Singaporean organisations are increasingly confident in their ability to govern AI. But while they are investing heavily in AI and the governance structures around it, a more fundamental question is becoming harder to answer – is that investment actually delivering measurable business value?
New research from OneTrust’s recent 2026 AI Governance Survey and Report shows a striking disconnect. While 82% of organisations believe they are mature in measuring the cost and return on investment of their AI systems, 30% identify measuring AI performance, benefits and business impact as their biggest cross-functional governance challenge. The message for business leaders is now becoming clear: AI confidence is rising, but demonstrable value is struggling to keep pace.
Arran Mulvaney from OneTrust explains why Singapore is moving AI governance from the IT department to the boardroom
Confidence is not the same as proof
Measuring traditional technology investments is relatively straightforward, with businesses able to assess metrics such as cost savings, productivity, revenue and efficiency. AI can influence all of these areas, but its impact is often spread across multiple teams and processes, making the return harder to isolate.
An AI tool may save employees time, improve customer interactions or accelerate decision-making, but translating those benefits into a clear return requires organisations to define what they want AI to achieve before deployment and measure its impact afterwards. That means looking beyond AI as simply a way to reduce costs or improve efficiency, and considering how it can support growth, innovation and better business outcomes. If measuring AI’s worth and business impact remains the biggest cross-functional governance challenge, organisations need to look beyond whether governance frameworks exist and ask whether they are helping demonstrate what AI is actually delivering.
That question is becoming increasingly relevant across Singapore, as rising AI investment puts greater pressure on companies to be more selective about deployment and demonstrate a clear ROI. While the cost of individual AI models has fallen, overall AI bills are rising as usage increases, with token consumption reportedly growing 4.5 times between 2024 and 2025. As AI becomes more deeply embedded, adoption alone is no longer enough; businesses need to show that increased usage is translating into meaningful outcomes.
The investment is accelerating
This growing focus on measurable outcomes is also influencing where organisations are investing. Further data from the OneTrust report found that a quarter of Singaporean organisations plan to increase their budget by 22% in the next financial year to modernise technologies used to govern AI, while 37% identify streamlining governance workflows to contribute to ROI measurement and frameworks as a governance priority.
But increasing investment in governance without improving how outcomes are measured risks creating more complexity without greater clarity. The next phase of AI maturity cannot simply be about deploying more technology or controls. It needs to connect governance to business outcomes.
That shift is already reaching the boardroom. In July, the Singapore Institute of Directors launched a guide for boards on overseeing AI, encouraging directors to look beyond activity-based measures when assessing whether AI investments are delivering results. Metrics such as the number of AI pilots, licences or prompts can demonstrate adoption, but not necessarily business impact. Instead, the guide points to outcomes such as time saved, workflow improvements and the proportion of pilots that progress into production as more meaningful measures of value.
This is a step in the right direction, but ultimately, meaningful AI outcomes start with leadership. Organisations that treat AI as a strategic business priority, rather than simply a technology investment, will be better positioned to turn adoption into measurable impact.
The new measure of AI maturity
The organisations that move ahead will treat measurement as part of AI governance from the outset. That means establishing success metrics before deployment, assigning ownership for measuring outcomes and connecting AI performance to broader business objectives. Governance teams shouldn’t sit in isolation and instead work closely with finance, technology and business leaders to understand not just whether an AI system is compliant and operating as intended, but whether it is delivering the outcomes it was introduced to achieve.
Singapore’s AI market is entering a more mature phase. The question is no longer simply whether organisations are confident enough to adopt AI or have governance structures in place to control it. The next question is harder – can they prove it is working? As AI investment accelerates, the organisations that create lasting value will be those that connect governance, performance and investment to measurable outcomes.
The article titled “Singapore’s AI spending is rising, but can businesses prove the return on investment?” is to be attributed to Arran Mulvaney, Regional Director, ASEAN & India at OneTrust
About the author
Arran Mulvaney is Regional Director for ASEAN and India at OneTrust, based in Singapore. He leads regional go-to-market strategy across Southeast Asia and India, working with enterprises in highly regulated sectors to operationalise data privacy, AI governance, consent, and third-party risk programs.
Arran partners with senior business, legal, risk, and technology leaders to help organisations move from regulatory awareness to practical execution. His work focuses on building trust as a business enabler, helping companies manage regulatory complexity, adopt emerging technologies responsibly, and create scalable governance programs across fast-growing Asian markets.
He is a frequent speaker on privacy, AI governance, and trust, with a particular focus on how organisations can turn compliance obligations into stronger customer relationships, better risk management, and more resilient digital growth.