

The founder’s cheat sheet: Five questions to ask before taking your first dollar

Before you sign that term sheet or spend six months on a grant application, ask yourself these five questions to ensure you aren’t walking into a trap:

• Is this patient or pressured capital? Does the funder understand that hardware takes 3x longer than software? If they expect a pivot to SaaS in 18 months, run.
• What is the compliance-to-capital ratio? Will the reporting requirements for this US$50k grant cost you US$60k in engineering hours and administrative overhead?
• Does this money unlock the next level of the stack? Will this grant provide the Technical Assistance (TA) needed to make you bankable for a concessional loan later?
• Are you solving for the goal or the grant? Are you tweaking your technology just to fit a specific NGO’s mandate, or does the funding truly support your core engineering roadmap?
• Is there a first-loss guarantee? Can this foundation or NGO provide a guarantee that makes it safer for a commercial bank or VC to follow them?

The status quo is a tax on our future. To the governments and the NGOs: Simplify your entry points. To the founders: Stop being accidental fundseekers and start being architects of your own capital stack.

Let’s stop talking about the gap and start building the map.

—

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.

Join us on Instagram, Facebook, X, and LinkedIn to stay connected.

The post The founder’s labyrinth: Why the US$2T climate finance industry is failing ‘atoms’ in SEA appeared first on e27.

For most of my adult life, I have been paid to write. That sentence used to carry a certain clarity. It meant you could report, interview, structure arguments, meet deadlines, understand audiences and turn half-formed ideas into something people wanted to read. Over the years, I have written for media platforms across Asia and globally, worked with founders, PR teams, editors, startups and business leaders, and seen how the definition of a “good writer” changes depending on the publication, market and moment.

Then generative AI arrived in the mainstream. This is no longer a niche productivity shift. McKinsey’s 2025 global AI survey found that 88 per cent of organisations now report regular AI use in at least one business function, up from 78 per cent a year earlier. For content and communications teams, that means AI-assisted writing is quickly becoming part of the operating environment, not a novelty

Today, ChatGPT, Gemini, Perplexity and a long list of other tools can produce a clean first draft in seconds. They can summarise research, suggest headlines, rewrite copy, generate social captions and mimic the structure of a thought leadership article. For hiring managers in media, PR, marketing and content, this raises an uncomfortable but necessary question: if everyone can now “write”, who is actually a good writer anymore?

More importantly, who is still worth paying to write? This is not a question only for editors or agency leads. It is increasingly an HR question. Across APAC, where companies operate across multiple languages, cultures, regulatory environments and media markets, the ability to communicate clearly is becoming more important, not less. But the signals we use to assess communication talent need to change.

A polished writing test is no longer enough. A portfolio full of neat articles is no longer enough. Even years of experience may not mean what it used to. The real differentiator now is not whether someone can produce words. It is whether they can think, judge, question, adapt and take responsibility for what those words do.

The old markers of writing talent are becoming weaker signals

Three years ago, if I were hiring a writer or content person, I would have paid close attention to bylines, writing samples, industry exposure, speed and grammar. These things still matter, but they are no longer sufficient. A candidate can now submit a clean sample with very little original thinking behind it. They can use AI to improve sentence flow, generate article structures or create a competent-looking draft on a topic they barely understand. This does not make them dishonest. In many cases, it simply reflects the new reality of work. Most content teams are already using AI in some form, formally or informally.

The problem is that hiring processes have not caught up. Many companies still assess writers as though the main scarcity is sentence construction. But in 2026, sentence construction is becoming cheaper. What remains scarce is judgment.

Can this person tell when a claim is weak? Can they spot when a statistic is outdated or being used out of context? Can they interview someone and hear the actual story beneath the corporate talking points? Can they understand why a founder’s opinion matters to one outlet but sounds self-promotional to another? Can they write differently for e27, Tech Collective, a lifestyle publication, a LinkedIn post and a client byline without flattening everything into the same generic tone? That is where talent now shows up.

Also Read: Is our talent pipeline ready for the AI economy? Not in the way we think

AI proficiency matters, but not in the way many people think

There is a temptation to treat “AI skills” as a new line item on a job description. Can the candidate use ChatGPT? Can they write prompts? Can they generate content faster? These are useful questions, but they are shallow on their own.

In content and media roles, AI proficiency should not mean the ability to outsource thinking to a tool. It should mean knowing how to use AI without losing editorial judgment. A strong candidate should be able to explain what they would use AI for, what they would never use it for and how they would verify the output.

For example, I would be more impressed by a candidate who says, “I use AI to test headline options and identify gaps in structure, but I do my own source checking and rewrite the argument myself,” than one who simply says, “I can produce five articles a day using AI.”

Speed is useful, but speed without discernment creates risk. In media and communications, that risk may appear as factual errors, bland thought leadership, weak attribution, cultural tone-deafness or content that sounds polished but says very little. For startups and agencies in APAC, where one article may need to work across Singapore, Malaysia, Indonesia, Vietnam or broader regional audiences, that lack of judgement can damage credibility quickly. The best talent today is not anti-AI. It is AI-literate and editorially accountable.

