Looking for ROI from AI? Focus on scale

According to our Global AI performance study, Singapore outperforms many peers on AI investments and the number of pilots in effect. As Singapore moves from experimentation to scaled deployment, the focus naturally shifts to strengthening AI foundations so value can be realised safely and sustainably. Rather than a gap, this momentum represents a high-impact opportunity: building on strong investment and AI pilot activity to scale AI performance and reinforce Singapore’s trajectory towards hub status.

Since the initial push for economy-wide AI adoption, Singapore has proved to be an exemplar of AI investment and integration. According to our findings, organisations in Singapore (67%) were more willing to take risks when investing in AI compared to those in other countries (41%). Some 63% of organisations surveyed in Singapore are also able to make key financial and workforce decisions based on AI opportunities, higher than the 51% globally.

This drive towards AI engagement is also reflected in underlying technology readiness: 30% of Singapore respondents say their organisation has eliminated outdated IT infrastructure, ahead of the 18% recorded globally, though still behind the 40% seen among the world’s highest-performing AI companies. 

Singapore’s rapid adoption of AI has also led organisations to deploy AI in more advanced ways. Many countries still use AI for less advanced tasks, such as analysis, prediction and recommendation (37%), compared with 20% in Singapore. By contrast, organisations in Singapore are already using AI in autonomous and self-optimising ways (17% versus 8% globally).

Nature of most-sophisticated AI use case

Singapore also outperforms the global average in using AI beyond traditional sector boundaries, with 43% applying AI to challenge companies outside their own industries, compared with 20% globally.

In our survey, AI leaders refer to organisations whose average AI-driven performance is in the top quintile among those surveyed—who achieved 7.2x the revenue and efficiency gains compared to the rest of respondents on an industry-adjusted basis.

Set against this benchmark, while AI performance is rising in Singapore, comparison with this group highlights areas where organisations can benefit from their example. These include governance and risk management areas, such as applying robust, up-to-date security to protect data, AI models, and underlying infrastructure (53% Singapore versus 69% AI leaders), maintaining a documented responsible AI framework (47% versus 63%), and having a cross-functional AI governance board (43% versus 64%).

Data capabilities also differentiate top performers. For example, 37% of Singapore respondents say their organisation maintains a single, trusted record of critical data that is accessible across the business, compared with 59% among AI leaders. Use of structured data, such as tables with defined fields, is reported by 40% of Singapore respondents, compared with 60% among AI leaders.

Top performers distinguish themselves by unlocking new value. Many organisations in Singapore are using data to support AI initiatives, while AI leaders go a step further by creating new value through data, such as deeper insights or monetisation opportunities (43% Singapore respondents versus 63% AI leaders). Some 37% of Singapore respondents report sensing emerging value pools, compared with 60% among AI leaders.

When it comes to processes, 56% of AI leaders report redesigning workflows to incorporate AI more fully, rather than simply adding AI tools onto existing processes, compared with 37% Singapore respondents. 

At the same time, national priorities are actively reinforcing AI adoption and performance at scale. Signals from Singapore’s 2026 Budget announcement and the recent Committee of Supply debates point to a clear ambition to scale AI performance and use across industries, echoing priorities and themes surfaced in our survey.

Budget 2026 emphasises AI readiness: clearer enterprise AI visions, sectoral sandboxes to de-risk experimentation, support for cross-industry collaboration, and targeted investment in priority sectors. The government is also setting the pace on AI strategy, infrastructure and guardrails through the launch of the National AI Council and national AI Missions. These initiatives build upon earlier efforts by Infocomm Media Development Authority (IMDA) and the AI Verify Foundation to operationalise trust in GenAI through initiatives, including the Global AI Assurance Pilot in 2025, enabling organisations to test applications and strengthen assurance at scale. Together, these measures help address trust as a scaling constraint by strengthening oversight, clarifying expectations and creating structured environments for responsible AI use.

While these measures create favourable conditions to accelerate responsible and scalable AI use, organisations must still align operating models, security and assurance, talent, and investment to take advantage of them. For many organisations, this means building confidence in how AI is governed and used as it moves into core operations.

To unlock economy-wide value, Singapore businesses must strive to meet these national goals in the middle to maximise AI performance at scale. 

Our study found that AI leaders invest 2.5x more than other companies and are 2.4x more likely to maintain reusable AI components across their organisation. They are also 1.7x more likely to ensure that high-quality data is readily available for prioritised AI applications.

They are roughly twice as likely to apply AI across the full breadth of business functions, including corporate strategy, supply chain management, and front and back-office operations, while being nearly twice as likely to operate AI at higher levels of sophistication. They are also 1.5x more likely to use AI to develop new business models, and 1.2x more likely to use it to drive revenue growth, rather than focusing solely on efficiency gains.

But what does a successful AI ecosystem look like in practice? How should AI policy and enterprise strategy reinforce each other? Top performers are distinguished less by intent than by how they deploy AI. Looking to the AI leaders surveyed, three priorities stand out for organisations seeking to translate AI ambition into sustained performance: