Tech Explained: Supporting technology producers to spur AI adoption in Simple Terms

This article first appeared in Digital Edge, The Edge Malaysia Weekly on December 29, 2025 – January 4, 2026

The phrase “artificial intelligence” (AI) is arguably one of the most overused buzzwords in the technology sphere in recent years, often cloaked in theoretical promise rather than practical impact. In contrast, GenAI Fund cuts through this hype by investing in AI-first technology companies that are driving tangible, real-world adoption of AI, with a particular focus on large enterprises.

Within the fund’s architecture is its AI-driven recommendation platform. For corporate entities, from banks and telecommunications firms to manufacturing giants, the process is now instantaneous, says Denning Tan, a partner at GenAI Fund.

This dramatically reduces the time and manpower that enterprises traditionally dedicated to scouting, vetting and initial due diligence, which could typically take months says Tan. He adds that the fund has also invested in fine tuning the engine’s accuracy, treating it as a key performance indicator.

“From an enterprise perspective, they can post their use case instantly on our platform and, within seconds, we are often able to recommend 10 to 15 AI start-ups that align with the specific solutions they are seeking,” he explains.

Kang Kai Yong, a partner at GenAI Fund, cites the example of Sime Darby Property, which the fund supported through the Cradle BIG programme. One of the start-ups that GenAI Fund is now working with was identified via its AI recommendation platform.

“It’s a big win for us because we want to really make sure that this AI engine is more intelligent than us, our combined intelligence,” he says.

“Eventually, it must truly help the enterprise to shorten the time to make the right decision. This is also why we are confident of onboarding 10,000 start-ups — because now that we have this platform, it’s growing by itself. The start-ups are spreading the word to their peers.”

Having aggressively scaled up its platform from an initial cohort of 500 start-ups to an active base of 2,500 in the span of a year, the fund is now setting its sights on onboarding 10,000 start-ups to its ecosystem by end-2026, positioning itself as a bridge between start-ups that are global AI innovators and Asian enterprises.

The exponential expansion of the platform, from a core focus on Asean markets, is a reflection of how innovation is sourced and commercialised across the region. The company developed its own AI matchmaking engine to replace slow, manual solution sourcing by directly matching enterprise needs with start-up solutions.

“Right now, AI start-ups are hungry for enterprise customers, regardless of where they are based because the use cases are pretty common across industries,” says Kang.

“We’re getting start-ups from beyond Asean, including Taiwan, Hong Kong, Australia, India and the US, hence the scale we are operating at demands a new approach. So, we started building our own AI matchmaking engine because we truly believe in walking the talk. To be an AI fund, we must embrace and adopt AI ourselves.”

Increasingly, governments are engaging GenAI Fund to help them specifically manage the inbound and outbound partnerships for their start-ups and enterprises.

One of the fund’s new key markets is Taiwan, where GenAI Fund is keen for start-ups to branch out and offer solutions for Asean-based businesses and, conversely, for Asean start-ups to explore their solutions and skill sets in AI with Taiwanese enterprises.

GenAI Fund is currently collaborating across large enterprise, big tech and government agencies to develop and execute workshops, programmes and events to produce compelling results on AI adoption, particularly with enterprises and key industries, through innovative solutions and partnerships with AI start-ups.

“They treat [our platform] as a launching pad. AI start-ups that are globally minded from day one do well; so, the goal is for them to succeed locally, regionally, in Asia and then globally. And they have to move very quickly and we provide them with real enterprise use cases and become an engine to accelerate this.”

The core challenge in enterprise adoption has always been speed and relevance. Typically, a start-up’s proof-of-concept (POC) is difficult to scale or their technological solution never reaches the right decision-maker. These types of decisions in enterprises need to be done from the top down, says Tan.

“For example, we have enterprises we work with that tell us they want to allocate 10% of their profit just for AI.

“They have never done this before, but they have a very clear, top-down approach, and that’s the kind of aggressiveness we will see from certain enterprises.”

GenAI Fund distinguishes itself by offering a hyper-specific, three-pronged support structure designed to ensure POC success translates into long-term production deployment.

The fund runs extensive programmes designed to connect start-ups with the right corporate stakeholders.

“The first thing, for us, is to make sure that the start-up can get the right connection,” stresses Laura Nguyen, a partner at GenAI Fund.

“These programmes ensure that the main stakeholders and decision-makers are present to evaluate the use case and the team, guaranteeing immediate feedback and reducing sales cycle friction.”

The fund also proactively mentors start-ups to look beyond the immediate glory of a successful pilot. They encourage a structured approach, focusing on short-, mid- and long-term milestones.

“We encourage them to have these varying focuses. In the short term, the focus is on how start-ups can make sure their POC is successful and how to get it picked up. For the mid-term, the focus is on how to scale it,” says Nguyen.

“For the long term, it’s focusing on how start-ups can make sure that their system or the solution is sustainable and can go into production successfully.”

This mentorship, supported by a network of venture builders with strong technical and operational backgrounds, is vital for mitigating the common “pilot purgatory” syndrome.

Recognising that enterprise environments require higher levels of reliability and integration, GenAI Fund provides practical technical guidance to start-ups participating in its programmes. This includes helping start-ups clarify enterprise requirements, refine their product workflows, and prepare their solutions for real-world pilot conditions.

The fund also works with start-ups to review integration needs with the enterprise’s existing systems, ensuring that the proposed solution aligns with technical, security and scalability requirements set by the enterprise.

Tan notes that GenAI Fund further supports start-ups by helping reduce infrastructure costs through access to cloud and compute programmes.

“This helps ease the financial burden for start-ups as they begin running pilots that require higher-volume workloads,” Tan says.

Furthermore, acknowledging the bureaucratic hurdles that often stifle innovation, the fund takes on the arduous task of procurement.

“Like it or not, procurement is really a pain. That’s why, for some of the pilots, we liaise directly with the enterprise procurement teams to reduce friction for start-ups,” Tan says, adding that this practice is a vital shield for the start-ups.

As the technology matures, the conversation has shifted from purely large language model (LLM) capabilities to practical, measurable impact.

“People are also very outcome-focused right now — multi-model, smaller model, workflow and so on,” says Tan.

A key emerging trend that the fund is banking on is agentic workflow. This refers to AI systems designed to autonomously execute multi-step tasks within an enterprise environment, moving beyond simple Q&A to perform complex operations such as data analysis, report generation and system integration. This transition is critical for delivering substantial productivity gains.

“Right now, there is the proliferation of outsourced models, shifting the landscape from a dominance of closed source models like ChatGPT and Claude to the rise of open-source and specialised models,” says Tan.

He adds that this diversification allows enterprises to select highly tailored, cost-effective models that can be hosted on-premise or managed with greater control, a necessity for industries dealing with sensitive data, such as finance and healthcare.

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