What I now look for in writers and content talent

The first thing I look for is curiosity. Not the performative kind, but the kind that shows up in the questions someone asks before they write. A good writer does not simply ask, “What is the word count?” They ask who the audience is, why this topic matters now, what has already been said, what the client or publication wants to avoid, what claim needs proof and what the reader should walk away understanding. In an AI-saturated content market, curiosity is a competitive advantage because it leads to better inputs. Better inputs still produce better work, whether AI is involved or not.

The second signal is taste. This is harder to teach than grammar. Taste is knowing when a sentence sounds too inflated, when an opening paragraph is dragging, when a quote is weak, when a headline is technically accurate but emotionally flat. It is what helps a writer avoid the generic “in today’s rapidly evolving landscape” style of content that AI tools produce so easily.

The third is accountability. I want to know whether someone feels responsible for the accuracy and usefulness of the work. This is especially important in journalism-adjacent roles, PR and thought leadership. A writer who cannot explain why they used a certain source, framed an argument in a certain way or removed a claim from a draft is not ready to operate independently.

The fourth is adaptability. The strongest content professionals are not locked into one format or one voice. They can write a founder byline, edit a client comment, turn a press release into a story, prepare interview questions, write a social caption and understand why each one requires a different approach. Finally, I look for perspective. AI can summarise what is already online. A strong writer can tell you what is missing from the conversation.

What matters less than it used to

This may be uncomfortable, but credentials matter less to me than they once did. A journalism degree, a communications qualification or a well-known previous employer can be useful signals, but they are not guarantees. Some of the strongest writers I have worked with were not the most credentialed. They were the ones who could listen carefully, think clearly and revise without ego.

Also Read: The creative gap: Why GenAI is outpacing the talent it was meant to empower

Years of experience also need to be examined more carefully. Someone may have spent five years producing content without ever learning how to shape an argument. Another person may have two years of experience but sharper editorial instincts, stronger research habits and a better grasp of digital audiences.

Even technical writing skill, while still important, is no longer the entire game. Grammar can be cleaned up. Structure can be improved. What is harder to fix is a lack of thinking. This does not mean lowering standards. It means raising them in the right places.

What this means for APAC’s HR and media ecosystem

Across APAC, companies are under pressure to produce more content, more quickly and across more channels. Startups need founder visibility. Tech companies need thought leadership. HR teams need employer branding. PR agencies need bylines, pitches, commentary and media-ready narratives. Publications need contributors who understand their audience and do not waste editorial time.

At the same time, budgets are tight, and AI tools are making leaders question what they should still pay humans to do. The answer is not to pay people merely to generate text. That work will continue to be automated, compressed or devalued. The answer is to pay people who can combine domain understanding, editorial judgement and strategic communication.

For HR leaders, this means rethinking how writing and content roles are assessed. Instead of asking candidates to produce a generic article from scratch, give them a messy brief. Ask them what they would question. Give them a weak AI-generated draft and ask them to improve it. Ask them to fact-check a paragraph. Ask them to explain which angle would work for which publication and why. In other words, test the thinking around the writing.

A practical framework for hiring content talent now

When hiring writers, editors, PR consultants or content strategists today, I would ask five questions.

• Can they think beyond the brief? A great hire does not simply execute instructions. They can identify what is missing, what is unclear and what needs to be challenged.
• Can they use AI without becoming dependent on it? The best candidates should be able to use tools for efficiency while still owning the final judgment.
• Can they adapt to the audience and context? A strong writer knows that a startup founder byline, a lifestyle feature and a regional tech analysis cannot sound the same.
• Can they handle feedback without losing the thread? In content work, revision is not a punishment. It is part of the job. Good talent can take feedback, improve the piece and still protect the core argument.
• Can they make the work more useful? This is the ultimate test. After they touch a draft, is it clearer, sharper, more accurate and more valuable to the reader?

Also Read: What hiring a high school graduate taught me about talent in the AI economy

The future belongs to writers who can think

AI has not made writing irrelevant. It has made average writing easier to produce. That distinction matters. For those of us who have built our careers on words, the shift can feel unsettling. But it is also clarifying. The market is no longer rewarding people simply because they can fill a page. It is rewarding those who can bring judgment, context, taste and responsibility to communication.

In media, PR, marketing and content roles, “great talent” no longer means the person who can write the cleanest first draft. It means the person who can understand what needs to be said, why it matters, who it is for and how to make it credible.

The tools will keep improving. More people will be able to produce acceptable content. But acceptable content is not the same as valuable communication. That is where good writers still matter. And that is why the best ones will still get paid.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post Why the best content talent is no longer just a good writer appeared first on e27.

AI skills have shifted from specialist niches into mainstream hiring practices, with AI‑related job postings leaping to capture 5.3 per cent of all roles in 2025, up from 3.3 per cent the year before.

That rise represents roughly 30,000 additional listings in a labour market otherwise facing global headwinds, according to PwC’s analysis of job-posting data and government surveys.

