From Knowing to Doing: Closing the AI Adoption Gap for Singapore SMEs

Singapore’s SMEs are operating in an environment of rising costs, tight labour markets, and increasingly demanding customers. At the same time, they face significant competitive pressures from larger firms with greater financial resources and wider regional presence. Compounding these challenges, digital-native competitors are redefining expectations around speed, personalisation, and always-on service. This is placing intensifying pressure on traditional SMEs to adapt or risk losing relevance.

The current environment reflects AI’s evolution from a nice-to-have initiative to an essential business capability. AI models are becoming more accurate and secure, while integrating stronger support structures. Together, they now allow organisations to tackle business challenges with greater confidence. Improved cost accessibility of AI solutions has also enabled wider adoption.

Awareness of AI has grown rapidly in the last few years. Yet, our recent survey on Singapore SMEs’ AI adoption suggests that most businesses are still only scratching the surface. Adoption remains limited to basic digital strategies. AI is often implemented as siloed solutions, lacking integration with broader business processes. This gap points to challenges in identifying relevant use cases, building technical readiness, and adopting more advanced AI capabilities.

As AI reshapes business operations, every organisation’s experience tells a different story. We want to hear yours. 

Contribute to policy outcomes: Participate in our upcoming research studies by registering your interest. 

 

From Assessment to Action: Finding Your Starting Point in 2026 

In 2026, SMEs looking to remain competitive and scalable can begin by assessing their current AI maturity. Understand where the organisation stands on the AI maturity curve. Identify use cases that align with existing strengths and resources.

The AI maturity curve offers a practical framework for business leaders. It helps to assess their position and allows them to plan next steps. Leaders can align AI adoption with business priorities and workforce readiness. Over time, it enables effective integration of high-impact applications.

Building AI value should be a progressive process. Quick-win implementations, such as process automation, deliver immediate returns. Higher-value applications, like AI-driven dashboards, can be layered on over time. Each stage should provide incremental value, creating the foundation for scalable growth. Organisations that periodically assess their AI maturity are better positioned to drive continuous AI improvement and stay competitive. They move from reactive adopters to AI leaders, staying ahead of rivals in Singapore’s tightening market.

Read more: AI and the Human Touch: How Leadership Paves the Way 

 

Reactive AI   

Reactive AI represents the entry-level stage on the AI maturity curve and focuses on automation. These systems execute tasks based on fixed rules and without memory, learning from past data, or anticipating future events. They produce consistent outputs for the same inputs. 

Reactive AI can streamline everyday operations through tools like automated email responses and basic workflow rules. Email triggers can send templated replies, helping businesses manage surges in customer enquiries. Retail SMEs often use simple workflow rules to prevent immediate disruptions. These include flagging invoice errors and generating low-stock alerts. 

For example, a hotel managing thousands of supplier invoices uses reactive AI to automate data processing and approvals. This has resulted in fewer late payments, faster turnaround, reduced errors, and lowered manpower needs. 

 

Who Operates Here

Businesses at this stage are often SMEs or departments new to AI. They face challenges such as skills gaps, limited resources, or uncertainty around return on investment. 

To operate effectively here, companies will need to identify processes, workflows, and tasks that are rules-based and repetitive This includes mapping operations, standardising procedures, and ensuring data is structured and accessible. By doing so, AI can reliably automate tasks and boost efficiency, forming a foundation for broader adoption. As AI maturity grows, organisations should build on these foundations and progress further along the curve. 

Concurrently, companies must weigh the costs and benefits, identifying where AI adds value and where human intervention remains essential. While AI excels at repetitive, data-intensive work, humans remain essential for other tasks. These tasks demand judgment, critical thinking, or oversight as the approver of AI-driven processes.  

 

Predictive AI 

Predictive AI is the next stage on the AI maturity curve. Unlike reactive systems that respond to fixed inputs, predictive AI relies on existing data. It processes large volumes of data, drawing on historical and real-time data to project demand. Based on anticipated risks, it then informs decision-making. 

Predictive AI can forecast long-term customer value and enable predictive maintenance. This helps businesses identify high-value customers for personalised engagement. Businesses can also schedule maintenance before equipment failures to reduce downtime and operational risk. 

For example, Estée Lauder, a global cosmetics and beauty company, uses predictive AI to analyse dispersed data. The system summarises trends and customer insights. Once taking hours, it is now completed in seconds to support marketing and product development.

 

Who Operates Here 

Mid-maturity SMEs and growing departments may operate at this stage. They have moved beyond reactive tools, and leverage data analytics and machine learning for forecasting.  

To operate effectively here, companies will need to invest in data readiness and develop analytical capabilities. This includes digitising data, tracking relevant business metrics for forecasting, and ensuring accuracy. By doing so, companies can integrate  reliable insights into decision-making processes. 

By shifting from reactive to forward-looking strategies, predictive AI enables organisations to act earlier and plan more effectively. 

Companies in Singapore are starting to engage with AI. We want to understand the challenges you may be facing. 

Contribute to policy outcomes: Participate in our upcoming research studies by registering your interest. 

 

Autonomous AI 

At the highest level of the AI maturity curve sit autonomous AI systems. They operate independently, make decisions, refine processes, and execute tasks with limited human supervision. 

With autonomous AI, companies can automate routine operational and strategic tasks. For instance, managing inventory by reordering stock and adjusting logistics in real time to prevent shortages or overstock. Another example is optimising marketing campaigns by continuously fine-tuning targeting and messaging to boost engagement. 

 

Who Operates Here

Autonomous AI adoption is expected to remain low and concentrated among AI-driven firms. This is largely due to the significant capabilities involved. Some include advanced data infrastructure, integrated datasets, sustained investment in technology and talent, and a higher risk tolerance. Autonomous systems execute decisions independently. Hence, businesses must have strong monitoring and accountability mechanisms in place before transitioning to fully autonomous operations.

 

Advancing AI with Momentum 

Adopting AI incrementally can further help manage risks. In a Business Dialogue Session conducted by IndSights Research, Mr. Michael Goh shared his perspective. Mr. Michael Goh is the CEO of Innovation Partner for Impact (IPI) Singapore, a subsidiary of Enterprise Singapore. He emphasised that SMEs can achieve meaningful transformation through bite-sized implementations. By leveraging readily available technologies, SMEs can deliver incremental wins. This helps build the momentum needed to scale impact over time, without reinventing the wheel. 

Targeted initiatives are in place to support SMEs in building AI capabilities. One initiative is the ASME-Lenovo partnership. It provides up to S$1 million in funding for hardware, technical expertise and training to selected SMEs. Businesses previously constrained by eligibility requirements can now for technology investments. 

Reinforcing this push, Budget 2026 introduced the Champions of AI programme and expanded both the Enterprise Innovation Scheme and the Productivity Solutions Grant. This move aims to support a broader range of AI investments. Together, these initiatives support businesses in transitioning from pilots to scalable implementation. 

Read more: Unlocking Growth: AI Adoption for Singapore Businesses (Part 1)

 

Building AI Capabilities with Purpose 

Understanding your organisation’s position on the AI maturity curve and taking the steps to advance is critical.      

Firms can begin by assessing whether they have the capacity to develop solutions in-house. Perhaps, partnering with an external provider may be more effective for some. Firms should also ensure that clear milestones and risk mitigation are in place.  

Mr. Michael Goh underscored the importance of taking a structured approach. Organisations should clearly define business problems. Use tools like the “5 Whys” to uncover root causes. Treat technology as an enabler. A clear business case must come first, followed by identifying how AI can best address the need.  

Meaningful progress also comes from acting on insights and learning quickly from failures. That is a point highlighted by Mr. William Smith, Head of Mid-Market Asia & Small Business Sales APAC of Zoom Communications, Inc.   

While careful planning, considering both success and failure, and selecting the right technology fit remain critical, SMEs need not start from scratch. Singapore has a robust ecosystem of government support, industry networks, and technology partners provides. Businesses are equipped with the resources and guidance needed to accelerate their AI adoption journey. 

This article is contributed by Tracy Chow, Research Executive, IndSights Research.