A startup changes the model behind its AI feature. The new model is faster, cheaper and performs better on public benchmarks. The engineering team runs its tests, deploys the update and waits for the improvement.

Instead, support tickets begin to arrive. The assistant is less accurate on short questions. It misunderstands customers who mix languages. Nothing is completely broken, but the product is noticeably worse.

This is one of the difficult realities of building with generative AI: a system can pass every conventional software test while its behaviour deteriorates. The cheapest protection is not another monitoring platform or a more powerful model. It is a small, carefully maintained collection of real examples known as a golden dataset.

AI regressions are different

Traditional software is usually tested against predictable outputs. Give a function a particular input and it should return an exact result.

Generative AI does not work that way. Two answers can use entirely different words and still be equally correct. Conversely, an answer can sound fluent and professional while omitting a critical fact or inventing something the system does not know.

This makes informal testing dangerously attractive. Someone tries five prompts in a staging environment, reads the answers and concludes that the new version “looks better”.

That judgement becomes unreliable as the product grows. A support assistant serving several Southeast Asian markets may encounter English, Thai, Vietnamese and code-switching within the same conversation. Users submit fragments, screenshots, misspellings and requests that the product team never anticipated.

The polished questions used in demonstrations rarely resemble this traffic. A golden dataset makes quality visible. It contains representative user inputs together with a description of what a successful response must and must not do. The team runs the same examples whenever it changes a model, prompt, retrieval pipeline or tool.

It does not replace production monitoring, user feedback or A/B testing. It catches problems before those slower signals arrive.

Begin with real behaviour

The first version does not need thousands of examples. Thirty to fifty inputs from actual usage are enough to begin testing one important product behaviour.

For a customer-support assistant, that behaviour might be answering refund questions, for a financial product, it might be explaining a transaction without offering unauthorised advice, and so on.

Production examples are far more valuable than questions invented during a workshop. They contain the ambiguity, incomplete information and unusual phrasing that expose weaknesses in the system.

Also Read: SEA’s AI boom has a water problem it cannot offset away

Naturally, using production traffic requires appropriate consent, access controls, retention rules and removal of personal information. If the product has not launched, examples from internal testing or a closed beta can be used temporarily. Synthetic examples are useful for getting started, but they should gradually be replaced by real interactions.

The objective is not to construct a perfect benchmark. It is to capture a small but recognisable sample of how people actually use the product.

Test failures, not just the happy path

Many teams create evaluation sets that resemble product demonstrations: clear questions, correct terminology and complete information. Unsurprisingly, the system performs well.

A useful dataset should contain the situations most likely to cause damage. These might include ambiguous requests, unsupported languages, missing account information, contradictory documents, attempts to override instructions or questions that should be escalated to a person. Support tickets and user complaints are often the best source of such examples.

Every production failure should leave something useful behind. Once the immediate problem has been resolved, the interaction should become a new evaluation case. That ensures the same class of failure is less likely to return quietly after the next update.

Over time, the dataset becomes a record of what the team has learned about its users and its product.

Evaluate outcomes, not identical wording

For open-ended AI outputs, requiring an exact answer is usually the wrong approach.

Consider a user asking for a refund. A successful response might need to mention the refund policy, correctly identify the order, avoid promising an outcome and call the account lookup tool before answering. Many different responses could satisfy those requirements.

The evaluation should therefore describe the outcome:

• What information must appear?
• What must never appear?
• Which tool or source must the system use?
• When must the request be escalated?
• Are there limits on length, latency or cost?

Also Read: Thailand’s mobility future will be decided by data, not just vehicles

Some checks are inexpensive and objective. A test can verify whether the response contains a required fact, follows a defined structure, calls the correct tool or stays within a token limit.

Subjective qualities such as tone or clarity may require human review or another model acting as a judge. But expensive AI-based scoring should not be the default. Use the cheapest test capable of detecting the problem.

Both OpenAI’s evaluation guidance and Anthropic’s work on agent evaluations emphasise structured, task-specific evaluation rather than relying on general benchmarks alone.

Keep the dataset small enough to trust

A dataset becomes useless when it is too large for anyone to inspect. Each regression report should show the affected example, the previous response, the new response and the reason it failed. A dashboard announcing that quality fell from 84 to 81 per cent is not enough. Engineers need to see what became worse.