The numbers underline a broader transformation: rather than phasing out roles, AI is reshaping them. Occupations that are more exposed to AI, where day‑to‑day tasks and core abilities overlap with AI capabilities, are seeing more job openings and a faster rate of skill turnover.

Also Read: AI will replace inertia before it replaces people

The trend has important implications for Singapore, which aims to remain Southeast Asia’s technology and finance hub, and for neighbouring markets that look to the city‑state as a bellwether.

AI exposure correlates with job growth and skill churn

PwC’s measure of AI exposure shows a clear pattern: the greater an occupation’s exposure, the larger the number of job postings. Between 2019 and 2025, there was a +0.30 correlation between AI exposure and net skills change, suggesting that occupations more entwined with AI are also evolving fastest in terms of required competencies.

The reshaping is visible in hiring data. AI‑related roles rose to about 84,000 in 2025, an increase from the previous year, and over half of all job postings now fall within occupations with higher AI exposure. This suggests that employers are not merely replacing human tasks with automation; they are redesigning job descriptions and adding new responsibilities that include working alongside AI tools.

Public sector, finance and tech lead hiring

Sectoral analysis shows that technology, media, and telecom (TMT), government and public sector, and financial services are leading AI hiring in Singapore. These sectors also report high rates of AI adoption in surveys by the Ministry of Manpower (MOM), where 18.9 per cent of firms said they were redesigning job functions and 13.9 per cent reported creating new AI roles in the first quarter of 2026.

The government and public sector, in particular, are offering large wage premiums for AI talent, with advertised wages approximately 107 per cent higher for AI‑related roles than for non‑AI roles in the same sector in 2025. Consumer Markets reported a 96 per cent premium. High premiums in lower‑volume sectors point to concentrated demand for specialised skills. In contrast, more broadly, AI‑enabled sectors show narrower pay gaps as AI becomes part of routine job requirements.

AI users, not just developers

A striking signal of mainstreaming is the concentration of demand. About 82 per cent of AI‑related job postings in Singapore are for AI user roles — non‑specialist or hybrid positions that require working fluency with AI tools — rather than for developers. AI user roles accounted for approximately +26,000 of the increase in postings, while developer roles added around +4,200 in 2025 versus the prior year.

This split shows employers favouring a model where AI augments existing workforces rather than remaining the preserve of elite engineering teams. For Southeast Asia’s startups and fast‑scaling firms, that means hiring managers will increasingly prioritise candidates who can blend domain expertise with practical proficiency in AI tools, rather than recruiting only core machine‑learning engineers.

Policy and upskilling: Singapore’s push and regional spillovers

Singapore’s policy moves in 2026, from a National AI Council to dedicated AI missions and an AI Impact Programme, underpin this labour market shift. Those initiatives aim to boost adoption across sectors and encourage workforce upskilling. As organisations transition from pilots to scaled deployments, the demand for job redesign and structured reskilling will only ratchet up.

Also Read: AI’s first real casualties: The tech jobs that vanished in 2025

For the region, Singapore’s policy and market signals matter. Regional governments and corporations often benchmark against Singapore, and multinational firms based in the city serve as hubs for talent and investment that spill over into Indonesia, Vietnam, the Philippines and Malaysia. Startups in those markets could both benefit and face talent competition as Singapore firms soak up AI‑literate candidates and pay premiums for specialised roles.

What this means for startups and talent markets

For startups across the region, several practical consequences follow:

• Hiring strategy: Expect competition for AI‑literate generalists. Startups will need clearer role definitions that combine domain knowledge with AI fluency and may have to offer training pathways rather than expecting fully formed skills.
• Costs and pricing: As wage premiums persist for specialised AI roles, early‑stage firms may face higher personnel costs or choose to outsource AI development to contractors and partner firms in lower‑cost markets.
• Upskilling and retention: Investing in internal reskilling programmes could become a cost‑effective alternative to poaching senior AI talent, especially where long‑term cultural fit and domain expertise are critical.
• Product roadmaps: Startups that embed AI into their core propositions, not merely as an add‑on feature, will be better positioned to attract customers and talent in an ecosystem where AI capability signals competitive parity.

Risk and governance

As roles proliferate, governance becomes central. PwC highlights AI governance frameworks as one way to manage risk and foster trusted deployments. For Southeast Asian firms, adopting governance standards early could reduce regulatory friction and build user trust across markets where consumer privacy and algorithmic fairness are growing concerns.

The regional picture

Singapore’s labour market is the most visible example in the study, but the underlying dynamics are relevant across Southeast Asia. Countries with maturing digital economies will see similar shifts, albeit tempered by local talent supply, wage structures and policy timelines. For regional policymakers and startup founders, the imperative is clear: investing in reskilling and responsible AI practices now will determine who captures the productivity gains of the next wave.

The post AI user roles surge as Singapore pivots from specialist to mainstream hires appeared first on e27.

There was a time when building a product was the hardest part of entrepreneurship.

Today, that is changing.

With AI, founders can generate code, design landing pages, create marketing assets, automate workflows, and launch products faster than ever before. What once took months can now happen in days.

But this shift introduces a new challenge.

