In this interview, e27 speaks with Kai Yong Kang, Partner at GenAI Fund, Southeast Asia’s first AI-focused fund dedicated to helping large organisations adopt AI responsibly and at scale. Founded by former senior executives from Amazon Web Services, the fund brings a rare inside-out perspective on enterprise transformation, shaped by years of working with governments, multinationals, and high-growth technology companies across the region.

Rather than treating AI as a standalone technology bet, GenAI Fund operates at the intersection of enterprise decision-making, execution, and long-term value creation.

This conversation forms part of e27’s broader AI Pulse coverage, which examines how organisations across the region are building, deploying, and governing AI in real-world settings.

Advancing responsible enterprise AI adoption

e27: Briefly describe what your organisation does, and where AI plays a meaningful role in your work or offering.

Kai Yong: GenAI Fund is Southeast Asia’s first AI-focused fund dedicated to helping large organizations adopt AI responsibly and at scale. The fund was founded by former senior executives from Amazon Web Services, who spent many years building and scaling the AWS startup and enterprise ecosystem across Southeast Asia and Pakistan, growing the regional business by more than 10×. In their previous roles, they worked closely with governments, multinational corporations, and high-growth technology companies, supporting the adoption of new technologies including AI that directly shaped how people work, live, and make decisions.

At GenAI Fund, we operate at the intersection of enterprise decision-making, emerging technology, and long-term value creation. Every day, we see how AI moves from an abstract idea into real systems that power banks, hospitals, manufacturers, and national infrastructure and just as importantly, where AI should not be applied.

Our work spans three closely connected areas. The first is digital transformation. We help large enterprises and governments understand where AI truly creates value and where it does not. Often described as the McKinsey for AI, our role is to guide organizations from initial awareness, through pilot programs, and into scaled, production-level deployment. To date, we have supported more than 100 large enterprises across Asia, including global companies such as Coca-Cola and KFC, as well as regional institutions like UOB and Prudential. Our work involves shaping AI strategy, identifying the right use cases, and connecting organizations with the right partners from a curated network of more than 2,600 AI startups across the region. Alongside this, we have trained over 20,000 government officials and enterprise executives on how to think about AI clearly, responsibly, and pragmatically.

The second area is investment. We invest in AI startups that are ready to work with real enterprises and real constraints, rather than theoretical use cases. Through our flagship FastTrack AI Accelerator program, run in collaboration with NVIDIA, selected startups receive direct investment from GenAI Fund, are matched with guaranteed enterprise engagement opportunities, gain access to up to US$1,000,000 in compute resources, and are supported through live enterprise pilot projects. This approach ensures that innovation is tested in real business environments, where outcomes matter and assumptions are challenged.

The third area is ecosystem development. Because we see the same patterns repeated across industries and countries, we believe it is important to share what actually works. Since 2023, we have hosted 30 AI events and programs across Asia, including Japan, together with partners such as AWS, Google Cloud, NVIDIA, Databricks, and FPT. These initiatives have reached more than 4,000 participants, including C-level leaders, senior executives, and technology founders. This experience culminates in our upcoming regional AI adoption conference, which will bring together 5,000 participants, showcase more than 100 real enterprise AI case studies, and facilitate 500 curated sessions between enterprises and startups, with a clear goal of launching 100 real AI pilot projects.

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Accelerating enterprise AI sourcing through matchmaking

e27: What is one concrete way AI is currently creating value within your organisation or for your users or customers?

Kai Yong: One concrete way our AI platform creates value for enterprises is by significantly reducing the time required to source, evaluate, and engage qualified AI solution providers for real operational use cases. Traditionally, enterprise AI sourcing is a slow and fragmented process, often taking weeks or months of manual research, referrals, and vendor screening before meaningful discussions begin. Our AI matchmaking platform compresses this cycle into minutes by translating enterprise use cases into structured requirements and automatically shortlisting and ranking AI startups based on technical fit, industry relevance, and deployment readiness.

This was demonstrated at Tasco Innovation Day, where Tasco JSC opened more than 30 live use cases across mobility, automotive, insurance, and infrastructure. Using our platform, Tasco was able to review over 300 global AI startup proposals and move directly into 71 closed-door business meetings with decision-makers within six weeks—something that would typically take several months through traditional sourcing channels.

Beyond a single event, this capability is scaled through our GenAI Open Innovation initiatives, where the platform supports over 100 enterprises and a curated database of 2,600+ AI startups across the region. To date, more than 500 AI startup–enterprise matches have been facilitated, with over 100 progressing into active or launched Proofs of Concept (PoCs), including one FastTrack startup that recently secured a multi-million-dollar enterprise deployment following this AI-enabled sourcing process.

