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Meta appoints Dhruv Vohra to lead Southeast Asia business as AI and chat commerce reshape online retail

Meta has appointed Dhruv Vohra as Managing Director of its Global Business Group in Southeast Asia, putting a longtime regional executive in charge of one of the company’s most commercially important and behaviourally complex markets.

Based in the region, Vohra will oversee Meta’s commercial strategy across Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam. He will report to Benjamin Joe, Meta’s Vice President for Asia Pacific.

The appointment comes at a time when Southeast Asia’s internet economy is moving into a new phase. The region’s first big consumer internet wave was shaped by e-commerce marketplaces, ride-hailing superapps and social media-led discovery. The next one is being built around messaging, short-form video, live selling and AI-assisted advertising — areas where Meta is trying to defend and extend its role among businesses of all sizes.

Also Read: Meta, Singapore Police disrupt 3.7M scam-linked assets across Facebook and Instagram

For Meta, Southeast Asia is not simply a user-growth story. It is a test market for how commerce is evolving in mobile-first economies, where small businesses may never have built a website, but can sell through Facebook Pages, Instagram accounts, WhatsApp chats and livestreams.

“Southeast Asia is one of Meta’s fastest-growing business regions, and the work ahead is helping businesses leverage AI and messaging at the pace their customers already expect,” said Joe. “Dhruv has spent seven years close to those businesses, and he has built the teams that support millions of them. He is the right person for this next phase.”

A familiar executive returns to a changed market

Vohra joined Meta in 2019 as Director for e-commerce and digital natives, working with the platforms and online-first brands that helped reshape retail across the region. He later led small and medium business growth in Southeast Asia, before expanding that responsibility across Asia Pacific.

His return to a Southeast Asia-focused role comes after a period in which the region’s digital commerce habits have become harder to categorise. Consumers may discover a product through a creator’s video, ask questions in a chat thread, compare prices on a marketplace, wait for a double-date sale day, and complete the purchase through a wallet or bank transfer.

This behaviour has made Southeast Asia a particularly important market for Meta’s business products. The company’s advertising engine depends heavily on businesses finding customers across Facebook, Instagram, Messenger and WhatsApp. In this region, those products often sit close to the transaction itself, especially for micro, small and medium enterprises.

Vohra has also built a public profile around the region’s small business economy. He writes a LinkedIn column, Small to Scale: Inside APAC’s Innovative SMBs, and contributed to Meta’s SYNC Southeast Asia thought leadership series on digital consumers and e-commerce trends.

Also Read: Pinterest and Shopee link up to bring creator-led shopping to Indonesia

“Southeast Asia is one of the most dynamic and creative regions in the world. Trends such as business messaging and live shopping found early adopters here,” Vohra said. “No two markets here behave the same way, and the businesses growing fastest here have outgrown the borders they started in. AI has opened up even more possibilities.”

Why the role matters now

The appointment is significant because Meta’s growth in Southeast Asia now depends on more than selling ads against a large user base. Businesses are asking for clearer returns on advertising spend, better tools to manage customer conversations, and easier ways to create and target campaigns.

AI is central to that pitch. Meta, like Google and other advertising platforms, has been pushing automated tools that help businesses generate creative assets, find audiences and optimise campaigns with less manual work. For small companies with limited marketing teams, these tools could lower the barrier to running digital campaigns. For Meta, they also help keep advertisers inside its ecosystem.

Messaging is the other major lever. In much of Southeast Asia, chat is not a customer-service afterthought; it is often the storefront. Consumers ask whether an item is available, negotiate details, request delivery information and expect rapid responses. This makes business messaging a commercial infrastructure layer, not just a communications feature.

That is especially relevant in markets such as Indonesia, the Philippines and Vietnam, where social commerce has grown alongside marketplace platforms. It also matters in Singapore and Malaysia, where more mature digital advertisers are looking for automation, cross-border reach and better conversion tracking.

Also Read: TikTok deepens Vietnam commerce bet with US$980M logistics project in Ho Chi Minh City

Vohra’s task will be to align Meta’s regional business strategy with these uneven market realities. Southeast Asia is often spoken about as a single bloc, but its digital economy is split across languages, payment habits, logistics networks, regulations and consumer preferences. What works for a beauty seller in Bangkok may not work for a food brand in Manila or a cross-border merchant in Ho Chi Minh City.

The competitive field

Meta’s Southeast Asia business also faces a crowded and increasingly localised competitive landscape. TikTok has become a major force in short-form video discovery and social commerce, particularly through TikTok Shop in markets such as Indonesia, Thailand, Vietnam and the Philippines. Google remains dominant in search and YouTube advertising, while e-commerce platforms, including Shopee and Lazada, have built sizeable retail media businesses, selling ad inventory close to the point of purchase.

Superapps such as Grab also compete for merchant marketing budgets, particularly in food, mobility and financial services. For small businesses, the choice is no longer simply whether to advertise on social media, but how to divide spending across platforms that each control a different part of the customer journey.

This rivalry puts pressure on Meta to prove that its platforms can drive measurable sales, not just awareness. Apple’s privacy changes in recent years also made ad attribution more difficult across the industry, forcing platforms to rely more on first-party signals, AI modelling and in-app business tools.

In Southeast Asia, that challenge is sharpened by the region’s reliance on mobile commerce and informal selling. Many businesses still operate across multiple channels without sophisticated customer data systems. Meta’s opportunity is to make advertising and messaging simple enough for these businesses, while powerful enough for larger brands and regional merchants.

A regional leadership test

Vohra’s appointment is therefore less about a routine leadership reshuffle and more about Meta’s next commercial chapter in Southeast Asia. The company is operating in a region where consumer behaviour often moves faster than formal retail infrastructure, and where business adoption can jump quickly when a tool proves useful.

The six markets under his remit together represent a large and diverse digital economy, with hundreds of millions of internet users and a deep base of entrepreneurs selling online. They are also markets where trust, affordability and responsiveness matter as much as technology.

Also Read: Grab’s US$1.49B Atome deal signals a deeper race for SEA’s credit economy

If Meta can turn AI and messaging into practical tools for these businesses, it could strengthen its position at the centre of Southeast Asia’s commerce stack. If it falls short, rivals with tighter links to entertainment, search, marketplaces or payments will keep pulling merchant budgets in their direction.

For Vohra, the job is to navigate both sides of that equation: helping businesses grow inside a region that rarely follows a single playbook, while keeping Meta relevant as the way Southeast Asia shops, sells and advertises continues to change.

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MoneyHero’s activist investor wants a sale. Richard Li holds the real vote

Every comparison site promises to find you a better deal. Jonathan Honig thinks it’s time MoneyHero found one for itself.

On 29 September, the investor, who says he owns about 9 per cent of MoneyHero’s Class A shares, published an open letter asking the board to hire an independent adviser and explore a sale.

Honig’s list of complaints is long and hard to dispute. There is still no permanent CEO six months after Rohith Murthy’s exit. Revenue slid from US$80.7 million in FY2023 to US$73.4 million in FY2025, against a promised US$100 million. And the share price of US$0.675 is down more than 88 per cent since the October 2023 debut.

Also Read: MoneyHero’s winning quarter has a US$6.7M problem

But open letters are theatre, and theatre depends on who’s in the audience. At MoneyHero, only one seat really counts, and it belongs to Richard Li.

The market has already priced the business at almost nothing

Start with the arithmetic, because it explains everything else. MoneyHero has about 44.08 million shares outstanding. At US$0.675, that puts its market value at roughly US$30 million. At the end of June, the company held US$28.2 million in cash.

Put differently, investors are valuing SingSaver, Seedly, Moneymax, 10.1 million registered members and a web of bank and insurer partnerships at roughly the price of a decent Singapore condominium. That is not a valuation. It is a verdict.

The second quarter explains why. Revenue fell 13 per cent year on year to US$15.8 million. Cash rewards paid to users jumped 77 per cent to US$5.1 million, which means MoneyHero is increasingly renting its demand rather than earning it. Monthly unique users fell 30 per cent, partly because the company began filtering out bot traffic in April without restating earlier periods. The first-half net loss widened to US$7.95 million.

Honig’s letter changes the tempo. It forces the board off the fence before his 5 October deadline. Silence will read as complacency, and a rushed CEO appointment will read as panic. Either way, the one asset an aggregator cannot afford to lose is now in play: the confidence of the banks and insurers who decide where their acquisition budgets go. Partners rarely rush to sign multi-year deals with a company that might have a new owner by Chinese New Year.

What does Honig actually want?

Look at how he built his position. In October 2025, his filings showed 1,117,401 Class A shares held individually and 496,604 held by a trust controlled by his wife. By April 2026, that had grown to 2,941,000 Class A shares, or 9.631 per cent of the class. He nearly doubled his stake while the stock fell.

That is not a man looking for the exit. It is a man betting there is a floor, and the floor is the cash. If the operating business is worth anything above zero, a sale at his proposed US$1.50 a share, roughly US$66 million for the company, would more than double his money. At today’s price, his entire stake is worth about US$2 million.

Also Read: Wealth management emerges bright spot in Southeast Asia financial services M&A

So his endgame could take three forms. The first is a sale at a premium, the outcome he is openly demanding. The second is more modest: a buyback, tender offer or capital return, anything that closes the gap between the share price and the bank balance. The third is a seat at the table, or at least a board that answers his calls.

There is one wrinkle. Honig has reported his stake on Schedule 13G, the short form for passive investors. In April, he certified that the shares were not held for the purpose of changing or influencing control of the issuer. A public demand for a sale is not what most people mean by passive. Watch whether he switches to a Schedule 13D, the disclosure for shareholders seeking influence. If he does, he is settling in for a longer fight.

The 81 per cent problem

Here is where the drama meets its limits. As of September 2025, Li’s sponsor entity beneficially owned 38.3 per cent of MoneyHero’s equity and 81.1 per cent of its voting power, since each Class B share carries 10 votes against one for a Class A share.

Honig’s 9 per cent of the Class A shares therefore carries a sliver of the vote. He can embarrass the board, but he cannot outvote it. His letter is effectively addressed to one man: the tycoon who controls Pacific Century, which has indirect majority ownership of the FWD group, and who chairs PCCW.

That makes the real question strategic rather than procedural. Does Li still want a sub-scale comparison platform fighting a cash-reward arms race in Singapore and Hong Kong? Or does it now make more sense in someone else’s hands? Honig also notes that no director or executive has bought shares on the open market. That silence speaks louder than any investor-day slide.

What if the board buckles?

Caving is not automatically good news for minority shareholders. There are three ways a pressured sale can go wrong.

The first is a take-under. A strategic review launched from weakness attracts bargain hunters who price off the cash, not the franchise. If no credible bidder emerges, the stock can fall further than where it started.

The second is a break-up. MoneyHero already sold its Malaysian CompareHero business to Jirnexu in 2024. Selling SingSaver or Seedly piecemeal to rivals would concentrate Southeast Asia’s comparison market into fewer hands. Consumers who rely on these sites for supposedly independent advice on credit cards and insurance would then have fewer places to check whether they are being sold the best product or merely the best-paying one.

The third is the quiet one. The buyer with the most certainty is the controller itself. Minority protections exist, but negotiating leverage is thin when the other side holds four-fifths of the votes.

Also Read: 48 PE investors, US$3.96B deployed, and not a single IPO exit in five years. Something is broken.

Then there are the people. MoneyHero cut 80 jobs in 2024. Another stretch of limbo is exactly when good engineers and partnership managers update their LinkedIn profiles.

Precedents: The Bridgetown family album

Southeast Asia has seen this film before, with an almost identical cast. PropertyGuru listed in 2022 via a merger with Peter Thiel and Li’s Bridgetown 2 SPAC, in a deal valuing the combined company at US$1.78 billion. Two years later, EQT agreed to pay US$6.70 per share, a 52 per cent premium to the last unaffected price, and TPG and KKR, holding a combined 56 per cent, signed voting agreements backing the deal.

The lesson cuts both ways. A premium is possible when controlling holders want out and the asset is a category leader. But PropertyGuru dominated property listings with real pricing power. MoneyHero is paying users to show up.

Singapore also has a small but persistent activist tradition. Swiss fund Quarz Capital has spent a decade writing open letters to local boards. At Sunningdale Tech, it accused the company of shareholder value destruction and pushed for a higher dividend payout, and the board replied that it preferred to focus on fundamentals. That is the standard Asian boardroom response, and it often buys time rather than results.

Globally, Toshiba is the cautionary tale. It spent years resisting activist funds before agreeing in 2023 to a buyout led by Japan Industrial Partners, then delisted after more than seven decades on the Tokyo exchange. Resistance did not change the destination. It only lengthened the journey while value leaked away.

The board’s real choice

Public markets do not grade on backers. Thiel and Li’s names got MoneyHero onto Nasdaq. They cannot keep it there on reputation alone.

The board should do what MoneyHero asks its own users to do: compare the options honestly. One option is a credible permanent CEO with a plan to stop buying traffic with cash. The other is a transparent review that gives minority shareholders a real voice. What it cannot do is keep everything interim.