If building becomes easier, founders risk creating things that nobody actually wants.

The new founder skill is no longer execution alone. It is learning how to validate demand before investing certainty.

For years, startup advice revolved around one central idea: Build the product, launch it, then figure out how to monetise it later.

That approach made sense when building itself was expensive. When engineering resources were scarce, simply getting a product into the market was an achievement.

But in today’s environment, where AI dramatically reduces the cost and speed of execution, the question has changed.

It is no longer, “Can we build this?”

It is, “Should we build this at all?”

This distinction matters.

Many founders still spend months researching, refining, and polishing their ideas before introducing them to the market. They work quietly behind the scenes, convinced that perfection increases the chances of success.

Then launch day arrives.

And nobody buys.

Also Read: B2B founders keep skipping brand, and it is costing them more than they realise

I have seen this pattern repeatedly among aspiring entrepreneurs. They pour countless hours into creating programmes, products, and services without ever testing whether genuine demand exists.

Interest and demand are not the same thing.

Someone might follow you because they find you entertaining. They may like your posts, comment enthusiastically, or even share your content with others.

None of those behaviours guarantees they will become paying customers.

Revenue reveals something that engagement alone cannot.

It reveals commitment.

Monetisation is often viewed as the finish line. In reality, it can be one of the earliest and most valuable forms of validation available to founders.

Revenue is information.

It tells us whether the problem is significant enough for people to pay to solve it. It tells us whether our positioning resonates. It tells us whether the timing is right.

Most importantly, it tells us whether we should continue investing our time, energy, and resources into building.

This was a lesson I learned firsthand.

Years ago, I started a media and technology venture that began as a school project. The focus was on creating something valuable and useful. Monetisation was never part of the original strategy.

People enjoyed the content.

They consumed it consistently.

However, because the audience had been conditioned to receive everything for free, introducing paid offerings later became extremely difficult.

The challenge wasn’t generating attention.

The challenge was converting attention into commercial intent.

That experience fundamentally changed how I approach new ventures today.

Also Read: Funded: SEA founders need a capital sequence, not another funding scramble

When I conceptualised Seraphina AI, I already had a version that I used internally. It helped me streamline workflows and supported my day-to-day operations.

What I didn’t have was a consumer product.

Instead of immediately building one, I asked a different question:

Would other people value this enough to pay for it?

Rather than spending months creating features based on assumptions, I started with a waitlist.

I shared the idea.

I sent newsletters.

I nurtured conversations around the problem the product was designed to solve.

Eventually, I opened pre-orders.

Only after people committed financially did I decide to invest fully in developing the consumer version of the product.

Those early customers joined in the first half of the year.

The product itself launched approximately nine months later.

Validation came before development.

Today, this philosophy shapes how I launch almost everything.

When exploring a new programme or initiative, I rarely begin by building the entire experience upfront.

Instead, I start with a waitlist.

If there is enough interest, I invite people to place a small deposit.

That deposit is not simply about generating revenue.

It is about measuring conviction.

If people are unwilling to commit a modest amount towards solving a problem, it raises important questions about whether the market truly exists.

This approach helps founders avoid one of the most expensive mistakes in entrepreneurship: building based on assumptions rather than evidence.

In an AI-powered world, ideas are abundant.

Execution is increasingly accessible.

The real constraint is no longer technical capability.

It is a judgment.

The founders who thrive in this environment will not necessarily be the ones who build the fastest.

They will be the ones who validate the smartest.

The ones who understand the difference between curiosity and commitment.

The ones who recognise that not every idea deserves to become a product.

The ones who are willing to test demand before investing in certainty.

Because when building becomes easier, discernment becomes more valuable.

I could build countless products, programmes, and systems.

Many founders can.

But if nobody is willing to use them, what is the point?

The future belongs not to founders who build everything they can.

It belongs to those who know exactly what is worth building in the first place.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post The new founder skill is knowing what not to build appeared first on e27.

India’s startup ecosystem is entering a more disciplined phase, one where capital efficiency, sustainable growth, and talent retention are taking precedence over unchecked expansion. In this environment, Employee Stock Ownership Plans (ESOPs) are no longer viewed as optional perks; they are becoming a critical lever in people strategy.

For founders and HR leaders alike, the question is no longer whether to offer equity, but how to structure it effectively. Despite their growing importance, ESOP pools are often designed reactively, shaped by investor expectations or immediate hiring needs rather than long-term workforce planning. This can lead to misalignment between business goals and employee incentives. Having said that, a more deliberate approach is needed.

ESOPs as a strategic people lever

At a fundamental level, an ESOP pool represents a portion of company ownership reserved for employees. But from a people and culture standpoint, it serves a deeper purpose. Well-designed ESOPs:

• Strengthen alignment between employee performance and business outcomes
• Enhance retention, particularly in critical and leadership roles
• Enable startups to compete for talent despite cash compensation constraints
• Foster a sense of ownership and long-term commitment

In talent-scarce sectors, ESOPs can significantly influence offer acceptance and employee loyalty, especially when employees clearly understand their potential value.

