While Singapore’s corporate sector is moving faster on artificial intelligence (AI) than much of the world, a new PwC study suggests the country’s ambition to become an AI hub will depend less on enthusiasm and more on whether companies can fix the unglamorous foundations that turn AI spending into business results.

The headline numbers are strong. According to PwC’s Global AI Survey 2026, 67 per cent of Singapore businesses surveyed said they have a higher appetite for risk when investing in AI, compared with 41 per cent globally. Another 63 per cent said they allocate people and funding based on AI opportunities, above the global average of 51 per cent.

Also Read: AI hype, hard lessons: Where SEA’s startup capital is really going

That points to a market where artificial intelligence is no longer being treated as a side experiment. In many companies, it is increasingly influencing resource allocation, strategic planning, and competitive positioning.

The survey, conducted between July and September 2025, polled 1,217 senior executives worldwide. The Singapore sample was far smaller, comprising 30 respondents from publicly listed companies with annual revenue above US$100 million. Even so, the local figures reinforce the view that Singapore’s large enterprises are leaning into AI more aggressively than peers elsewhere.

Beyond pilots and buzzwords

One of the clearest signs of that shift is how Singapore companies are using AI to push beyond their traditional markets. The study found that 43 per cent of local respondents are deploying artificial intelligence to compete outside their sector, compared with 20 per cent globally.

That matters because it suggests AI is being used not just to optimise operations, but to reshape how companies think about growth. In practice, this could mean banks behaving more like software companies, manufacturers building data-driven services, or incumbents using AI to enter spaces that previously sat outside their core business.

Singapore also appears to be moving faster on the less visible infrastructure work that often determines whether AI projects scale or stall. Thirty per cent of respondents said their organisations have eliminated outdated IT infrastructure, versus 18 per cent globally.

Legacy systems rarely grab headlines, but they are one of the main reasons AI efforts remain trapped in pilot mode. Companies cannot scale advanced AI if their data is fragmented, their systems cannot integrate, or their infrastructure is too old to support modern workloads. On that front, Singapore seems ahead of the curve.

The study also suggests local businesses are pushing into more advanced AI deployments. Globally, 37 per cent of companies are still focused on relatively basic applications such as analysis, prediction and recommendation. In Singapore, that figure is just 20 per cent. Meanwhile, 17 per cent of Singapore respondents said they are using AI in autonomous or self-optimising ways, compared with 8 per cent globally.

That indicates a market that is beginning to move from AI as a support tool towards AI as an operating layer embedded in decision-making and workflows.

The gap with global AI leaders

Still, the bigger story lies in what happens when Singapore is compared not with the global average, but with the top 20 per cent of companies in PwC’s study, classified as “AI leaders”.

These firms are generating 7.2 times more AI-driven revenue and efficiency gains than peers on an industry-adjusted basis. They are also 2.5 times more likely to invest heavily, 2.4 times more likely to maintain reusable AI components across the organisation, and 1.7 times more likely to have high-quality data readily available for priority artificial intelligence applications.

In other words, the companies pulling ahead are not simply spending more. They are building stronger operating foundations, reusing what works, and aligning AI efforts with real business priorities.

Also Read: Kickstarting your AI journey: How to avoid the million-dollar mistakes most companies make

That is where Singapore’s weaknesses become clearer.

Only 53 per cent of Singapore businesses surveyed said they have robust, up-to-date security in place to protect data, AI models and infrastructure. Among AI leaders, that figure rises to 69 per cent. Just 47 per cent of Singapore respondents have a documented responsible AI framework, compared with 63 per cent of AI leaders, while only 43 per cent have a cross-functional AI governance board, versus 64 per cent among top performers.

The gaps extend to data. Just 37 per cent of Singapore firms said they maintain a single, trusted record of critical data, and 40 per cent said they use structured data effectively. Among AI leaders, those figures stand at 59 per cent and 60 per cent respectively.

Workflow redesign is another weak point. Only 37 per cent of Singapore companies said they had redesigned workflows to integrate AI rather than simply layering tools onto existing processes. Among AI leaders, 56 per cent had done so.

That shortfall is significant. AI rarely delivers meaningful gains by being bolted on top of old systems and ways of working. Its value tends to emerge when companies rethink processes from the ground up.

Policy support is growing, but companies still have to deliver

The findings arrive as Singapore steps up its national AI agenda. In 2025, IMDA and the AI Verify Foundation expanded efforts to build trust in generative AI through the Global AI Assurance Pilot. Budget 2026 added further momentum with a National AI Council, AI missions in advanced manufacturing, connectivity, finance and healthcare, as well as regulatory sandboxes and tax incentives.

That policy push strengthens Singapore’s position as a serious AI market. But it does not close the execution gap inside companies.

Anthony Dias, AI Hub Leader at PwC Singapore, said the best performers are distinguished not by the size of their spend, but by how deliberately they deploy artificial intelligence . “The companies achieving the highest AI-driven returns globally are distinguished not by how much they spend, but by how deliberately they operate: making targeted choices about where AI creates value, embedding it into core workflows, and scaling what works consistently across the enterprise.”

That is the central challenge for Singapore. The country has momentum, capital, regulatory support, and a business community that is more AI-forward than many global peers. But hub status will not be secured by ambition alone.

Also Read: AI is eating the world and startups are riding the infrastructure wave

For startups, enterprise vendors and investors in Southeast Asia, the message is straightforward: the biggest opportunities may lie less in AI hype and more in solving the enterprise basics — governance, trusted data, infrastructure modernisation and workflow redesign.

Singapore has shown it is serious about AI. The next test is whether its companies can turn that seriousness into repeatable business outcomes. Right now, the country looks like a strong contender for AI hub status — but not the finished product.

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The shift from SEO to AEO: Why AI visibility matters in 2026

The growing importance of Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) in the Singapore marketing landscape has undergone a massive transformation. From 2024 to 2026, the industry sentiment toward AI-driven search has turned from initial doubts into absolute faith. As users increasingly rely on AI assistants to find information, brands realize that appearing in traditional search results is no longer enough; they must be visible within the generative responses that now dominate the digital discovery process.

The limitations of legacy SEO tools in the AI era

While SEO agencies often stick with legacy tools due to habitual behavior and established workflows, this loyalty is beginning to hurt both agencies and their clients. Household names like Ahrefs or SEMRUSH were built for a world of blue links, and their recent attempts to pivot often result in an incapability to track true AI visibility accurately. When combined with their increasingly insane pricing schemes, these traditional platforms are becoming a bottleneck for Singaporean brands aiming to master the new frontier of AI SEO.