For a company whose business is helping people make better financial decisions, the least it can do is make one of its own.

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SMEs adopt AI four times slower than big business, the fix starts with the PC

Talking about artificial intelligence (AI) in the context of Small, Medium Enterprises (SMEs) is no longer a discussion about the future, but about the present. According to IMDA’s latest data, SMEs’ AI adoption rate has more than tripled in just one year: 14.5 per cent of SMEs have adopted AI as compared to 62.5 per cent of non-SMEs. In an environment where efficiency, speed and adaptability define business survival, AI is consolidating itself as a strategic ally for companies that need to do more with less and make better-informed decisions in less time.

SMEs need to leverage the advantage AI brings more than ever. Just as critically, Singapore depends on them to. SMEs are the backbone of the local economy, accounting for over 99 per cent of all enterprises in Singapore. Employing about 70 per cent of the local workforce while also driving nearly half of the Republic’s Gross Domestic Product (GDP), Singapore’s AI ambitions cannot be realised without addressing this crucial group of businesses.

After all, SMEs operate in high-pressure environments: fewer resources, reduced teams, and the constant need to adapt to volatile markets. In this scenario, technology plays a key enabling role. Unlike large organisations, SMEs have a natural advantage: their agility to adopt new tools without lengthy purchasing processes or rigid structures. Today, that flexibility plays in its favor in the face of an AI that is no longer exclusive to large corporations and has become increasingly accessible.

One of the most relevant developments that SMEs should pay attention to then is the evolution of the PC as an intelligent productivity centre. AI no longer lives solely in the cloud; It can now run directly on the device. This allows you to automate repetitive tasks, optimise workflows, and analyse real-time information to make faster, more informed decisions. Local processing offers concrete benefits: increased speed, operational continuity, even offline, and better data control, a critical aspect for companies that handle sensitive information.

Also Read: Why AI could unbundle the beauty industry

But AI can only unleash its full potential if it has the right technological foundation. Hardware is no longer a secondary element and becomes the foundation on which daily productivity is built. Running AI models, automating processes, or analysing information in real time requires teams that are prepared for those types of workloads. Otherwise, the promise of efficiency is quickly diluted.

At this point, the renewal of the PC must be understood as a strategic decision, it is one lever that, if used effectively, will narrow the AI adoption gap. With AI-capable PCs, SMEs stand to gain from having key barriers like cost, data privacy, and the need for specialised IT staff, reduced. Processors designed for enterprise environments, with built-in AI capabilities, enable SMEs to tackle AI workloads without sacrificing performance or power efficiency, while incorporating security features designed to protect business information. These characteristics are especially relevant in organisations where each team fulfils multiple functions and there is no room for interruptions.

The underlying question is no longer whether it is advisable to invest in technology, but how to do it with a business vision. Buying a PC should not only respond to operational urgency, but to clear criteria: AI capabilities, security, performance and scalability. Each of these factors has a direct impact on the competitiveness and sustainability of the business.

AI is already helping SMEs to speed up processes and improve decision-making from the most everyday place: the PC work. Betting on AI-ready hardware not only generates immediate benefits but also prepares companies to grow with greater agility in an environment where adapting quickly makes a difference.

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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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The hidden economics of autonomous AI agents

For many startup founders, the first attempt to understand the cost of artificial intelligence (AI) begins in the wrong place: the model provider’s pricing page.

They calculate the price of input and output tokens, compare one model against another, and try to forecast usage as if AI were a simple utility meter.

That approach may work for a chatbot answering one question at a time. It breaks down quickly when companies move into autonomous agents — software systems that can plan, retrieve information, run commands, inspect errors, and try again without constant human prompting.

In that world, the model call is often not the expensive part. The real bill sits around it.

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

As Erik Perttu, Head of Engineering at Edu2Review, puts it in a recent Agoda report on AI adoption across Southeast Asia and India: “The generation is cheap; the trust-building around it is where the spend actually lives.”

That line captures a growing problem for engineering teams in the region. As AI coding assistants and agentic workflows become part of daily development, costs no longer come only from asking a model to write code. They come from everything needed to make that code usable, safe, and reliable.

The hidden cost of making AI useful

The report points to a case study involving an independent engineering pipeline working on a routine software task: renaming a variable across a dozen interconnected files.