Also Read: From perk to power: Rethinking ESOPs in the modern talent economy

Moving beyond the “standard percentage” mindset

A common mistake organisations make is relying on broad benchmarks when determining ESOP pool size. While many Indian startups allocate between five per cent and 25 per cent, this range offers limited guidance without context. The more relevant considerations include:

• Workforce expansion plans over the next two to three years
• Seniority mix and critical roles to be hired
• Market competitiveness for key talent segments
• Investor expectations and future funding rounds

For HR leaders, this is an opportunity to play a more strategic role, linking equity allocation directly to workforce planning rather than treating it as a finance-driven decision.

Structuring ESOPs: governance matters

An effective ESOP programme is not just about allocation; it requires robust governance and operational clarity.

• Clear ownership and administration: Organisations should define who is responsible for ESOP management, typically a combination of leadership, HR, and finance. This includes grant approvals, compliance, and ongoing communication.
• Vesting design as a retention tool: Vesting schedules are one of the most powerful retention mechanisms within an ESOP framework. Standard structures—such as a four-year vesting period with a one-year cliff—encourage continuity while rewarding long-term contribution. However, companies may need to tailor vesting terms for senior hires or critical roles.
• Thoughtful grant strategy: Equity distribution should be intentional —

• Early-stage employees may receive higher allocations due to higher risk
• Performance-based grants can reinforce meritocracy
• Reserving equity for future leadership hiring is essential for scalability

A static, one-time allocation approach often limits flexibility as the organisation grows.

Managing dilution while driving value

Dilution remains a key concern for founders when creating or expanding ESOP pools. However, it should be viewed through a value-creation lens. Strategic dilution used to attract and retain high-impact talent can significantly enhance enterprise value over time. From a people perspective, the focus should be on ensuring that equity allocation drives:

• Business growth
• Leadership stability
• Long-term employee engagement

The trade-off is not ownership versus dilution; it is short-term control versus long-term value creation.

Also Read: The best new year resolutions for startup founders: Offering ESOPs that actually work

Choosing the right equity instruments

While stock options remain the most widely used ESOP structure in India, organisations are increasingly exploring alternatives such as:

• Restricted Stock Units (RSUs)
• Employee Stock Purchase Plans (ESPPs)
• Phantom stock or cash-settled plans

Each instrument differs in terms of taxation, complexity, and employee perception. HR and leadership teams must align the choice of instrument with:

• Company stage and liquidity outlook
• Employee demographics and financial awareness
• Administrative and compliance capabilities

Bridging the employee understanding gap

One of the most overlooked aspects of ESOP programmes is employee communication. While equity is often positioned as a high-value benefit, many employees lack a clear understanding of vesting timelines, exercise processes, tax implications and realistic value scenarios. This gap can reduce the perceived value of ESOPs, even when the underlying structure is strong. Organisations that invest in ESOP education, through workshops, dashboards, or transparent communication, tend to see higher engagement and retention outcomes.

Risks of poorly designed ESOP programmes

Without careful planning, ESOPs can create unintended challenges:

• Over-allocation leading to excessive dilution
• Under-allocation reduces competitiveness in hiring
• Lack of transparency impacting employee trust
• Compliance and regulatory risks
• Administrative complexity and cost

For HR leaders, this underscores the need to treat ESOPs as an ongoing programme rather than a one-time initiative.

Also Read: How to do ESOP right for your startup

Building a culture of ownership

As India’s startup ecosystem matures, ESOPs are becoming more meaningful due to increasing liquidity events such as IPOs, buybacks, and secondary transactions. However, the true impact of ESOPs extends beyond financial outcomes. When implemented effectively, they contribute to stronger accountability, long-term decision-making and a culture where employees think and act like owners. This cultural shift is often what differentiates high-performing organisations from the rest.

Final thoughts

ESOP pools are not merely financial structures; they are integral to how organisations attract, retain, and engage talent. For founders and HR leaders, the priority should be to:

• Align ESOP design with business and workforce strategy
• Build transparent and well-governed frameworks
• Continuously evolve programmes as the organisation scales

Ultimately, the success of an ESOP programme is not defined by how much equity is allocated, but by how effectively it aligns people with the company’s long-term vision. Because sustainable growth is rarely built by founders alone, it is built by teams that feel invested in the outcome. 

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post Rethinking ESOP pools in India: Building ownership without losing control appeared first on e27.

I was chatting with my mother last week, and she mentioned the weavers of Bengal.

Not as history. As family memory, the way an older generation talks about things their grandparents lived through. Dhaka muslin had once been the finest textile in the world, exported across Europe, Asia, and the Arab world for centuries. Then the Industrial Revolution arrived. Manchester mills, British tariffs against Indian cotton, and a few decades later, the weavers of Bengal — generations of inherited craft, an entire economic ecosystem — were destitute. The skill did not save them. The market for the skill simply went away.

I have been thinking about that conversation ever since. Because I am also a professor at a business school, and the question I get asked most often, by students and by parents of students, is some version of: what should they study, what should they do, how should they prepare for the workforce of tomorrow?