Analyzing the top 3 AI visibility tools for Singapore

As the market matures, three platforms have emerged as the primary contenders for brands looking to monitor their presence in AI search. In this comprehensive comparison, we now analyse 3 popular AI visibility tool being used for AI SEO in Singapore: 

BuildSOM:

 Workduo.ai:

Semrush:

Each of these tools claims to offer insights into how AI models perceive and recommend your brand, but their execution, data depth, and regional support vary significantly for businesses operating in Southeast Asia.

Also read: Why traditional SEO is dying in Singapore — and how AISEO pioneers are winning the next Blue Ocean

Our testing methodology: A multi-region, multi-lingual brand case study

To provide a realistic comparison, we established a rigorous test case reflecting a typical Singaporean enterprise expansion. The test involves a brand holding three distinct domains. The goal is to promote the brand to English and Mandarin speakers in Singapore, as well as English, Cantonese, and Mandarin speakers in Hong Kong. We monitored four specific AI models: ChatGPT, DeepSeek, Google AIO, and Google AI Mode. The scope included 10 unique prompts per language per region, totaling 20 prompts for Singapore and 30 for Hong Kong.

Cost efficiency and monthly investment comparison

When evaluating the financial commitment required for these tools, we looked at the monthly cost based on an annual subscription. The pricing structures vary wildly, ranging from straightforward prompt-based billing to complex, multi-layered domain charges.

• BuildSOM: At USD229 per month for the Standard Plan, this covers the total 50 prompts needed for our test case. It offers a transparent, all-in-one pricing model that fits the multi-lingual requirements of the Singapore and HK markets.
• Workduo.ai: The Pro Plan is required at USD299 per month. This is necessary to accommodate the 200 daily AI responses generated by our test (4 AI models multiplied by the 50 total regional prompts).
• Semrush: This is the most expensive and complex option. Users must first pay USD139 for a Pro SEO plan. On top of that, Semrush charges per domain. For three domains in Singapore (3 Base plans) and three in Hong Kong (6 Base plans, as each covers only 25 prompts), the total balloons to USD1,030 per month.

AI response accuracy: Testing regional and linguistic nuance

The value of an AI SEO tool lies in its ability to replicate the actual user experience. If a tool cannot simulate a local user in Singapore or Hong Kong, the data it provides is functionally useless for optimization.

• BuildSOM: This tool excels by running non-English prompts on the corresponding language settings. For example, it runs Mandarin prompts on a Simplified Chinese device environment and Cantonese on Traditional Chinese settings. The results collected are identical to what a human buyer would see.
• Workduo.ai: Fails to account for local device settings. It runs non-English prompts on an English-configured device environment, leading to misleading results that do not reflect the actual local AI output.
• Semrush: Similar to Workduo, it relies on English device configurations for non-English queries. This lack of linguistic localization renders the data useless for brands targeting the Mandarin or Cantonese-speaking demographics in Singapore and HK.

AI model coverage: Which engines are being monitored?

A strong visibility tool should cover various AI models. This includes Western models and emerging models like DeepSeek.

• BuildSOM: The Standard Plan includes ChatGPT, Gemini, Google AIO, Google AI Mode, DeepSeek, and Perplexity. This exceeds the requirements of the test case.
• Workduo.ai: The Starter Plan mentions ChatGPT and Google AIO. The Pro Plan does not explicitly name the five supported models. Users may not know which models are supported until after purchase.
• Semrush: This tool supports ChatGPT, Google AI, Gemini, and Perplexity. It lacks support for Google AI Mode and DeepSeek.

Historical data and period coverage

Tracking performance trends is crucial for long-term AI SEO strategies. The period of historical data each tool provides was compared.

• BuildSOM: Users can analyze AI responses at a daily level for up to 360 days.
• Workduo.ai: Analysis is limited to the last 30 days of data, which is insufficient for long-term SEO campaigns.
• Semrush: Detailed AI responses can be tracked for up to 60 days. This is better than Workduo, but less than BuildSOM.

Final verdict: Comparing the top 3 AI visibility tools

The best tool depends on the need for accuracy and the range of AI models you want to influence. Specialized tools provide deeper insights into generative search, especially for the multi-lingual Singapore market.

Choosing the best fit for your brand

Each AI Visibility tool has its own focus. Users should evaluate these platforms based on their needs. Factors include target regions, languages, number of domains, and the specific AI models.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

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Southeast Asia’s startup ecosystem continues to mature rapidly, with founders increasingly building companies that operate across multiple markets and time zones from day one. As businesses scale across borders, access to global talent has become a key advantage, allowing companies to build distributed teams that bring together specialised skills from different parts of the world. However, hiring internationally often introduces complex challenges, from navigating local employment laws and payroll systems to managing tax requirements and benefits across multiple jurisdictions.

To successfully build a global workforce, companies need solutions that simplify global hiring while ensuring compliance with local regulations. This need is particularly relevant for startups and scaling businesses that want to access international talent without creating operational complexity. Remote addresses these challenges by providing a platform that enables companies to find, hire, manage, and pay employees anywhere in the world while remaining compliant with local employment laws.

Meeting Remote at Echelon Singapore 2026 offers founders, HR leaders, and operators an opportunity to understand how global hiring can be managed more smoothly and compliantly. Whether building distributed teams across Southeast Asia or hiring specialised talent globally, understanding how to navigate international employment regulations and workforce management across borders can help organisations scale with greater flexibility and confidence.

Enabling global teams through compliant hiring

Remote is a global HR platform designed to support international hiring and workforce management. Its platform brings together key functions such as global payroll, international hiring, benefits administration, and compliance management into a single system that helps organisations manage distributed teams more efficiently.

The company’s mission is built around a simple principle: great talent exists everywhere, and companies should be able to work with the best people regardless of location. By removing barriers associated with international employment, Remote enables businesses to expand their talent pool and hire across borders with greater confidence.

This approach is particularly relevant for startups and scaling companies in Southeast Asia and the wider Asia Pacific region. As businesses in markets such as Singapore, Australia, Indonesia, Malaysia, and the Philippines continue to expand internationally, the ability to build distributed teams can play a key role in expanding into new markets, accessing specialised skills and supporting rapid growth.