On paper, this is exactly the kind of job AI should handle cheaply. The raw large language model calls needed to generate the code modifications cost about US$0.50. But once the full workflow was measured, the picture changed. Automated context retrieval, prompt construction, syntax parsing, multi-pass test validation, security scanning, and human review accounted for close to 90 per cent of the total financial and computational spend.

In other words, most of the cost was not in generating the answer. It was in proving that the answer could be trusted.

This matters because autonomous agents behave very differently from single-turn assistants. A coding assistant might suggest a function. An agent tasked with resolving a software issue may inspect local files, search dependencies, execute terminal commands, read test failures, re-prompt itself, and produce several corrective patches before stopping.

Each loop can be useful. Each loop also consumes resources.

Without guardrails, these systems can burn through budgets in surprisingly ordinary ways. Teams may dump entire code repositories, database schemas, or raw application logs into a high-context model when only a small slice is needed. Agents may be allowed to retry a failing unit test 15 or 20 times before a human steps in. Static system prompts, API definitions, and architecture notes may be sent repeatedly instead of being cached.

Also Read: Language was never the problem: Inside SEA’s real AI adoption gap

The result is not a dramatic AI failure. It is something more mundane: death by a thousand inefficient calls.

“The real risk isn’t AI being expensive. It is AI being used carelessly,” says M. Ridwan Agustiawan, Head of Engineering at Indonesian media-intelligence firm dataxet.

How dataxet made cost control an engineering habit

For dataxet, part of the Dataxet Group, the issue is not theoretical. The company uses agentic AI in production systems that support infrastructure and data-monitoring reporting. Its agents retrieve metrics from internal and client repositories, detect anomalies, translate raw data trends into executive narratives, and generate prioritised action recommendations for leadership.

That is a higher-stakes use case than a developer asking for help with boilerplate code. The output can influence operational decisions. It also requires the AI system to work across fragmented information sources, a familiar challenge for Southeast Asian companies dealing with multilingual markets, varied data maturity, and uneven legacy infrastructure.

dataxet noticed early that once developers became comfortable with AI tools, invoking an agent became the default response to many routine tasks. Individually, those requests looked harmless. Across an engineering organisation, they added up.

Rather than banning usage or imposing blanket restrictions, dataxet treated AI cost control as an engineering discipline.

The company narrowed the context fed into agents, using tightly scoped and pre-filtered data payloads instead of raw logs. It routed routine data aggregation to deterministic scripts — predictable software that does not need a reasoning model — and reserved high-reasoning large language model calls for anomaly interpretation and executive synthesis. It also tracked token consumption by feature, pipeline, and engineering workflow, making AI usage visible rather than abstract.

That visibility is crucial. Cloud computing went through a similar cycle. In the early days, teams spun up servers freely in the name of speed. The bills came later. The response was FinOps, a set of practices for managing cloud costs without killing innovation. Agustiawan sees a similar shift coming for AI: resource-conscious AI engineering.

Also Read: Why Singapore, Indonesia, and Vietnam are losing the AI race they think they are winning

For Southeast Asian startups, the lesson is particularly relevant. Many operate with lean engineering teams, limited runway, and investor pressure to show productivity gains from AI. The temptation is to either embrace agents everywhere or lock them down as soon as costs rise. Neither approach is sustainable.

Why quotas alone do not solve the problem

The Agoda report also highlights a split between how senior and junior technologists experience AI adoption.

Senior technology leaders (including CTOs, VPs, and architects) are more than twice as likely as junior developers to identify cost as the main barrier to agent adoption, at 32 per cent compared with 15 per cent. Junior developers, meanwhile, are nearly three times as likely to cite lack of skills, at 17 per cent compared with 6 per cent.

That gap helps explain why many organisations reach first for rationing. Across Southeast Asia and India, four in five developers now operate under usage limits, token quotas, or budget restrictions. Among large enterprises with more than 1,000 employees, more than 91 per cent enforce active usage caps.

Caps may be necessary, especially in companies where AI usage has spread faster than governance. But blind quotas can create a false sense of control. If a team is feeding bloated context into every prompt, using the wrong model for simple work, or allowing agents to retry indefinitely, a quota only slows the waste. It does not remove it.

Julius Domingo, Founder and CTO of Yappler, frames the issue more broadly: “The cost of AI not only involves the build, but also the data, process, and infrastructure preparation.”

That is the part many AI return-on-investment calculations still miss. A cheaper model is not always cheaper if it fails more often, requires more retries, or produces output that demands heavier review. A more expensive model may be economical if it completes complex tasks with fewer loops and lower downstream risk.