And I do not have an honest answer that is also a comfortable one.

The thing we cannot keep saying

For two years, the comfortable position in education circles has been that AI is a productivity tool. That it will augmentknowledge workers, not replace them. That the disruption will be gradual, manageable, similar to other technology cycles.

That position is becoming harder to hold honestly.

In May 2025, Dario Amodei, the CEO of Anthropic — one of the companies actually building this technology — told Axios that AI could eliminate roughly 50 per cent of entry-level white-collar jobs within one to five years, and push unemployment to between 10 per cent and 20 per cent. He named tech, finance, law, and consulting specifically. The line that has stayed with me: “We, as the producers of this technology, have a duty and an obligation to be honest about what is coming. Most of them are unaware that this is about to happen.”

A year later, the data is moving in that direction. Big Tech hiring of new graduates has dropped roughly 50 per cent from pre-pandemic levels, according to venture firm SignalFire. Wall Street banks have announced cuts concentrated in entry-level analyst seats. Tech entry-level hiring fell 30–50 per cent across 2025. The first rung of the white-collar ladder is the one being sawed off.

Also Read: The real AI threat isn’t your job, it’s your mind

This is not the metaverse. This is not crypto. Those were narratives in search of use cases. What is happening now is the opposite — capability arriving faster than the use cases, faster than the labour market, faster than education systems can adapt. Every senior leader I speak with this year is seeing it inside their own organisation.

And the next wave is physical

The instinct so far has been to tell young people: go into the trades. Become a plumber or an electrician. The body is safe even if the desk job is not.

I do not think we get to say that for much longer, either.

Self-driving vehicles, until recently a punchline, are now running commercial robotaxi services in multiple cities across the US and China. Humanoid robotics that two years ago could barely walk are now folding laundry and stocking shelves in pilots. The combination — large models meeting physical actuators — is what people in the field are starting to call physical AI. It is at roughly the stage knowledge AI was at in 2022. Look at how far that has come in three years.

I am not predicting that plumbers will disappear by 2030. I am saying I am no longer willing to tell a sixteen-year-old that physical work is a permanent moat. The honest answer is we don’t know. And the pace at which that answer keeps moving makes any specific prediction we make today suspect by next year.

What we cannot predict, and what that means

Here is the other half of the honesty.

The most lucrative careers of the last twenty years are the ones nobody in 2005 could have advised a child to prepare for. The full-time YouTuber. The Twitch streamer. The prompt engineer. The TikTok creator earns more than a partner at a top consultancy. The DevOps engineer. The growth marketer. The mobile app indie developer. None of these was on a syllabus. None had a college pathway. The most we could have done in 2005 was say: the internet seems important; learn to use it, follow your interests, be ready to invent the rest.

This will be true again. Almost certainly more so. There will be wealth, professions, and entire categories of human work that we cannot picture from here and that will become obvious in retrospect. The graduates of 2026 are going to invent jobs we do not yet have words for.

This is the strangely hopeful part of the answer. The thing we cannot do is hand them a map. The thing we can do is make sure they are equipped to draw one.

Also Read: The future is full of humans working with humans, AI systems and other technologies

What I tell graduates now

I have stopped trying to point to specific professions as safe harbours. Instead, I share three things, in roughly this order.

Become fluent with AI before it becomes furniture

Not as a search engine. As a thinking partner, a builder, a critic, a research team in your pocket. The graduates who treat AI as a tool to dodge will be displaced by the graduates who treat it as a force multiplier. The latter group is small today. It will be the entry condition tomorrow.

Build judgment around something you genuinely care about

AI is flattening the cost of producing anything; what becomes scarce is taste, judgment, and the ability to decide what is worth producing. That cannot be taught from a syllabus. It is built by going deep on something — a craft, a domain, a question — that you would care about even if nobody paid you for it. The depth becomes the platform from which you can leverage AI. Breadth without depth produces nothing memorable.

Expect to reinvent yourself, and treat it as normal

My generation built careers around the idea that you would do one thing well for thirty years. The next generation will need to be comfortable doing several things across thirty years, with two-to-three-year reinvention cycles. This is uncomfortable to us. It is not, it turns out, uncomfortable to them. The teenagers I meet are already pattern-matching to this faster than their parents are.

What my mother actually said

After we talked about the weavers, my mother said something I keep returning to. She said the weavers’ children eventually found new ways to live. Not the same way. Not as wealthy, not for a long time. But Bengal did not end with the looms. Something else came after.

That is the most honest thing I can say to a young person right now. The looms you were trained for are changing under your feet. We do not know exactly what comes next. But something will. And the people who do best in any disruption are the ones who stop arguing with the change and start positioning for what is on the other side of it.

The youth I meet are already doing this. Quietly, mostly without us. They do not need us to predict their future. They need us to be honest about ours.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post The weavers of Bengal, my mother, and what to tell tomorrow’s graduates appeared first on e27.

As AI reshapes the marketing function, Southeast Asian startup founders face a deceptively simple question: what does good actually look like now?