Remote’s platform provides organisations with tools to manage employment contracts, payroll processing, tax requirements, and employee benefits in different jurisdictions. This integrated approach helps companies maintain compliance with local regulations while reducing the operational complexity associated with managing global teams.

Meet Remote at Echelon Singapore 2026

Remote joins Echelon Singapore 2026 as the Preferred Remote Hiring Partner, alongside founders, investors, corporates, and ecosystem leaders gathering at Suntec Singapore CEC on 3–4 June 2026. The event brings together Southeast Asia’s startup and technology community through content stages, exhibitions, networking opportunities, and knowledge sharing sessions designed to support regional innovation and growth.

Attendees can connect with the Remote team during the exhibition to learn more about building distributed teams and managing international hiring across the Asia Pacific region. For startups and SMEs navigating talent shortages or looking to expand internationally, conversations around compliant global hiring and workforce management are becoming increasingly important. Platforms like Remote play a role in enabling organisations to access talent beyond geographic boundaries while maintaining operational clarity and regulatory compliance.

As Southeast Asia’s technology ecosystem continues to evolve, access to global talent will likely remain a key factor in how companies scale and compete internationally. Events such as Echelon Singapore provide a space for founders, operators, and technology partners to exchange ideas and explore tools that can support the next phase of regional growth.

The region is evolving quickly, and Echelon 2026 offers the right place at the right moment to be part of what comes next. Register here to join the conversation.

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Local fractional marketing agency OtterHalf has launched Ottie’s Splash & Sell Marketing Workshop, a hands-on workshop where kids learn the basics of marketing through play. Designed for children aged 7–12, the workshop brings marketing concepts to life through play. Kids will learn how brands attract attention, design their own eye-catching posters, and perform a confident sales pitch in a fun, supportive environment.

Hands-on learning through real-world marketing and play

Held in OtterHalf’s studio at 195 Pearl’s Hill Terrace, the workshop introduces participants to basic marketing concepts using real-world examples such as KPOP Demon Hunters and Labubu. Participants also play OtterHalf’s original card game, Ottie’s Fishy Business, which subtly introduces common marketing tactics such as influencers and word of mouth.

Each workshop includes:

• Real-world discussions of trending brands, shows, and toys
• Group play time using OtterHalf’s marketing-themed card game, Ottie’s Fishy Business
• A poster competition, where participants pitch to each other and canvass for votes

Building confidence and communication skills in the next generation

OtterHalf founder Cassandra Ong believes marketing and communication are important skills for the next generation.

Parents have praised the format for encouraging communication and creativity, while educators see it as a refreshing alternative to traditional business education.

From card game success to real-world marketing lessons

Ottie’s Fishy Business achieved breakout success using only organic marketing and word-of-mouth, selling over 100 units within 3 months and reaching more than 300 households across Singapore.

The game introduces marketing concepts such as branding and storytelling, influencer marketing, competitive tactics, business ethics, and campaign planning.

Expanding the programme with future workshops and experiences

Looking ahead, OtterHalf plans to expand with additional workshops including a sales workshop and a supervised virtual mall experience.

All participants receive a certificate and earn the title “Mini Marketer”.

About OtterHalf

OtterHalf is an award-winning fractional marketing agency based in Singapore, helping businesses unlock senior-level marketing strategy and execution without the full-time cost.

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In the rapidly evolving digital landscape, the traditional boundaries of search engine optimization are expanding. For years, businesses focused almost exclusively on keyword rankings and backlink profiles. However, as generative AI becomes a primary interface for information gathering, a new discipline has emerged: Brand Mention Tracking within the context of artificial intelligence. This practice involves monitoring how often and in what context a brand is cited by Large Language Models (LLMs) and AI-driven search engines.

What is brand mention tracking?

Brand mention tracking is the process of monitoring online conversations, citations, and references to a specific company or product across the digital ecosystem. Traditionally, this meant tracking social media, news outlets, and blogs. Today, the scope has broadened to include “AI Visibility.” This refers to how prominently a brand appears when users query AI tools like ChatGPT, Google Gemini, or DeepSeek.

For modern enterprises, tracking these mentions is no longer just about reputation management; it is about data-driven SEO. It requires understanding the sentiment and frequency of brand citations within the datasets that train and inform AI. By analyzing these patterns, businesses can identify whether they are perceived as industry leaders or if they are being overlooked by the algorithms that currently guide consumer decisions.

Why brand mention tracking is essential for SEO and GEO in Singapore

For startups and established firms in Singapore, the shift toward Generative Engine Optimization (GEO) is particularly significant. Singapore serves as a hyper-competitive global hub where consumers are highly tech-savvy and quick to adopt AI assistants for local service recommendations, financial advice, and product research.

In this environment, Brand Mention Tracking serves as the backbone of a successful SEO and GEO strategy. If an AI tool does not “mention” your brand when a user asks for the “best fintech solution in Singapore,” you effectively do not exist in that user’s journey. High visibility in AI responses establishes authority and trust—two pillars of the EEAT (Experience, Expertise, Authoritativeness, and Trustworthiness) framework that search engines now prioritize. Furthermore, because Singapore is a multicultural gateway, tracking mentions across different linguistic and cultural contexts is vital for capturing the full breadth of the market.

Also read: What the top 10 time attendance systems in Singapore tell us about workforce management in 2025

Why BuildSOM is the leading SEO tool for GEO targeting Singapore

When evaluating the best SEO tool for GEO targeting Singapore, BuildSOM has gained significant traction among marketing experts for its specialized approach to AI visibility. Unlike legacy tools that often treat AI as a secondary feature, BuildSOM is built specifically to bridge the gap between traditional search and the new era of LLMs.

Key advantages of utilizing BuildSOM for your visibility strategy include:

• Native Non-English Monitoring: It is the only global tool offering native non-English AI visibility monitoring. This is crucial for Singapore’s diverse market, as it monitors responses in Malay, Chinese, and other languages through native environments rather than simple translation.
• Realistic Consumer Simulation: Instead of relying solely on LLM APIs, BuildSOM captures results through the actual browser UI, simulating a true consumer journey for more reliable data.
• Broadest LLM Coverage: It provides extensive coverage across various platforms, including DeepSeek and Doubao, which are essential for reaching the non-English speaking and regional communities.
• Dual-Market Insights: It is uniquely positioned to provide AI visibility data both inside and outside of China, a critical feature for Singaporean exporters and retailers.
• Cost-Efficiency: The brand-based fee scheme makes it significantly more affordable than legacy SEO tools, often costing only 10-30% of traditional alternatives.
• Practical Dashboard: The interface is designed for “prompt gap analysis,” allowing marketers to spot “missed AI opportunities” through a comprehensive birdview matrix.
• Accessibility: Startups can begin with a free account without any credit card commitment, allowing for immediate exploration of AI response monitoring.

