DBS has expanded its Spark GenAI programme as Singapore’s SMEs move into a more execution-focused phase of artificial intelligence (AI) adoption. Developed in partnership with the Infocomm Media Development Authority (IMDA) and Enterprise Singapore, the programme builds on an earlier Spark GenAI version introduced in 2024 that focused on awareness and access to tools. It now introduces clearer pathways to help SMEs translate early use into operational deployment. The enhanced programme addresses a key challenge in SME adoption, where early experimentation has not consistently translated into wider operational deployment. It introduces a more structured progression from identifying use cases to integrating them into business operations, with a greater focus on implementation and measurable outcomes. Tiered pathways aligned to SME readiness At the centre of the enhanced Spark GenAI programme is a three-tiered structure aligned to SME readiness, designed to guide firms through successive stages of adoption. Early-stage firms are supported through advisory, training and subsidised access to off-the-shelf solutions, enabling them to address immediate operational needs such as workflow automation or basic customer service tools without significant upfront investment. Firms seeking to accelerate adoption are provided with structured assessments and group-based consultancy. This stage focuses on identifying specific use cases and aligning them with business priorities, helping companies move into targeted deployment. More advanced organisations are supported through one-to-one consultancy to design technical roadmaps and integrate AI more deeply across operations. This tier also facilitates access to a global network of more than 16,000 solution providers through IMDA’s Open Innovation Platform. This supports more complex implementations. The programme integrates financial support alongside capability development. DBS provides financing solutions designed to support digital transformation while addressing concerns around risk and resilience, bringing together funding, skills support and access to partners in a single framework. Across all tiers, the design reflects the varying levels of SME readiness. Helping SMEs move from experimentation to action Within this shift towards execution, DBS has positioned the enhanced Spark GenAI programme as a way to support SMEs in applying AI within their operations. “AI is here to stay. It’s become an integral part of how business leaders think about their challenges and the solutions for them,” said Chen Ze Ling, managing director and group head of corporate and SME banking at DBS. DBS reported that more than 380 companies have signed up for the programme to date, with participation concentrated in professional services, wholesale and retail, and construction — sectors where process efficiency and cost control are under pressure. The sign-up figures suggest demand for structured support is concentrated in these areas as firms begin to operationalise AI. He noted that the path to implementation remains unclear for many organisations. “The answers may not be very apparent to all of us. Some are thinking about use cases, some about needs, some about payback and cost, and what kind of resources to deploy,” he said. He added that adoption is not uniform across businesses and often requires an iterative approach, where companies start with smaller initiatives and refine them over time rather than treating AI as a single implementation. “The community really matters, the ecosystem really matters… this is where public-private partnership comes together to remove some of the friction in adoption,” he added. Lim Him Chuan, country head of DBS Singapore, placed the programme within a broader operating environment. Rising costs and increasing expectations for digital capability are influencing how SMEs approach AI adoption. A DBS Pulse Check survey found that nearly two out of five SMEs are seeking guidance at different stages of adoption, pointing to a gap between interest and execution. Lim highlighted that while awareness of AI is no longer the primary constraint, clarity on how to apply it within business operations remains a key issue. He emphasised that mindset and clarity of purpose are critical in determining whether adoption translates into measurable outcomes, particularly as businesses move into implementation. Drawing on DBS’s internal experience, he noted that the bank has developed more than 400 AI use cases supported by over 2,000 models. This informs how DBS approaches SME enablement, particularly in connecting demand with relevant solutions and ecosystem partners. Practical lessons from early implementers These structural elements were reinforced in a panel discussion on practical AI implementation (conducted under Chatham House Rules). A consistent observation was that AI adoption has moved beyond awareness. Many businesses are experimenting with tools but are encountering difficulty translating this into operational impact. The discussion highlighted how progress begins with reframing how AI is approached. Rather than starting with available technologies, businesses that advanced more quickly to deployment began with clearly defined problems — low conversion rates, inefficient workflows or underutilised resources — enabling more targeted applications and faster rollout. Another recurring theme was the importance of ecosystem support in reducing execution risk. Access to curated solution providers and structured platforms was seen as critical in helping businesses avoid fragmented approaches. By connecting problems with pre-vetted solutions, these platforms enable firms to implement in a more controlled and efficient manner. Practical experiences also pointed to the need for a resource-conscious approach. SMEs that leveraged existing platforms and solutions were able to accelerate adoption while minimising cost and complexity, particularly where in-house technical expertise is limited. Implementation was described as iterative, with companies starting with smaller initiatives to build confidence and align teams before scaling. Participants identified inaction as the primary risk. As AI becomes more embedded in competitive dynamics, the advantage of early adopters may increase over time, making it more difficult for lagging firms to close the gap. How peers compare: UOB, OCBC and regional banks Within the wider banking sector, similar efforts are emerging, although the structure and emphasis of these initiatives vary. United Overseas Bank, for example, has developed the FinLab AI Ready Programme in collaboration with AI Singapore and IMDA. The initiative operates as a cohort-based accelerator that helps SMEs move from awareness to proof of concept through masterclasses, access to pre-configured AI tools and guided pilot projects, typically delivered in batches through the FinLab platform. It is also supported by financing structures linked to government grants, enabling firms to test use cases before committing to wider deployment. This cohort-driven approach differs from more continuous adoption models, where support extends into implementation and integration within business operations. OCBC Bank has taken a different approach, embedding data and analytics capabilities within its banking platforms. Tools such as integrated business dashboards and digital cash management systems support operational decision-making within existing workflows. Across the broader Asia Pacific region, many banks are prioritising AI within their own operations, particularly in areas such as credit assessment, customer engagement and process automation. CIMB Group, for example, is embedding AI into digital lending and onboarding channels, although these efforts appear to focus more on product-level transformation than structured SME adoption programmes. In this context, Spark GenAI brings together funding, advisory and solution access within a single framework. This is intended to simplify the move from identifying use cases to implementation, where many SME initiatives have historically stalled. What comes next for SME AI adoption The expansion of Spark GenAI points to changes in how SME transformation is being operationalised. As access to AI tools becomes more widespread, attention is shifting towards how these capabilities are embedded within business processes and decision-making. This also reflects changes in the role of financial institutions, where banks are extending from financing into facilitating access to technology ecosystems and capability development. Across the industry, adoption pathways are extending into sustained use across functions and alignment with commercial objectives. For SMEs, this places greater emphasis on execution discipline. The ability to prioritise use cases, integrate solutions into existing workflows and build internal confidence will determine whether early use translates into sustained operational impact.