AI is restructuring the marketing function faster than most startups have had time to notice. The skills that made a strong marketing hire in 2022 are being automated. The skills that actually matter now are different, and most hiring managers don’t yet have a clear framework for identifying them.

It’s not a question of whether AI will replace marketers. It has largely already replaced specific tasks. The more useful question for founders and operators is: given that, what should your marketing team actually look like?

The execution layer is gone

For lean startup marketing teams, which describe most of the Asia region, AI has effectively eliminated the cost of execution. Content production, campaign setup, basic reporting, and social scheduling: these are now table stakes that AI handles faster and cheaper than a junior hire.

That sounds like good news. In some ways it is. But it creates a structural problem. Many early marketing hires in startups were valued precisely for their ability to execute at volume. If that’s the primary value proposition, the role is under pressure.

A recent conversation with fintech marketing leaders across the region made this tension explicit. Teams are at wildly different stages of AI adoption, from basic prompting to fully agentic workflows, and the gap between early adopters and the rest is widening fast. The consensus: the most valuable marketing hire right now is someone who can adapt to change, operate across multiple functions, and direct AI systems rather than just use them.

Also Read: AI as an audience: Welcome to the citation economy

The profile that keeps coming up: T-shaped specialists who can act as orchestrators. Depth in one discipline, whether that’s demand generation, brand, or content strategy, combined with enough breadth to work across the AI toolchain. Pure generalists, interestingly, may be losing ground. The winning profile is depth plus adaptability, not breadth alone.

Three questions worth asking before your next hire

• Can they tell when AI output is wrong?

Anyone can generate copy, build a campaign brief, or pull a competitive analysis with AI now. The rarer skill is editorial judgment: knowing immediately when the tone is off, the claim is shaky, or the output doesn’t reflect your brand. For APAC startups operating across multiple markets, this is especially critical. AI tools trained predominantly on Western data consistently underrepresent Asian consumer behaviour, local nuance, and regional context. A marketer who can catch that gap and correct for it is genuinely valuable. One who can’t will ship content that quietly erodes trust.

• Are they waiting to be trained, or training themselves?

Only 25 per cent of workers receive formal AI training from their employers, even as skills in AI-exposed roles are evolving 66 per cent faster than other jobs. The marketers pulling ahead aren’t waiting for a curriculum. They’re running experiments, building workflows, and developing their own framework. For founders evaluating candidates, this is a useful signal. Ask what they’ve built or tested with AI in the last three months. The answer tells you a lot.

• Do they understand the trust problem?

This one is particularly relevant in fintech and financial services, but it applies across sectors. AI-generated content at scale risks producing what some are calling “AI slop”: homogenised, generic output that erodes brand differentiation and credibility. In categories where trust is the product, that’s an existential risk, not a content quality issue. The marketer who understands this, who treats AI as a tool for amplification rather than a replacement for judgment, is the one who protects your brand as you scale.

The build vs buy question

One unresolved tension for startup founders right now: whether to build AI marketing capabilities in-house or buy them through agencies and tools. The honest answer is that most startups are doing both, somewhat chaotically, without a clear framework for when each makes sense.

Also Read: The future is full of humans working with humans, AI systems and other technologies

A few rough principles worth considering. Use AI tools for execution that’s repeatable and low-stakes: content variations, SEO drafts, campaign copy. Retain human judgment for anything that touches brand voice, customer trust, or strategic positioning. And be cautious about cutting agency relationships entirely in favour of AI-generated output, the consensus among marketing leaders is that AI isn’t yet ready to own branding at scale. The cost savings can be real; the brand risk is also real.

What this means for how you structure the function

The CMO or marketing lead role is shifting toward orchestration, setting creative and strategic direction while AI handles activation.

AI fluency across the function is now a baseline requirement, not a specialist skill. That doesn’t mean everyone needs to be a prompt engineer. It means everyone needs to understand enough to work with AI intelligently, to direct it, evaluate its output, and know when to override it.

APAC’s talent scarcity makes this more acute. Skills shortages already affect 77 per cent of employers in the region, with sales and marketing among the hardest roles to fill. The pool of candidates who combine domain expertise, AI fluency, and genuine regional judgment is small. Founders who know what they’re looking for and can articulate it clearly in a job description have a meaningful advantage.

The talent reset is already underway. The startups that adapt their hiring frameworks now will be better positioned than those still hiring for the job that existed three years ago.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post What AI means for your next marketing hire appeared first on e27.

One of the fastest ways to weaken an AI programme is to declare it responsible before the organisation has agreed on what that word means in practice.

This is a common mistake because responsible AI sounds mature, board-ready, and difficult to argue against. It travels well in policy documents, governance forums, investor language, and internal announcements. It signals seriousness. It suggests the organisation has thought ahead. It gives the impression that the hard questions are already under control.

Often, they are not.

In many companies, responsible AI is still being treated as a label applied after the real decisions have already been made. The model is selected, the use case is funded, the vendor is approved, the pilot is underway, and then the organisation asks how to make the initiative responsible. By that point, the most important definitional work has usually been deferred. Nobody has been forced to settle what kind of system this actually is, what kind of judgment it is influencing, what kind of harm matters most, what level of error is acceptable, what counts as meaningful human oversight, or which decisions should never be delegated to probabilistic systems at all. 

Most responsible AI programmes are stronger on language than on meaning

The surface signs of seriousness are now familiar. Principles are published. Review committees are formed. Risk templates are created. Training is rolled out. Human in the loop language appears in design documents. Fairness, transparency, explainability, and accountability are all referenced in the right places.

None of this is useless. Much of it is necessary. But none of it matters enough if the core terms remain vague.

What exactly counts as a high impact use case?  What counts as decision support rather than decision making? What counts as a customer affecting output? What counts as automated action? What counts as a material model change? What counts as explainable enough for the real context in which the system will be used? What counts as acceptable performance when the harm is not evenly distributed? What counts as sufficient review when the humans involved do not fully understand the model but are still expected to sign off on its behaviour?

These are not drafting issues. There are operating issues.

The real weakness is the definition debt

Every organisation understands technical debt. Fewer understand the definition of debt.

Definition debt accumulates when an institution moves faster on deployment than on conceptual clarity. It uses broad terms that sound robust but remain internally unstable. It talks about safety, fairness, explainability, oversight, harmful use, customer impact, model drift, and accountability as though these were settled ideas, while different teams are quietly operating with different meanings.

Also Read: Responsible AI won’t scale on good intentions alone

This creates the worst kind of governance problem because it often looks like alignment from a distance.

Legal may think human oversight means a named approver exists in the process. The product may think it means a user can technically ignore the model output. Engineering may think it means the model is not directly triggering an automated downstream action. Operations may think it means an analyst glances at the result before moving on. Audit may think it means there is an evidential record after the fact. Everyone uses the same phrase. Nobody is governing the same reality.

That is the definition of debt in action. The language of control exists, but the operational meaning remains fractured. Over time, this debt becomes expensive. 

Responsible AI fails first as a framing problem

Much of the current debate still assumes that responsible AI is mainly a model problem. How do we reduce bias? How do we improve explainability? How do we strengthen monitoring? How do we govern vendors? How do we prevent misuse?

Those are important questions, but they often arrive too late.

The first failure is usually one of framing. The organisation does not define the system in a way that matches the consequences it is about to create.

A model assisting with internal drafting is one thing. A model shaping customer communications, fraud handling, cyber response, financial recommendations, hiring decisions, investigation summaries, claims triage, or exception management is something else entirely. Yet many institutions still group these under the same technology umbrella and then try to manage them through generic policy language.

That is not governance. That is category collapse.

A serious responsible AI programme starts by distinguishing what kind of influence the system is being granted. Is it informing, recommending, ranking, screening, approving, acting, or persuading? Is it being used in a reversible context or an accumulative one? Is the output advisory in theory but determinative in practice? Is the system affecting a user directly, or affecting the employee who affects the user? Is the harm visible immediately, or does it compound quietly through repeated use?

A more mature approach begins by accepting that the big words in responsible AI are not self-executing.

Fairness for what decision, against what baseline, across which groups, measured over what period, with what acceptable trade-offs. Safety for what use case, against which harms, under what misuse assumptions, with what residual risk tolerance? Oversight by whom, with what expertise, with what authority to intervene, and with what evidence available at the moment intervention is needed. Explainability for which audience, for what decision, and with what purpose. Accountability is assigned to which actor when the output was produced by one team, approved by another, deployed by a third, and acted on by a fourth.

Also Read: 5 dimensions of responsible AI: Enhancing societal needs with blockchain

These are definitional questions disguised as governance questions.

That matters because responsible AI has become crowded with high-level commitments and light on decision-grade clarity. Too much of the discussion still assumes that shared vocabulary means shared understanding. It does not.

Real governance starts when the organisation is willing to pin terms down hard enough that they shape investment, architecture, approval rights, monitoring design, incident response, and executive accountability.

Until then, the programme is mostly speaking in values while operating in approximation.

Process matters, but only when it is tied to consequence

To say responsible AI is a process is not to defend bureaucracy. It is to argue that responsibility must be continuously produced, not merely declared.

A serious process does not begin and end at model approval. It starts with use case framing, continues through design, testing, deployment, monitoring, escalation, retraining, change management, incident learning, and sometimes withdrawal. It recognises that the model will be used differently from how it was originally described, that humans will adapt around it, that workflows will stretch it into adjacent roles, and that the meaning of harm may change once the system interacts with real customers, regulators, operations, and frontline pressure.

That is why a checkbox cannot work. A checkbox assumes the relevant question has been settled at a single moment. Responsible AI assumes the opposite. It assumes the organisation must keep asking whether the system is still behaving within the boundaries that were originally judged acceptable, whether those boundaries were defined well enough in the first place, and whether the real use of the system has drifted beyond what was approved.

This is not red tape. It is the minimum discipline required when deploying systems whose outputs can look more stable than their consequences.

—

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.

Join us on WhatsApp, Instagram, Facebook, X, and LinkedIn to stay connected.

The post Responsible AI is a process, not a checkbox appeared first on e27.