Explore more useful tools for GEO:

1. BuzzSumo: Excellent for identifying trending topics and influential mentions across social platforms to inform content strategy.
2. Semrush: A comprehensive suite for traditional keyword research and competitive analysis that complements AI visibility data.
3. AnswerThePublic: A tool that visualizes search questions and suggested images, helping creators understand the specific queries fueling AI responses.

Also read: Costing comparison of top 7 popular ERP software for food manufacturing in Singapore

How to utilize an AI visibility report (AVR) to improve brand mention tracking

An AI Visibility Report (AVR) is a diagnostic tool for any SEO professional. To improve brand mention tracking, you must first analyze the “Prompt Gap.” This involves identifying the specific queries where competitors are mentioned by AI, but your brand is not.

By reviewing an AVR, you can determine the specific sentiment associated with your brand. If the AI recognizes your brand but provides outdated information, you can adjust your PR and content distribution to ensure fresher data is available for AI crawlers. Additionally, the AVR helps in “Cross-Cultural Surveillance,” ensuring that your brand’s authority remains consistent across different languages and regional LLMs. Using these reports allows you to move from reactive monitoring to proactive optimization, ensuring your brand is the “top of mind” choice for AI engines.

The competitive risk for Singapore startups

For Singaporean startup founders, the window to claim “AI Real Estate” is closing. As LLMs become more entrenched in the daily lives of consumers, those who fail to establish their AI visibility now risk being permanently excluded from the digital conversation. Waiting to build an AI Visibility Report is no longer an option; it is a delay that allows competitors to define the narrative of your industry.

The cost of inaction is high, yet the barrier to entry is low. You can set up a free account today with no credit card commitment. Exploring how an AI Visibility Report works will provide you with the raw data needed to protect your brand and dominate the future of search. Don’t let your brand become a ghost in the machine—start tracking your AI presence today.

Why we write this article

PRbyAI aims to share updated market news using our team’s tech knowledge, helping B2B customers make informed decisions.

About PRbyAI

PRbyAI is a tech-driven Martech startup leveraging cutting-edge AISEO to help customers generate leads and tap into new markets.

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During Southeast Asia’s fundraising boom, oversubscribed rounds were common and later-stage investors often wanted larger allocations than primary rounds could accommodate. Secondary share sales were sometimes the answer. Now, funding continues to stall, companies have reached meaningful scale, but exits remain limited. In this environment, secondary share sales can remain an important tool, but instead of providing early liquidity to investors before a full exit.

What is a secondary sale?

A secondary sale occurs when existing shareholders sell some or all of their shares before the company has a full exit. This differs from a primary issuance, where a company issues new shares and receives the investment monies.

A secondary sale is legally a transaction between selling shareholders and the buying investor. The company is not a direct party, but it is almost always involved because:

• Transfer restrictions in shareholder agreements are common.
• Board approval is often required.
• The company may need to address the liability gap (explained below).
• Governance rights may need to be updated after early investors sell down.

The liability gap

Secondary sales often occur alongside a new funding round, especially when the round is oversubscribed or existing shareholders want to avoid further dilution. This blended structure creates additional legal and commercial complexity. The liability gap is one of the most important issues in a secondary transaction.

By way of example, incoming investors commit US$30 million to a company:

• US$10 million goes into the company (primary issuance).
• US$20 million goes to early investors (via the secondary).

If the entire US$30 million had been a primary issuance, the company would typically be liable for warranties to investors up to the full amount.

Also Read: Do you need to rethink your startup fundraising strategy?

But in a mixed deal, the company only receives US$10 million, while selling shareholders receive the other US$20 million. Those sellers, especially VCs, are unlikely to take on full business warranties for the US$20 million of shares being sold. Institutional investors selling shares often only give title and capacity warranties, not full business warranties.

A liability gap, therefore, emerges between what the incoming investors expect and what sellers are willing to cover. This is usually resolved in the following ways:

• Incoming investors accept reduced warranty coverage.
• The company agrees to cover some exposure, even though it only received part of the funds.
• Investors rely on the commercial reality that large warranty claims are rare and accept the lower coverage.

Restrictions and governance implications

Companies undertaking a secondary transaction will have governance documents in place – shareholders’ agreements, constitution, etc. These typically include rights of first refusal (ROFR), tag‑along/co‑sale rights, and board or shareholder approval requirements. Almost certainly, a series of waivers will be required before a secondary sale can proceed alongside the approvals for the fundraise.

Also Read: Mastering the art of fundraising: Winning strategies to engage investors

If an early investor sells down significantly, the company may also need to revisit items such as board representation, veto rights, reporting rights and other investor rights. These rights may no longer be appropriate for a shareholder with a much smaller stake.

Different share classes and liquidation preferences

Cap tables often involve multiple share classes with different rights. When an incoming investor acquires shares through both primary and secondary transactions, they may end up with:

• A new class of preferred shares (from the primary issuance), and
• An older class, which may even be ordinary shares (purchased from existing shareholders).

If the investor wants identical rights across all of their shareholding, especially liquidation preferences, the company may need to consider reclassifying shares or buying back shares and reissuing the new class to align their rights.

In conclusion

Secondary sales are already a feature of Southeast Asia’s startup ecosystem, providing some liquidity when a full exit is still far off. But they also introduce complexity, with transfer restrictions, warranty and liability allocation, governance matters, and share‑class alignment all needing careful consideration.

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Every technology boom produces its own version of unauthorised adoption. Cloud had it. SaaS had it. Messaging apps had it. Now, AI agents are doing it at machine speed.

That is one of the most explosive threads running through the US-based API management company Gravitee’s “AI Agent Governance Gap” report. It argues that the real AI security problem is no longer hypothetical misuse but ungoverned deployment already underway within the enterprise.

The report says 75 per cent of organisations have discovered unsanctioned AI tools already running in their environments. Gravitee’s own survey data adds another damning metric: only 14.4 per cent of organisations have achieved full IT and security approval for their entire agent fleet. If shadow IT used to creep in through departmental software subscriptions, shadow AI is charging in through copilots, browser tools, API wrappers, open-source models and workflow automations that can be spun up in days.