Evolving from investment fund to transformation platform

e27: What was a key decision or trade-off you had to make when adopting, building, or scaling AI?

Kai Yong: A key decision we made was to evolve from a traditional investment-led model into an end-to-end enterprise AI transformation platform. Early on, we realized that capital alone does not drive real-world AI adoption—especially in Southeast Asia, where enterprises face fragmented data, limited internal AI readiness, and complex procurement processes. To generate meaningful outcomes for both startups and enterprises, we chose to move beyond being passive investors and become an active execution partner across the entire adoption journey—from leadership alignment and use-case definition to pilot delivery and production scaling. This meant investing in our AI matchmaking platform and transformation frameworks, and running “Working Backwards” workshops to help enterprise leaders align on high-impact use cases before any technical work begins.

Working this closely with enterprises requires dedicated time and new capabilities across strategy, technology, and change management, which also led us to build a strong regional network of domain experts, technical advisors, and operators who now support deployments alongside our team. That investment has paid off. We have supported over 100 enterprise AI initiatives into Proof of Concept, created structured pathways for startups to engage real buyers, and helped multiple projects progress toward production deployment and commercial contracts—turning AI from isolated experiments into revenue-generating collaborations.

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Momentum in enterprise collaboration and scaling challenges

e27: Looking back, what has worked better than expected, and what proved more challenging than anticipated?

Kai Yong: First, looking back at 2025, what exceeded our expectations most was the level of openness from large enterprises to collaborate deeply with AI startups. We initially anticipated a slow, conservative adoption curve. Instead, over 100 enterprises across banking, mobility, retail, manufacturing, and infrastructure actively engaged our ecosystem with real operational problems. Many moved quickly from exploration to pilots—and in several cases beyond—showing strong urgency to deploy AI for immediate business impact. Most surprisingly, some enterprises were willing to go beyond being customers and explore co-investment opportunities with startups following successful Proofs of Concept. This created a high-velocity environment where startups could secure multi-million-dollar enterprise deals and regional contracts far faster than traditional B2B cycles typically allow. It reinforced our belief that when enterprise innovation is anchored in real use cases and supported by the right execution framework, momentum accelerates rapidly.

Second, the biggest challenge has been moving from Proof of Concept to production at scale. While building a working prototype is often fast, enterprise-wide deployment introduces human and structural complexities that go far beyond the technology itself. Through our work with over 100 enterprises, three recurring friction points emerged:

1. Organizational readiness: AI cannot simply be “plugged in” to existing workflows. Successful deployment requires rethinking processes, ownership, and decision-making. Without this, even strong solutions struggle to take root.
2. Stakeholder alignment: Many projects stall due to gaps between executive intent, technical teams, and frontline operators. Without buy-in from middle management and clear operational ownership, momentum fades after the pilot phase
3. Measuring production impact: While pilots demonstrate technical feasibility, translating results into clear cost savings or revenue impact—aligned to existing business OKRs—is often harder, making it difficult to secure long-term investment for scaling.

Ultimately, we learned that the real work begins after the PoC. Moving from pilot to production is less a technical challenge and more an organizational one. Enterprises that succeed are those that treat AI as an operating model shift—not just a software deployment—and invest as much in change management and execution readiness as they do in the technology itself.

AI adoption as an organisational challenge

e27: What is one lesson about applying AI in real-world settings that leaders or founders often underestimate

Kai Yong: One lesson leaders and founders consistently underestimate is that deploying AI is primarily a people and operating-model challenge—not a technical one. Many organizations assume that once the tools are in place, adoption will follow. In reality, most AI initiatives stall because teams are not aligned on ownership, workflows, or decision-making. Without clear executive sponsorship, cross-functional accountability, and practical integration into daily operations, even strong AI solutions end up sitting unused. Another overlooked factor is employee perception. When AI is introduced without clear communication, it is often viewed as a threat rather than an enabler. This slows adoption, degrades data quality, and limits feedback—ultimately reducing the effectiveness of the system itself.

At GenAI Fund, we address this by starting with leadership alignment through Working Backwards workshops, building team readiness via AI Readiness bootcamps, and driving behavior change through hands-on Proofs of Concept in our GenAI Open Innovation programs. Practical exposure to real use cases helps demystify AI and builds internal confidence far more effectively than theoretical training. The leaders who succeed with AI in real-world settings are those who invest as much in change management, ownership, and execution readiness as they do in models and infrastructure.

Practical guidance for early AI adoption

e27: Based on your experience, what is one practical recommendation you would give to organisations that are just starting to explore or scale AI?