The dataset should also be versioned alongside the product. When expected behaviour changes, the reason should be recorded. A small portion of the examples can be held back from everyday development so the team does not unconsciously tune the system only to the cases it sees.

Finally, someone must own the dataset. Shared responsibility often means no responsibility. The owner should add newly discovered failure modes, remove obsolete examples and ensure evaluations continue to run as the product changes.

A practical starting point

A startup can establish a useful regression process within a week. Choose the single AI behaviour that matters most to customers. Collect 30 to 50 representative inputs. For each one, write down two to four conditions that define a successful outcome. Automate the inexpensive checks and run them whenever the relevant system changes.

AI teams will never eliminate uncertainty completely. Models change, products evolve and users find new ways to surprise us. But a team should always be able to answer one basic question before releasing an update: did this make the product better or worse? A golden dataset is the cheapest reliable way to find out.

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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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In Southeast Asia’s heat and humidity, keeping fruit, vegetables and ready-to-eat food fresh is not just a logistics problem but an economic one, too. Produce can lose value at every stage of the chain, from farm to wholesaler to supermarket shelf, while restaurants and households often throw away food long before it should have spoiled.

Singapore-based N&E Innovations is betting that part of the answer can come from the waste stream itself.

Also Read: Deeptech’s secret: Ignore the market, master the engineering, and let opportunity find you

The deeptech company has raised approximately US$1.7 million in Series A funding led by Australian agrifood investor Tundra Capital. SGInnovate, The Radical Fund, Archipelago VC and SG7 Group also joined the round, alongside existing backers Cercano, SEEDS Capital, Elev8 Capital and Qian Hu Corporation.

Founded in 2020 by biomedical scientist Didi Gan, N&E has developed ViKANG99, a patented edible antimicrobial ingredient made from upcycled agricultural by-products, including discarded cashew nut husks. The company says the ingredient can help fresh produce last up to four times longer by slowing the growth of bacteria and mould.

“Food waste is usually seen as something we need to get rid of. We see it as a resource,” Gan said. “We can take something like a discarded cashew nut husk, extract the compounds that naturally fight microbes and turn them into an ingredient that can help protect food.”

From cashew husks to cling wrap

ViKANG99 works by extracting naturally occurring antimicrobial compounds from agricultural waste, refining them into a food-grade ingredient, and embedding or applying that ingredient across different use cases.

One of N&E’s first commercial products is The Orange Wrap, which the company describes as the world’s first antibacterial cling wrap. Conventional cling film acts largely as a barrier. N&E’s version incorporates ViKANG99 into the material, allowing it to actively inhibit microbial growth on food surfaces.

The same core ingredient is also being used in KeepWell, N&E’s plant-based cleaning and hygiene range, and in post-harvest agriculture products that can be applied to fruit and vegetables after harvest. These include a post-harvest wash and guard system designed to suppress mould and microbial growth during storage, transport and retail.

That matters in a region where supply chains are often fragmented and temperature control can be inconsistent outside premium channels. In markets such as Vietnam, Indonesia and the Philippines, produce may move through multiple intermediaries before reaching consumers. Even in Singapore, where retail standards are high, the country’s reliance on food imports makes shelf life a national resilience issue as much as a supermarket concern.

Also Read: The environmental ethics of AI should be a product decision, not a sustainability footnote

Food waste is also a stubborn problem for Singapore. The city-state generates hundreds of thousands of tonnes of food waste each year, according to official waste statistics, while importing more than 90 per cent of its food. Any technology that can keep produce usable for longer without adding heavy infrastructure could therefore have relevance beyond a niche sustainability story.

Commercial traction beyond the lab

N&E is not entering this round as a company still searching for its first use case. Its customers already include Singapore Airlines and supermarket chain Sheng Siong. Its KeepWell hand sanitiser was also selected for inclusion in the official 2026 Singapore National Day Parade fun pack, putting the company’s technology in front of a mass local audience.

The new capital will be used to expand ViKANG99 across three areas: active antimicrobial food packaging, plant-based cleaning, and post-harvest agriculture. N&E also plans to support regulatory programmes, international market entry and team expansion.

Australia is shaping up as one of its key growth markets. Kinoya was appointed exclusive Australian distributor for the KeepWell range in June, with selected IGA supermarkets in Sydney lined up as an initial retail channel. KeepWell products are already being trialled by hospitality businesses including M Bar Thai Eatery, Show Nom Dessert and Show Neua Thai.

The Orange Wrap has also entered Australia’s foodservice market through Perth-based distributor Familiar Goods, which supplies hotels, restaurants and other foodservice operators.

For N&E, Australia offers a useful test bed. It is a sophisticated retail and foodservice market with strict food safety expectations, but it also faces long supply routes and high spoilage costs across fresh produce. Success there could strengthen N&E’s case with partners in other developed markets, including the UK and Germany, while giving it credibility in regional export markets such as Vietnam and Taiwan.

A crowded but growing shelf-life race

N&E is operating in a global market where several startups and established players are trying to extend food shelf life without relying solely on cold-chain expansion or traditional preservatives.

US-based Apeel Sciences, one of the best-known companies in the space, developed plant-derived coatings that slow water loss and oxidation in produce. Hazel Technologies, also from the US, works on packaging inserts that regulate ripening and reduce spoilage.

AgroFresh, a more established post-harvest technology player, provides freshness solutions for fruit supply chains, while startups such as Mori and Sufresca are exploring edible or biodegradable coatings.

N&E’s differentiation lies in its use of upcycled agricultural waste as an antimicrobial source and its attempt to apply the same active ingredient across packaging, cleaning and post-harvest treatment. That breadth could open several revenue channels, though it also means the company must navigate different regulatory regimes, customer buying cycles and product-performance expectations at once.

The company’s model is described as licensing-led, with ViKANG99 embedded into partner products rather than only sold through N&E’s own branded lines. If executed well, that could help it scale without building a large manufacturing footprint in every market. But licensing in food and packaging is rarely quick; partners typically require evidence on safety, performance, cost, durability and consumer acceptance.

Why investors are paying attention

Tundra Capital Managing Partner Timothy Hui said the investment fits the firm’s focus on technologies that address structural challenges in food systems.

“N&E Innovations is one of those startups that ticks every box; their technology transforms agricultural waste into a valuable ingredient, their antimicrobial active ingredient is applicable at various points across the food value chain, and they have demonstrated customers globally love their product,” Hui said.

The round also reflects a broader investor interest in climate-adjacent technologies that can show near-term commercial value. Food waste reduction has often been framed as an environmental goal, but for supermarkets, restaurants and growers, the argument is simpler: less spoilage means better margins.

Also Read: Why Southeast Asia’s next climate unicorn might be built from farm waste

For Southeast Asian startups, that commercial framing is important. Sustainability products can struggle when they are priced as a moral choice. They stand a better chance when they solve a cost, compliance or operational problem. N&E’s challenge now is to prove that its food-waste-derived antimicrobial can do so consistently across geographies and applications.

The company’s story is also unusually circular: agricultural waste becomes an ingredient that helps prevent more food from becoming waste. If N&E can translate that logic into scalable products and partnerships, it could turn a Singapore lab-born idea into a practical tool for food systems well beyond the region.

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For decades, talent management has operated on a seemingly logical premise: identify your top performers, your A-players, and invest in them disproportionately. This approach, popularised by McKinsey’s War for Talent in the late 1990s, promised that organisations could secure competitive advantage by systematically differentiating their workforce. The logic was seductively simple: measure performance annually, rank employees on a curve, and focus resources on the highest-rated segment.

The universal A-player framework spread across industries because it offered HR leaders a simple, comparable metric for talent quality. If every role could be measured against the same scale, then talent could be managed like capital, allocated efficiently, tracked rigorously, and optimised continuously. This aspiration reached its logical conclusion in the talent supply chain movement, where frameworks like the R7 model (Recruit, Rate, Retain, Redeploy, Redevelop, Release, Rehire) attempted to apply manufacturing discipline to workforce management.

The problem is that the universal A-player never actually existed. Consider a global bank that rated a call centre agent and an investment banker on the same 1-5 scale. The agent who resolved 95 per cent of calls on first contact received a 3 because her manager was a tough rater. The banker who closed a mediocre deal received a 5 because his manager was lenient. The bank then used these ratings to allocate training budgets and promotion opportunities. Research has found that individual rater biases can account for up to three times as much variance in performance scores as actual employee performance. When a manager rates an employee, the result reveals more about the manager’s evaluation style than about the employee’s capabilities.

This bias problem becomes exponentially more dangerous when flawed historical ratings are used as training data for AI systems. Consider Amazon’s AI recruiting tool: trained on resumes submitted over a decade, it learned to penalise resumes containing the word women’s (e.g., Captain of women’s chess club) because most past hires were men. Algorithms trained on biased human decisions do not eliminate bias. They scale it with terrifying efficiency.

Why the universal A-player cannot survive the AI-driven supply chain

The talent supply chain metaphor demands real-time visibility into quality, throughput, and defects. Yet the universal A-player provides none of these. It suffers from three terminal pathologies.

First, it is static. Annual performance reviews look backward six to twelve months. Consider a cybersecurity firm whose annual reviews in December rated engineers on skills relevant to pre-AI threat landscapes. By January, generative AI had transformed the work entirely. Those A-players from December lacked the capabilities needed for February’s critical incident response. The organisation knew who was excellent yesterday, not who was right for today.

Second, it is retrospective. The universal A-player relies entirely on lagging indicators, completed projects, closed deals, past ratings. Consider a retail chain during the pandemic shift to e-commerce. Their A-player store managers, rated highly on in-person sales, struggled to adapt to digital fulfilment. Meanwhile, a previously B-player assistant manager who had built the store’s social media presence in her spare time turned out to be the ideal candidate. The lagging indicators missed her entirely.

Third, it is universal. Traditional A-player frameworks apply generic traits, leadership, initiative, strategic thinking, across wildly different roles. Consider a manufacturing company that rated a warehouse supervisor and a data scientist on the same strategic thinking competency. The supervisor, whose role demanded real-time operational decisions about shift scheduling, scored poorly. The data scientist, who spent weeks developing long-term forecasting models, scored highly. The company concluded the supervisor was a B-player and redirected development funds. Six months later, the supervisor had reduced warehouse defects by 40 per cent through a simple reorganisation. The universal scale had measured the wrong thing.

Also Read: Are you a human resource?

The new model: Dynamic talent matching for the AI era

The end of the universal A-player opens the door to dynamic talent matching based on real-time, role-relative fit. This new model solves both the measurement problem and the bias problem.

• Pillar one: From static to dynamic

Instead of annual ratings, the new model measures talent continuously against specific role demands. A global consumer goods company transformed its recruitment process by abandoning CVs and annual reviews entirely, replacing them with game-based assessments and video interviews analysed by AI. Candidates completed neuroscience-based games measuring problem-solving and learning agility in under 25 minutes, with results evaluated against role-specific benchmarks, not universal scales. The approach increased new hire diversity while reducing screening time by 75 per cent.

Research from Heliyon demonstrates that skill assessments can be recalibrated using neural networks that combine expert knowledge with real-time data, ensuring measurement adapts as roles evolve.

• Pillar two: From retrospective to predictive

The new model uses predictive signals instead of lagging indicators. In one documented implementation, candidates completed open-ended questions like How do you know you’ve understood what someone said? without seeing skill labels. The AI mapped free-text responses to the five to ten core tasks of the specific role. This approach prevented self-screening bias, candidates who doubted their qualifications applied anyway, and eliminated the correct answer bias of multiple-choice tests. The result: retention increased by 25 per cent and time-to-hire dropped by 23 per cent.

Research from Taylor & Francis on adaptive job recommendation systems demonstrates that such approaches achieve 92 per cent accuracy in job matching while reducing algorithmic bias by 15 per cent through adversarial debiasing mechanisms.

• Pillar three: From universal to role-relative

The new model recognises that different critical roles require different measurement criteria. A manufacturing company implemented role-specific validation by studying 226 employees, correlating assessment scores with actual job outcomes. For warehouse associates, top scorers performed at the 64th percentile on the job, while bottom scorers performed at the 33rd percentile. Top scorers were also three times less likely to be involved in safety incidents. For data analysts in the same company, the predictive criteria were completely different: pattern recognition speed and intellectual curiosity, not physical safety indicators.

Also Reda: The app worked, the product didn’t: Can we install judgement into AI agents?

Solving the bias problem through dynamic matching

The universal A-player made bias invisible. Dynamic talent matching makes bias detectable and correctable through three mechanisms.

First, role-relative measurement breaks proxy discrimination. Consider a health system where universal ratings penalised nurses who took family leave, marking them as lower commitment. Under role-relative measurement, a nurse’s fit for an intensive care role depends on clinical judgement and rapid response time, not attendance patterns from three years ago. The proxy of leave-taking no longer leaks into the prediction.

Second, continuous auditing replaces one-time validation. A technology company’s engineering team documented that the most effective bias mitigation is continuous fairness auditing using the EEOC’s four-fifths rule: a model’s scoring rate for any demographic group must be at least 80 per cent of the highest-scoring group’s rate. Leading implementations audit intersectional groups, Black women, Latinx men, where bias is often most severe. When a financial services firm ran these audits, they discovered their model systematically downgraded candidates from historically Black colleges. They retrained the model, removing the proxy signal, and hiring diversity improved without reducing performance.

Third, human-in-the-loop governance ensures accountability. Under the EU AI Act, hiring algorithms are classified as high-risk systems requiring full risk-management programmes. Employees and candidates must have the right to understand what signals influence fit scores and to contest inferences. Consider a logistics company where an algorithm flagged a driver for low reliability based on GPS data showing frequent stops. The driver contested, explaining the stops were required safety checks for hazardous materials. The system was adjusted, and the driver became one of the highest-performing team members.

Conclusion: The universal A-player is dead

The universal A-player was a useful simplification for a stable world. That world no longer exists. In an AI-driven talent supply chain where roles evolve continuously and critical talent must be redeployed in days, static, retrospective, universal ratings are worse than useless.

The new model asks a fundamentally different question: not who are our A-players? but who fits this role, right now? By shifting from static to dynamic measurement, from retrospective to predictive signals, and from universal to role-relative standards, organisations can finally escape the bias trap and manage talent with real-time discipline.

Organisations that cling to the universal A-player will find themselves measuring what no longer matters. Those that embrace dynamic talent matching will redeploy capability as quickly as they respond to changing demand. The supply chain taught us to manage inventory in real time. It is time to manage talent the same way.

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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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AI is remarkably good at producing answers. It is even better at sounding certain. Ask a difficult question and, within seconds, a system can gather information, connect ideas and return a polished explanation. Yet a harder question arrives later: what happens when reality proves the answer wrong?

I met that problem while building OnTheRice, a Singapore-based AI research publication. Part of its work involves making time-bound directional calls, saving the evidence available at the time, and checking the result later. The experience changed how I think about AI products. Producing an answer was the easy part. Preserving an honest record of it was much harder.

Freeze the answer before reality arrives

A prediction is easy to admire while its outcome is unknown. It is also easy to repair after the fact. A changed sentence, a missing timestamp or a quietly removed failure can turn poor judgement into a convincing success story.

We began locking each call before its result was known: the direction, entry price, source set, publication time and evaluation time. As at 1 September 2026, the public Founder Ledger showed 52 correct and 34 incorrect results across 86 resolved calls, or 60.47 per cent. 10 older records remained visible but excluded because they could not be verified properly.

Those figures are not proof that the system is exceptional. They are useful because they are incomplete, imperfect and inspectable. Once the misses remained on the page, wrong stopped being one category. Sometimes the reasoning failed. Sometimes the information arrived too late. Sometimes the market had already moved. Sometimes an event changed the conditions after publication.

The practical lesson is simple: save the original output, the evidence, the timestamp and the scoring rule before the outcome arrives. Otherwise, learning can become hindsight wearing a lab coat.

More links do not mean more evidence

A second problem appeared when the system began reading large numbers of reports. 10 websites may cover the same event, yet nine might trace back to one wire story. That is not ten witnesses. It is one witness with excellent distribution.

AI systems can confuse information volume with independent confirmation. Counting URLs rewards duplication. It can make a thin claim look strong simply because it travelled far.

Also Read: SEA’s AI boom has a water problem it cannot offset away

We started treating source origin as part of the evidence. Reports were grouped when they repeated the same underlying account, while genuinely independent reporting carried more weight. The practical rule is to trace claims backwards, not merely count how many pages repeat them. Three independent reports can tell us more than 100 copies.

Time belongs inside the evidence

Suppose a report at 8am says oil is falling and another at 5pm says it is rising. Which is wrong? Possibly neither. The world happened between them.

An AI system that ignores time can flatten both statements into one moment. Worse, it can use information published later to explain a decision made earlier. This creates the illusion that the system knew more than it could have known.

Useful records therefore need more than a source link. They need the time the source was published, the time the system found it, the time the output was made and the time it was assessed. This applies beyond markets. A business cannot honestly link a competitor’s price change to falling sales without knowing which came first.

Documentation is not decoration

This approach is not unique to one product. The researchers behind Model Cards for Model Reporting proposed standardised records of a model’s intended uses, evaluation and limitations. The NIST AI Risk Management Framework also treats accountability, transparency, validity and reliability as central parts of trustworthy AI.

The need is growing. Stanford’s 2025 AI Index reported 233 AI-related incidents in 2024, 56.4 per cent more than in 2023. The figure does not mean every incident could have been prevented by better records. It does show why explanations offered only after something goes wrong are not enough.

For an applied AI product, a small receipt can carry the source, timestamp, system version, original output, confidence level, known limits, evaluation rule and eventual outcome. None of this looks as exciting as a smarter model demonstration. It is far more useful during a dispute, audit or failure review.

Also Read: The app worked, the product didn’t: Can we install judgement into AI agents?

Let the system say I don’t know

AI products are designed to answer. Silence looks broken, especially in a demonstration. But forcing a decision when sources conflict or evidence is missing creates artificial certainty.

One of our hardest lessons was to separate wrong from unverifiable. The first means reality contradicted a recorded call. The second means the evidence is too weak to score it honestly. Combining them hides different problems; counting either as a win is worse.

A mature system needs permission to abstain. I don’t know yet should be a valid output when confidence falls below a clear threshold. Teams should record why the system abstained, then test whether the rule was too cautious or appropriately restrained.

Trust needs evidence

The AI industry is understandably focused on better reasoning, larger context windows and stronger models. Yet applied systems face a less glamorous test: can another person inspect what happened?

Can they see the original source? Can they see what the system said, when it said it and what changed afterwards? Can they find the failures as easily as the successes?

Building an AI that keeps receipts taught me that mistakes are not embarrassing leftovers. Properly preserved, they are training data for the product team, evidence for the user and a guard against self-deception.

A system should not earn trust by describing itself as intelligent. Show the work. Keep the misses. Let the record speak.

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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.

The post I built an AI that keeps receipts. The mistakes became the useful part appeared first on e27.

Every founder has opinions.

After all, they spend their days solving customer problems, navigating market uncertainty, raising capital, hiring talent and making decisions that shape the future of their business. Those experiences naturally produce perspectives on everything from emerging technologies and regulation to leadership, innovation and industry trends.

Yet expertise alone does not earn media coverage.

Every week, journalists receive pitches from founders who want to share their views on the latest developments in their industry. Most are ignored, not because the founders lack credibility, but because their perspectives have not been translated into stories that are timely, relevant or valuable to readers.

That is the difference between having an opinion and having a media-ready narrative.

The role of a startup PR agency or tech PR agency extends well beyond writing opinion pieces or arranging interviews. Effective thought leadership begins with understanding how journalists think, what audiences care about and how a founder’s expertise can contribute meaningfully to conversations already taking place.

When those elements come together, founder insights become more than commentary. They become stories that build credibility and strengthen a company’s reputation.

Start with the media landscape, not the founder

Many companies begin by asking what their founder wants to say.

A better question is where that conversation belongs.

Every publication serves a different audience and has its own editorial priorities. Business publications may look for commentary on market trends, while technology media often seeks practical insights into innovation, product development or investment activity. The same founder perspective can be highly relevant to one publication and completely unsuitable for another.

This becomes even more important for companies operating across Southeast Asia.

Although the region is often discussed as a single market, each country has its own media ecosystem, business priorities and cultural context. A narrative that resonates with journalists in Singapore may require a different angle in Indonesia, Malaysia or the Philippines.

For example, a founder discussing artificial intelligence could focus on regulatory frameworks for Singaporean business media, manufacturing transformation in Vietnam or digital inclusion in Indonesia. The expertise remains consistent, but the story evolves to reflect local priorities.

The best thought leadership is never one-size-fits-all. It respects regional nuances while remaining authentic to the founder’s perspective.

This is why an experienced corporate communications agency develops narratives with both editorial relevance and local market context in mind.

Understand what journalists are trying to write

Successful media relations is not simply about understanding publications. It is about understanding the journalists behind them.

Every reporter develops areas of expertise and recurring themes throughout their career. Some focus on breaking news, while others specialise in long-form analysis, policy developments or emerging technologies. Many spend years covering the same industries and are constantly looking for credible voices who can help explain what is happening beneath the headlines.

Also Read: The role of thought leadership in scaling beyond your first market

The strongest founder commentary supports those objectives.

Rather than asking, “What do we want to say?”, communications teams should ask, “What questions is this journalist already trying to answer?”

Can your founder explain why investment in a particular sector is accelerating? Have they observed changing customer behaviour before industry reports identified the trend? Can they provide practical context behind a new government policy or technological development?

When founder expertise helps journalists tell a better story, media opportunities become significantly easier to secure.

This is one reason companies work with a PR agency in Singapore. The media pool tends to be smaller in comparison, and strong media relations help you understand editorial priorities and shape founder insights to create genuine value for journalists and their audiences.

Genuine expertise is more valuable than manufactured opinions

There is a common misconception that thought leadership requires controversial opinions or bold predictions.

In reality, credibility consistently outperforms sensationalism.

Founders possess something that analysts and commentators often cannot replicate: first-hand operational experience. They understand customer challenges, market dynamics and competitive pressures because they navigate them every day.

Those experiences produce insights that are both practical and distinctive.

Instead of encouraging founders to manufacture provocative opinions, communications teams should identify recurring patterns, lessons and observations that have emerged through building the business.

Sometimes the most compelling narrative is not about predicting the future. It is about helping people understand the present.

Journalists value perspectives grounded in experience because they offer readers something genuinely useful rather than simply adding another opinion to an already crowded conversation.

Every narrative should strengthen your company’s positioning

Not every interesting opinion deserves to become thought leadership.

The most effective founder narratives reinforce the expertise that a company wants to become known for.

A cybersecurity company should consistently contribute to discussions around digital resilience and cyber risk. A fintech founder should become recognised for perspectives on financial innovation, regulation and customer trust. A climate technology business should build authority around sustainability and industrial transformation.

Also Read: Choosing the right tool: What works for news, thought leadership, influence

Over time, these repeated associations shape how journalists, customers, investors and industry peers perceive the organisation.

Visibility alone is rarely the objective.

Authority is.

Every interview, contributed article and expert quote should strengthen the same strategic positioning. This is where an experienced tech PR agency provides lasting value, ensuring media opportunities contribute to long-term reputation rather than short-term exposure.

Timing is often the deciding factor

Even exceptional insights can fail if they arrive at the wrong moment.

Media operates on relevance. Journalists are constantly looking for expert perspectives that help explain developments already shaping the news cycle, whether that involves funding announcements, new regulations, economic uncertainty, emerging technologies or changing customer behaviour.

The most successful communications strategies therefore connect founder expertise with conversations that are already happening.

Instead of waiting for inspiration, communications teams should actively monitor industry developments and identify opportunities where their founder can contribute a credible, differentiated perspective.

When expertise meets timing, media-ready narratives become significantly more compelling.

Turning expertise into influence

Founders rarely struggle with having opinions. More often, they struggle with communicating those opinions in ways that resonate beyond their own organisation.

Media-ready narratives are built at the intersection of expertise, editorial relevance, strategic positioning and timing. They are grounded in genuine experience, shaped around the needs of journalists and aligned with the company’s long-term business objectives.

That is why effective thought leadership is never simply about publishing more content. It is about consistently contributing meaningful perspectives that help audiences better understand their industry.

Whether you’re an emerging startup preparing for your first round of media engagement or an established technology company expanding across Southeast Asia, the goal remains the same.

Transform founder expertise into stories that journalists want to tell, audiences want to read and stakeholders remember long after the headlines have disappeared.

—

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 How to turn your founder’s opinions into media-ready narratives appeared first on e27.