Also Read: AI agents are already inside your systems, but who’s controlling them?

Southeast Asia is especially exposed because its digital businesses run on speed, improvisation, and distributed decision-making. That is not a criticism. It is part of why the region produces agile startups, resilient consumer platforms, and scrappy enterprise teams. But the same traits that drive innovation also make it easy for AI tools to bypass official channels. A product lead in Jakarta, a growth team in Manila, or a developer unit in Ho Chi Minh City does not need a six-month procurement cycle to start using AI. They need a company card, an API key, and a reason.

Friction is the mother of shadow adoption

One of the most useful insights in the report is brutally simple: shadow AI is a rational response to organisational friction. The white paper quotes journalist Jane Wakefield, who says, “Business leaders want to move quickly with AI. However, with different tools, different models, and different rules, it can be hard to have a clear picture of where data is going or how decisions are being made.”

That line lands because it describes a very familiar corporate pattern. Approved tools are slow to procure. Security review takes time. Legal wants data clauses. Compliance wants records. The business unit wants results this quarter. So the team finds a faster route.

This is not usually sabotage. It is an incentive design. Employees are judged on output, speed, and innovation. If the approved path to AI is painful, the unapproved path becomes attractive.

In Southeast Asia, that logic is amplified by competitive pressure. Startups are trying to conserve headcount while increasing output. Large enterprises are under pressure to automate customer support, sales operations, fraud detection, procurement and internal knowledge work.

Regional conglomerates are pushing digital transformation into subsidiaries with very different levels of technical maturity. In all of those environments, an AI tool that promises faster decisions or lower labour intensity can spread before governance catches up.

The real risk is not the chatbot. It is the connection

The public conversation around shadow AI often gets stuck on employees pasting sensitive text into consumer chatbots. That is a problem, but it is no longer the whole problem. The bigger enterprise risk emerges when unsanctioned AI tools are connected to internal systems.

An AI assistant with read access to a Slack workspace is one thing. An AI agent with delegated access to a CRM, document repository, billing dashboard, or cloud admin console is something else entirely. Once those connections exist, shadow AI stops being a data leakage issue and starts becoming an operational control issue.

Also Read: When tools start acting for you: The hidden cost of shadow IT

The report warns that these tools can arrive with embedded credentials or elevated system access that security teams do not even know exists. That observation should resonate across Southeast Asia, where many companies depend on external agencies, implementation partners and loosely documented integrations. In fast-moving businesses, access is often granted to “just get it working”. Later, nobody is entirely sure which tool is calling what.

That creates a dangerous asymmetry. Business teams see productivity gains immediately. Security teams see the underlying exposure only after an incident, an audit finding or a suspicious log pattern. By then, the tool may already be part of a critical workflow.

The region’s startup culture makes this even harder to police

For a pan-Asia tech audience, the uncomfortable truth is that startup culture itself can nurture shadow AI. Founders prize initiative. Engineers are rewarded for solving problems without bureaucracy. Growth teams experiment first and document later. That is often a strength. It is also how invisible dependencies get created.

Imagine a sales team using an AI agent to summarise leads, enrich account data and draft outreach. Then it gets connected to HubSpot or Salesforce. Then it gains access to internal pricing sheets. The customer success team then follows the same workflow. Six months later, the company has an undeclared AI layer sitting between staff and core customer systems.

Nothing about that progression sounds dramatic while it is happening. That is precisely why it is dangerous.

The problem is even more acute in Southeast Asia because many companies are managing multilingual operations, fragmented vendor stacks, and regional expansion simultaneously. A single shadow AI deployment can touch data subject to Singapore’s PDPA, Indonesia’s personal data law, Vietnam’s privacy rules or sector-specific controls in financial services. The compliance exposure is no longer local. It is distributed.

Security teams are losing the race to discover what exists

Gravitee’s broader research found that 88 per cent of organisations confirmed or suspected security incidents this year were related to agent security. Read alongside the 75 per cent shadow AI figure, the message is blunt: enterprises are not merely struggling to secure authorised AI. They are struggling to discover unauthorised AI before it matters.

This is why “approval gap” may become one of the most important phrases in enterprise AI. Many governance discussions focus on policy design. But before policies can be enforced, organisations have to know which agents, tools and workflows are already active. That sounds basic. It is not.

Also Read: AI systems as policy executors without policy clarity

Discovery is hard because AI adoption is now decentralised. Teams can access public models directly, use embedded AI features in SaaS products, deploy open-source models on cloud infrastructure or build wrappers around multiple providers. Some tools look like standalone apps. Others are merely features hiding inside software the company already uses. The sprawl is astonishingly easy to underestimate.

The cost of being slow is now higher than the cost of being wrong

There is a strategic twist here that many leaders have not internalised. In the past, central technology teams could often slow adoption in the name of control. In AI, that strategy backfires. If the secure path is significantly slower than the insecure path, business units will route around it.

That means the winning governance model is not simply stricter. It has to be faster, clearer and easier to use than shadow alternatives. This is particularly relevant in Southeast Asia, where businesses operate in highly competitive markets with thin margins and relentless pressure to move. Governance that adds friction without adding usable infrastructure will be ignored.

The lesson from the report is not that organisations should crack down theatrically on every unauthorised tool. They need to make compliant AI access genuinely convenient. If official channels are slow, shadow AI will keep winning.

The next era of enterprise AI security will not be defined by who writes the toughest policy. It will be defined by who builds the fastest trustworthy route from business need to approved deployment. In a region that values execution, that may be the only governance model with any chance of survival.

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Most people don’t have a networking problem. They have an environmental problem.

Networking remains one of the most talked-about skills in business, yet the way it is commonly approached has barely evolved. The prevailing advice still centres around doing more — attending more events, meeting more people, expanding reach.

But after hosting a recent event focused on networking, one thing became clear:

The issue isn’t that people don’t know how to network. It’s that they are doing it in the wrong environments.

The persistence of a broken model

Traditional networking is built on volume.

The assumption is simple: the more people you meet, the more opportunities you create. This often results in rooms filled with introductions, surface-level conversations, and an underlying pressure to make every interaction “worth it”.

In practice, this creates the opposite effect.

Conversations become transactional. Follow-ups are inconsistent. Most connections never move beyond the first meeting.

