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.

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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. 

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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.

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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.

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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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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.

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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.

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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.

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

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