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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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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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.

The post How we scaled a B2B events business across 40+ countries appeared first on e27.

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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