As Kelly Kam, Co-Founder of Speakers Society and Co-Creator of the KellyK Authentic Networking OS, puts it: “Most people still think networking is about collecting contacts… Trust is the real currency.”

The emphasis on volume over continuity is where most networking efforts break down.

Also Read: Networking is expanding, but execution still lags

What practitioners are actually seeing

Across founders, creators, and operators, a different pattern is emerging.

Gayathri Ramaswami, Founder and CEO of All Hands Together Inclusive School, highlights the role of reciprocity: “It is a two-way street… offer help and share resources, and watch your network become your most powerful support system.”

Cindi Wirawan, Founder of Vibe Tribe and LinkedIn Top Voice, points to timing: “They think networking is something you do when you need something… by then, you’re already late.”

Bosco Lim, Founder of Hearted Moments Studio, frames it in terms of value: “If you focus on giving first… people naturally want to reciprocate.”

Belle Kwok, Founder of Lexine Enterprise, brings clarity to the selection process: “Real networking is about choice – who you spend time with, who you align with, and who you actually want to build with.”

Taken together, these perspectives suggest a shift away from volume and towards something more deliberate.

Not more conversations. Better ones.

From presence to alignment

The most striking observation from the event was not how many people connected, but how easily conversations progressed.

Participants were not “working the room”. They were continuing discussions, scheduling follow-ups, and exploring collaborations – often within the same interaction.

The difference was not technique.

It was alignment.

Everyone in the room shared a common objective: to grow and monetise their voice as speakers, coaches, trainers, and creators.

This shared intent removed the friction typically associated with networking. Conversations had context. Outcomes had direction. Follow-ups had a purpose.

In other words, networking became a byproduct – not the goal.

Why alignment outperforms volume

When people operate within aligned environments, several things change:

• Filtering happens upfront: The room itself reduces noise, eliminating the need to “figure out” who is relevant.
• Conversations gain depth faster: Shared context allows discussions to move beyond introductions almost immediately.
• Follow-through becomes natural: When there is mutual relevance, staying in touch no longer feels forced.
• Opportunities emerge organically: Collaborations are discovered, not chased.

This is a fundamentally different model from traditional networking – one that prioritises the quality of interaction over the quantity of contacts.

Also Read: Why networking, not online applications, now determines career success

The role of systems in modern networking

Even when alignment exists, most people struggle with consistency – remembering conversations, maintaining follow-ups, and staying relevant across multiple relationships.

This is where systems, including AI, are beginning to play a role.

Rather than replacing human interaction, they reduce the operational friction around it – supporting continuity, context, and consistency across conversations.

As AI and automation become more embedded in how we work, the advantage will not go to those who can meet the most people, but to those who can build and sustain the most relevant relationships over time.

In practice, this shifts networking from a series of isolated interactions into an ongoing relationship system.

Beyond networking: Building environments that convert

It is worth noting that the event itself was not designed as a networking platform.

It was built to bring together individuals focused on monetising their voice – people actively working towards visibility, positioning, and opportunity creation.

The networking emerged as a natural consequence.

This distinction matters.

Because it suggests that the future of networking may not lie in better tactics, but in better environments – spaces where alignment is built into the room, not left to chance.

The shift ahead

Networking is not disappearing. But it is evolving.

The emphasis is moving away from:

• How many people do you meet towards
• How relevant those people are

And from:

• Starting conversations towards
• Sustaining them

For founders, creators, and operators, the implication is clear: The most valuable networks are no longer built by indiscriminately expanding reach, but by positioning yourself within the right ecosystems.

Because when the room is right, you don’t need better networking tactics.

You need better alignment.

And when that happens, networking stops feeling like effort.

It becomes momentum.

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When my Co-Founder, Samuel Adcock, and I started The Ortus Club in Singapore in 2015, we were in our mid-twenties, pitching a fairly unsexy idea to enterprise clients who had never heard of us: that a dinner for 15 people would generate more pipeline than a conference booth in front of 5,000.

Nobody was particularly interested in hearing that from a small agency in Southeast Asia. The established event houses were in London and New York. The enterprise brands we wanted to work with — the Googles, the Visas, the Metas — were already being courted by firms with decades of track record and offices in every major market. We had a WeWork desk and a thesis.

A decade later, The Ortus Club has produced more than 2,500 invitation-only executive events across 40+ countries. Our client list includes Google, Visa, Meta, Adobe, IBM, Zendesk, and Airwallex. We operate across APAC, EMEA, and North America. And we have never run a single open-registration conference.

This is not a success story disguised as a LinkedIn post. This is what we actually learned — including the parts that were painful — about building a global B2B services company from Southeast Asia.

Specialisation was the only thing that made us credible early on

The most important decision we made was also the most uncomfortable one commercially: we said no to everything except invitation-only executive roundtables. No conferences. No exhibition stands. No sponsored speaking slots. One format, done obsessively well.

For the first couple of years, this meant turning away revenue. Prospective clients would ask us to run a panel at their conference or manage a large-format event, and we would say no. That felt reckless when we were trying to build a business. But it gave us something that turned out to be far more valuable than early revenue: a clear identity in a crowded market.

When a VP of Marketing at a Fortune 500 company asks, “Who does invitation-only executive roundtables?” — we wanted the answer to be us, immediately, without qualification. That only works if you are not also doing twelve other things.

We eventually documented our entire methodology in a free guide called The Art of Networking. It remains the most practical thing we have published, and it is the backbone of our company trainings.

Also Read: Strategic investment 101: A founder’s playbook for winning without losing control

Southeast Asia was not a disadvantage — it was a training ground

There is a specific advantage to building a global B2B business from this region that does not get discussed enough in founder narratives. Southeast Asia is the most culturally diverse business environment on the planet. Running events across Singapore, Manila, Jakarta, Kuala Lumpur, Bangkok, and Sydney in the same quarter forces a localisation discipline that companies built in more homogenous markets simply do not develop.

Our team is largely based in Manila and Singapore. The operational muscle we built, adapting tone, format, guest curation approach, and follow-up cadence across six or seven dramatically different cultures in APAC, gave us something we did not fully appreciate until we expanded into Europe and North America: we were already better at localisation than most of our competitors because we had been doing it since day one.