Kai Yong: Based on our experience supporting enterprise AI adoption across Southeast Asia, one practical recommendation for organizations starting or scaling AI is to focus early on two things: organizational readiness and fast, outcome-driven execution tied to business KPIs.

1. Start with people and operating readiness—not technology. Most AI initiatives fail not because of model performance, but because teams are not aligned on ownership, data access, or decision-making. Enterprises should establish clear executive sponsorship, cross-functional ownership (business + IT), and transparent communication with employees. When teams understand that AI is meant to augment their work rather than replace them, data quality improves and adoption accelerates.
2. Drive quick wins linked directly to cost savings or revenue growth—and map them to existing OKRs. Rather than launching broad transformation programs, enterprises should start with narrowly scoped use cases that can demonstrate measurable impact within 60–90 days—such as reducing manual processing costs, improving conversion rates, or accelerating sales cycles. Anchoring pilots to existing organizational OKRs ensures accountability, unlocks budget, and prevents teams from getting stuck in “pilot purgatory.” In practice, organizations that combine readiness with fast, metric-driven execution move significantly faster from experimentation to production—turning AI from isolated projects into a scalable business capability.

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Accelerating enterprise AI deployment timelines

e27: Over the next 12 months, how do you expect your organisation’s use of AI, or the role of AI in your industry, to evolve?

Kai Yong: Over the next 12 months, we expect a fundamental acceleration in how enterprises move from AI exploration to real deployment. Historically, large organizations take 3–5 years to progress from initial awareness to production-scale AI adoption. At GenAI Fund, our goal is to compress this cycle into a single year by using AI itself to orchestrate the transformation journey.

Our 2026 strategy focuses on accelerating three critical stages:

Awareness: Moving enterprises beyond surface-level AI curiosity through leadership alignment initiatives, masterclasses, and “Working Backwards” workshops—helping executive teams translate operational challenges into prioritized AI use cases tied directly to business outcomes.

Pilot: Leveraging our GenAI Open Innovation model coupled with our AI matchmaking platform to reduce solution sourcing and validation from weeks to minutes, enabling enterprises to rapidly identify qualified AI providers and launch structured pilots within weeks rather than months.

Scale: Transitioning successful Proofs of Concept into production through our FastTrack AI Accelerator, supported by hyperscalers and hands-on execution sprints—providing technical guidance, deployment support, and commercialization pathways to drive enterprise-wide adoption. By integrating these stages into a single operating model, we expect enterprises to move faster from intent to impact.

Rather than simply helping companies “adopt” AI, our platform and programs are designed to help them operationalize AI at speed—turning fragmented experimentation into measurable business outcomes and building AI-native capabilities within one fiscal year.

Building toward GenAI Open Innovation Summit 2026 (GOI Summit 2026)

e27: Anything else you want to share with the audience?

Kai Yong: One final thing we’d love to share is what we’re building toward in 2026. Later this year, we’ll be launching GOI Summit 2026 as our flagship enterprise AI conference—bringing together enterprises, AI startups, hyperscalers, governments, and investors from across the region. Our ambition is to create a regional “big bang” moment that positions Southeast Asia as the fastest AI adoption market globally. GOI Summit is not a standalone event—it’s the culmination of a year-long program designed to drive real adoption.

Leading up to the summit, we are running a series of initiatives including monthly GenAI Builders Meetups, GenAI Open Innovation programs with enterprises, and our scaling accelerator with hyperscaler support to help startups move from pilots to production.

Together, these form our “Road to GOI Summit,” continuously matching enterprises with AI builders, validating use cases, and pushing real deployments throughout the year. At the summit itself, we expect over 5,000 attendees, including more than 100 CIOs from large enterprises, thousands of enterprise executives, and 1,000 AI startups globally. Our goal is to facilitate 500 curated enterprise–startup matchmaking sessions and catalyze at least 100 new AI Proofs of Concept directly from the event, alongside showcasing 100+ real enterprise AI case studies.

More broadly, we see Southeast Asia at a unique inflection point. With rapidly digitizing enterprises, growing AI talent, and increasing urgency to stay competitive, the region has the opportunity to leapfrog into global leadership in applied AI. GOI Summit 2026—and everything leading up to it—is our way of accelerating that future by turning AI ambition into measurable execution.

Enterprise AI adoption at scale

This conversation highlights the accelerating shift from AI experimentation to real enterprise deployment across Southeast Asia. As organisations move beyond pilots toward operational integration, the focus increasingly turns to execution readiness, ecosystem collaboration, and measurable business outcomes. Initiatives that combine investment, transformation expertise, and ecosystem building may play a key role in shaping how AI adoption scales across the region.

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Featured Image Credit: GenAI Fund

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