OpenAI’s Derrick Choi, Head of Codex Applied AI for APAC, makes a similar point in the report, noting that evaluation is shifting from simple token pricing to “how much useful work each dollar of intelligence can deliver.”

For engineering leaders, that means the metric cannot be tokens alone. Oravee Smithiphol, Tech Intelligence and Insights Manager at SCB 10X, the venture arm of Siam Commercial Bank, argues that organisations should assess cost per successful business outcome, weighing spend against code quality, delivery speed, and operational risk.

From AI spend to AI investment

The practical playbook is becoming clearer.

First, companies need to measure AI cost at the pipeline level. Token spend should sit alongside build status, test coverage, and deployment metrics, not in a separate finance spreadsheet reviewed only after the bill arrives.

Second, autonomous loops need termination gates. If an agent cannot fix a test after three attempts, for instance, it should escalate to a human developer rather than continue blindly.

Third, teams should use tiered model selection. Smaller or open-weight models can handle low-risk tasks such as documentation drafts, summarisation, or initial test scaffolding. Frontier models should be reserved for work that requires deeper reasoning, such as architecture decisions, security reviews, or complex debugging.

Also Read: Singapore firms embrace agentic AI, but audit trails remain thin

Finally, governance matters. The report finds that organisations with established AI guidelines show higher production adoption, at 43 per cent compared with 30 per cent, and stronger codebase readiness, at 56 per cent compared with 40 per cent.

The next phase of AI adoption in Southeast Asia will not be defined by who gives developers the biggest token allowance. It will be shaped by who builds the best systems around AI: cleaner context, smarter routing, visible consumption, and clear rules for when machines should stop and humans should step in.

The raw model call may be cheap. Trust is not. For startups hoping to turn AI from an experiment into an operating advantage, that is where the real work begins.

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The next information advantage is knowing what changed

AI has made it much easier for decision-makers to work with information. A company announcement can be summarised in seconds, a long annual report can be condensed, and a chatbot can help compare competitors or explain an unfamiliar industry. These are useful improvements, but they still solve only part of the information problem.

Most AI tools remain dependent on the user knowing what to ask. If you already know which company, event or issue deserves attention, AI can help you investigate it quickly. The harder problem is often earlier in the process: knowing that something important has changed at all.

This matters because many business developments do not become significant through a single announcement. A company may appoint a new executive, acquire land, increase capital expenditure and enter a new market over the course of several months. Each event may look routine when viewed separately. The pattern only becomes clear when those developments are connected over time.

Traditional search does not solve this particularly well. Search is excellent when the user already has a question in mind, while AI chat is increasingly good at helping users explore that question. But both are generally reactive. They become useful after the user has already decided what to investigate.

For investors, business owners and corporate decision-makers, there is growing value in systems that work one step earlier: continuously organising information and surfacing meaningful changes before the user thinks to search for them. Instead of starting with a blank search box, the system could tell the user that a company has made several related moves, that competitors are changing pricing or capacity, or that a regulatory development may affect a group of companies being monitored.

This is one of the ideas behind platforms such as Scope and Signals. The aim is not to compete with general-purpose AI chat by producing another chatbot. It is to build the information layer around it: collecting relevant corporate and market developments, structuring them over time and making changes easier to detect. Once an important development has been identified, AI can then become much more useful for interpreting the implications, comparing it with historical information and helping the user investigate further.

Also Read: Global expansion is no longer about reducing information costs, it’s about reducing trust costs

The distinction is important because better AI models alone do not guarantee better decisions. An AI model can only reason from the information and context available to it. If it is given one announcement, it can explain that announcement. If it can see a structured history of a company, its competitors, previous transactions and related industry developments, it has a much stronger basis for analysis.

This suggests that the next stage of business information platforms may not be defined simply by faster search or better summaries. Their value may increasingly come from maintaining context and detecting change. The useful system is not only the one that answers a question quickly, but the one that helps the user notice what deserves a question in the first place.

That becomes more important as the volume of information continues to grow. Decision-makers are unlikely to suffer from a lack of documents, news or data. The constraint is attention. Nobody can continuously monitor every filing, announcement, competitor and policy development that might eventually become relevant.

AI can make analysis much faster, but continuous monitoring and structured context solve a different problem. The combination of the two is potentially more useful: first identify what has changed, then use AI to understand why it matters.

In that sense, the next generation of business intelligence may be less about searching more efficiently and more about helping decision-makers know what changed before they think to search for it.

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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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