The cultural nuances are not trivial. The formality expected in a Tokyo executive dinner is fundamentally different from what works in Sydney. The way you position an invitation to a CTO in Singapore is not the way you position the same invitation in Jakarta. Getting this wrong does not just reduce attendance — it damages the client’s brand with exactly the people they are trying to reach. We learned this the hard way more than once in our early APAC expansion, and those lessons became the foundation for everything we built afterwards.

Scaling into EMEA and the US meant rebuilding, not copy-pasting

The move into London was our first real test of whether the model could travel outside APAC. The answer was yes — but not without significant adaptation.

London’s senior executive community operates differently from Singapore’s. There is more scepticism toward event invitations generally, longer relationship-building timelines, and a much higher premium on credibility signals in the invitation itself. Who else is attending, who is hosting, what is the venue — these details carry more weight in EMEA than in APAC, where the topic and format tend to do more of the heavy lifting.

Also Read: How founders should build for a Meta-national suture

The US market presented a different challenge entirely. American executives reward directness and clear commercial framing in ways that would feel abrupt in most of Asia. The post-event follow-up expectations are faster and more transactional. And the sheer volume of competing events in markets like New York, San Francisco, and Chicago means your invitation is fighting for calendar space against a much larger field.

The constant across every market — the one thing that has not changed in a decade — is the core thesis: get the right people in a room, design a conversation that creates genuine value for every person present, and the commercial outcomes follow. That has been as true in Zurich and San Francisco as it has been in Singapore and Sydney.

Delegate acquisition is the real challenge

If I had to identify the single most underrated capability in B2B event marketing, it is delegate acquisition — the process of actually getting senior executives to say yes to your invitation and show up.

Anyone can book a nice venue. Anyone can write a compelling agenda. The part that separates event companies that deliver genuine commercial value from those that do not is whether the right people are actually in the room. A beautifully produced roundtable with the wrong 15 people is worthless. An average venue with the right 15 people is transformational.

Our entire operational model is built around this. We do not sell sponsorship packages and hope people register. We identify the specific executives our client needs in the room, and then we do the work — the research, the outreach, the personalisation, the follow-up — to get them there. That process is manual, labour-intensive, and does not scale elegantly. It is also the reason our clients keep coming back.

The 2026 Event Marketer’s Playbook — our annual research publication based on data from 295 senior B2B marketers across 29 roundtables in 30 cities — confirmed what we have seen operationally for years: the cost-per-qualified-conversation for curated invitation-only events is significantly lower than for open-attendance formats when you weight for pipeline quality. The per-event production cost is higher, but the outcome is not comparable.

Also Read: The alliance economy: How founders and investors should position in a fragmented world

What I would tell a founder building a services business in Southeast Asia

First, specialise earlier and more aggressively than feels comfortable. The temptation to be a generalist is strongest when revenue is scarce, which is exactly when saying no matters most.

Second, treat the cultural complexity of this region as a competitive advantage, not a logistical headache. If you can operate effectively across APAC, you are better prepared for global expansion than you realise.

Third, document your methodology and publish it for free. The Art of Networking has been our single most effective resource — not because it generates leads directly, but because it establishes the core mission of what the company stands for. And that’s contagious.

And fourth, invest disproportionately in the part of your service that is hardest to replicate. For us, that is the guesting process. What goes on behind the scenes? For your business, it will be something else. But the principle is the same: the thing your competitors find most difficult to copy is the thing your clients will value most.

We are a decade in now. The thesis has not changed. The rooms have just gotten bigger — and more global.

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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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Artificial Intelligence (AI) is no longer a futuristic concept; it’s a present-day imperative, dominating boardroom discussions and reshaping industries. Yet, for all the excitement, many organisations stumble on their AI journey. Having advised leaders from global conglomerates to agile, owner-driven firms, I’ve witnessed firsthand the common pitfalls and the pathways to genuine success. My goal is to share these global experiences through this article, helping your organisation navigate the complexities and truly benefit from AI.

Beyond the hype: What companies get wrong (and right) with AI

So, when companies declare, “We’re doing AI,” where do they most often go wrong?

The AI missteps: Where ambition meets reality

• The “instant gratification” trap

Many executives fall into the allure of quick wins, treating AI like an “instant button” for immediate results. This often leads to hasty, and expensive, investment choices without a solid foundation. Imagine attempting to build a skyscraper without a proper blueprint – it’s a recipe for disaster. I recall one executive who privately confessed to exhausting their entire AI budget on expensive hardware before even defining the problem they were trying to solve. That’s like buying a Ferrari before you have a driver’s license, let alone a road to drive it on!

• The missing “why”: Unclear problem formulation

Excitement over the latest AI tools, like Generative AI, is understandable. However, a common misstep is failing to clearly define the actual business problem AI is meant to solve. It’s akin to having a shiny new hammer but no nail in sight! Without a clear “why,” even the most advanced AI becomes a solution in search of a problem.

• The scattered approach: Lacking a cohesive roadmap

I’ve observed organisations launching a flurry of independent AI initiatives without a cohesive strategy. This often results in teams competing for resources, and even if projects are approved, the overall organisational improvement can be negligible. It’s like a rowing team where everyone paddles in a different direction – lots of effort, but little forward momentum. While initial exploration through understanding the concepts and trying to imagine the context in the team, attempting to solve lab-scale problems is valuable, a well-defined organisational roadmap is crucial to be drawn in a reasonable time from the start of exploration. Otherwise, you’re just building a collection of really cool individual rooms, but no functional house.

• The data dilemma: Overlooking data integrity

AI thrives on data. Yet, the importance of accurate, clean, and accessible data is frequently overlooked. This, in my experience, is the single most critical bottleneck. If your data isn’t robust, your AI efforts will struggle. It’s the classic “garbage in, garbage out” scenario, but with much more expensive garbage!

• The human factor: Fear and resistance

People inherently resist any change. If it is coupled with the fear of job displacement, the resistance becomes even stronger. This situation can potentially slow down any AI initiative at the execution level, and it’s imperative to properly address this genuine concern. My message is simple: AI is inevitable. You can’t put the genie back in the bottle. Embracing AI and learning to work with it is about acquiring a new superpower, not facing a new threat.

In essence, “getting it wrong” often stems from treating AI as a magic bullet or a purely technical endeavour, rather than a strategic business transformation. It’s not just about the tech; it’s about the entire orchestra playing in harmony.

Also Read: Balancing ambition and well-being: A founder’s take on sustainable company building

The ingredients for AI success: A recipe for impact

To distil it down, successful AI initiatives typically require:

• AI literacy at the top: Board and executive levels need a clear understanding of AI’s potential and limitations.
• Contextual understanding: AI capabilities must be understood within the unique context of your specific organisation.
• Foundational investment: Allocate sufficient time for building robust foundational capabilities.
• Business value focus: Clearly define the business problem and the expected value outcomes.
• Company-wide strategy: A cohesive, well-defined roadmap ensures alignment and efficiency.
• Addressing human emotions: Empathy and clear communication are vital to mitigate fear and uncertainty.
• Data sanity: Clean, reliable data is the lifeblood of effective AI.
• Top-down commitment: AI is a strategic imperative requiring unwavering support from leadership.
• Tolerance for failure: Expect initial setbacks; they are opportunities for learning and adaptation.

From vision to reality: Making AI deliver

Moving from an AI vision to tangible business impact requires significant organisational transformation and, sometimes, tough decisions. A true cultural shift demands strong stakeholder buy-in and, frankly, top-down enforcement. Making the organisation “AI aware” and up-skilling key executives are paramount.

Here are the critical decisions that determine whether AI creates real business impact or remains theoretical:

• The executive sponsor: An executive sponsor with a complete understanding of the goal, approach, and unwavering commitment is absolutely key. He/she is the champion, the cheerleader, and the bulldozer, moving initiatives from the drawing board to tangible benefits.
• Strategic sourcing: I’ve also seen organisations stumble because they made the wrong decision between in-house skill development versus outsourcing, or they ended up with the wrong implementation partner or product. These are critical choices that can make or break a project.
• Avoiding the “lab-trap”: It’s easy for in-house teams to prove a concept in a lab environment and become complacent. However, scaling to production demands an entirely different approach, requiring robust engineering and operational expertise. A proof-of-concept is like baking a single cupcake; scaling to production is like running a bakery that churns out thousands daily.
• Robust data infrastructure: Once again, robust data infrastructure and governance are non-negotiable. AI initiatives frequently stall because their data isn’t sanitised or is simply insufficient. It’s like trying to bake a cake while basic ingredients are missing – you’re just going to end up with a mess.

Leadership, ownership, and decision-making: The pillars of success

For AI initiatives to truly deliver results, several internal conditions must be met:

• Visionary executive sponsorship: A strong executive sponsor must articulate a compelling vision, positioning AI as a transformative and strategic imperative. A dedicated AI or data leader, accountable for adoption and monetary impact, is also crucial. True AI adoption rarely happens without an executive actively “pushing” (emphasis is on “pushing”) from the top, not just passively monitoring.
• Cross-functional ownership: AI implementation is inherently cross-functional. Ownership must be distributed across diverse teams – data scientists, engineers, business analysts, domain experts, legal, and compliance. Each member needs a clear understanding of their role and how their contribution fits into the larger picture. It’s a team sport, and everyone needs to know their position and strategy.
• Data-driven culture and iteration: The organisational culture should foster data-driven decision-making, embracing rapid prototyping, testing, and iteration. This means moving away from lengthy development cycles and adopting shorter feedback loops. In the world of AI, it’s “fail fast, get up, gather yourself, use the learning and try differently”.

Also Read: AI agents could become the new OTAs — What it means for Agoda and the future of travel

Measuring what matters: Quantifying AI’s impact

When it comes to measurable results, leaders must focus on tailored metrics. I recently spoke with a CEO whose manpower costs were only five per cent of his operational costs, having recently rationalised his workforce by 30 per cent. In his context, simply discussing human productivity enhancement, while valuable, wouldn’t be the most impactful objective for his business.

So, what to measure? It depends entirely on the business problem you’re solving. It could be:

• Revenue growth: From new AI-powered products or services.
• Cost reduction: Through process automation or optimisation.
• Improved customer satisfaction: Due to personalised experiences or faster service.
• Reduced risk: Through AI-driven fraud detection or predictive maintenance.
• Faster time-to-market: For new innovations.
• Real-world examples: I’ve led teams implementing AI combined with physics (Digital Twin) that saw a 15 per cent yield increase in an oil rig. In another instance, quality and customer satisfaction improved, and production output increased by over 25 per cent in a process manufacturing plant.

The key is to link AI initiatives directly to strategic business objectives, define quantifiable metrics before you start, and compare them post-implementation.

Beyond efficiency: Focusing on human outcomes

Perhaps the most important question is how to ensure AI adoption genuinely improves human outcomes – for teams, customers, and society. Any technology developed by humans should ultimately enhance human comfort and well-being. Therefore, embedding ethical AI principles from the very beginning is imperative.

This includes considerations like:

• Fairness and equitable outcomes
• Transparency and explainability
• Sustainability
• Community well-being
• Inclusion (to moderate the digital divide)

The focus should always be on employee empowerment and augmentation, rather than automation that simply replaces jobs. How can AI make our employees better, more effective, and happier? How can it serve our customers more thoughtfully? How can it contribute positively to society? These are the questions we must continually ask.

The smartest first step: Don’t boil the ocean

For senior leaders feeling both excited and overwhelmed by AI, my recommendation is clear: Do not try to create a five-year AI master plan to start with. That would become obsolete quickly, given the pace of evolution of this technology

Instead, identify and champion one or two high-impact, low-complexity AI initiatives that solve a critical business problem and can deliver measurable results within 1 to 3 months. Think of it as a pilot project, a quick win to build momentum and confidence.

Also Read: AI at work: Moving forward with employee engagement

The steps are straightforward:

• Select a concrete, high-value business problem: What’s a genuine pain point AI could alleviate where success would be clearly visible?
• Ensure clean data for that problem: Focus on the specific data needed, not trying to clean all your data at once.
• Define clear, measurable business outcomes: What does success look like, specifically, for this pilot?
• Assemble a small, dedicated, cross-functional team: Empower them by freeing them from routine work and providing necessary training.
• Commit to success: Provide resources and remove roadblocks.
• Achieve that first tangible success: Celebrate it! Make a big deal out of it.
• Replicate and scale: Then, and only then, replicate what you’ve learned to other areas.

This iterative approach builds confidence, demonstrates value, and allows organisations to learn and adapt without getting bogged down in overly ambitious plans from day one. It’s about taking smart, actionable steps, not giant leaps into the unknown.

Ultimately, the companies that succeed with AI will not be the ones that move fastest, but the ones that build the right foundations and make it work in practice.

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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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