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July 1, 2025
Perhaps Steve Jobs put it best..
“To me, ideas are worth nothing unless executed. They are just a multiplier. Execution is worth millions.”
An age-old quote by the late Apple co-founder that can be attributed to any technology, at any given time, in any given market.
But in the context of artificial intelligence (AI) – and all the perks and perils that surround it – this assessment is most profound.
For most organisations at the AI starting line, the pendulum continues to swing from planning to proof of concepts (POCs), and back again. One step forward, sometimes two steps back.
When pressed, which word best summarises the current state of AI in Singapore – enthusiasm or execution?
“Enthusiasm,” observed Ivan Ng, CTO of City Developments Limited (CDL). “Many organisations are realising that AI has immense potential to enable their businesses.
“While the engagement levels in Singapore are encouraging, they remain exploratory, however. The excitement is real, and leadership teams recognise the potential of AI, but the challenge remains in converting that intent into scaling into business-critical applications. That’s where the value creation comes from.”
Such observation is mirrored in the data – 82% of senior leadership teams in Singapore are actively championing AI initiatives and driving AI strategies company-wide.
Top-down enthusiasm is further supported by 63% of IT departments driving the full-scale use of AI across organisations, followed by similar commitment from senior management (39%) and at CEO level (20%).
Spanning business units, early stage interest is developing across a range of departments, each housing different use cases and requirements.
Specifically, finance and accounting are driving AI adoption in 26% of companies surveyed in Singapore, followed by human resources (19%), business operations (19%) and product development (16%). This is in addition to sales and marketing plus customer service teams in 15% and 13% of businesses surveyed, respectively.
That’s according to findings from AI in Action: Singapore Edition – Moxie Research commissioned by Dell Technologies and Intel. A total of 252 IT decision-makers in Singapore were surveyed in January 2025.
“Enthusiasm,” added Nirupam Das, Asia Data Leader at Liberty Mutual. “There is a lot of support from the ecosystem – such as government and various technology product companies – that are willing to co-partner to build POCs. There’s a lot of thought leaders in this space in Singapore.”
Based on Moxie Research, most organisations in Singapore are segmenting the process of AI adoption into three core phases.
In phase one, the below initiatives are taking on increased priority during the next 6-12 months:
For organisations already at phase two, the below initiatives are taking on increased priority during the next 6-12 months:
At the mature end of the scale in phase three, the below initiatives are taking on increased priority during the next 6-12 months:
“I would say it’s a synergy where enthusiasm meets execution as ‘enthu-cution’,” shared Juliana Chua, Global Head of Digital Tech and Innovation at EssilorLuxottica. “The enthusiasm comes from an importance to build more momentum in view of a high potential for bottom line impact.
“The value of AI comes from rewiring how companies run and execution is crucial with jointly owned AI governance, and encouraging workforce participation in AI deployment as a starting point to move forward.”
Don’t let bad data derail AI deployments
According to Moxie Research, 75% of organisations in Singapore cite data and infrastructure as the most foundational pillar of their AI strategy. This is followed by governance and ethics (66%), operational integration (64%) and performance measurement and optimisation (58%).
“Data readiness is the most common issue, whether it involves fragmented, siloed data or poor-quality data,” Ng of CDL added. “This can effectively mean that AI is not able to use this data to be useful to the business.”
In response, 69% of businesses in Singapore are building ‘modern, scalable data infrastructure optimised for AI’ – a commitment which is also driving increased demand for graphics processing units (GPUs).
The primary motivations for using GPUs to support AI and machine learning initiatives are:
“Don’t underestimate the challenge created by poor processes and poor data quality and management,” Das of Liberty Mutual cautioned. “New edge companies that don’t have legacy processes or data tools have an edge here.
“Start from the basics of eliminating bad and redundant rules and processes. You don’t want to train your AI wrong. Get the fundamentals of data management and AI governance created before one goes full blown into AI. I think prompt engineering and prompt analysts are good places to start.”
Based on Moxie Research, the most common deployment challenges preventing AI success in Singapore can be identified as:
“Another common challenge is a lack of change management,” added Ng of CDL, referencing the people equation in roll-outs. “AI is transformational, so it’s natural for internal pushback to occur – this is especially so if it involves rethinking workflows and perhaps the possibility of AI replacing jobs.
“As business leaders, we must be empathetic and ensure that these difficult conversations are addressed upfront, whether the impact to employees is new processes, re-skilling or even transitioning to a different role.”
Building on the roadblock thread, Chua of EssilorLuxottica acknowledged that strategically, as new areas emerge in AI, a centralised model to navigate becomes “purposive”. In this scenario, a Centre of Excellence (CoE) becomes invaluable.
“A CoE providing a broad roadmap, anchored by clear guidelines while being visible and responsive along the way,” Chua added. “Information can then be distributed across functions to enable greater momentum.”
On the ground, Chua said the challenges observed publicly rest in content review and GenAI risks.
“The six key risks that recur are: inaccuracy, cyber security, IP infringement, regulatory compliance, privacy and explainability,” she explained.
Understanding how to amplify AI use cases
Although current AI-related use cases are dependent on the industry in some instances, common ground is being found regardless of company size or sector.
For example, AI in the form of business automation is already in play for organisations in Singapore seeking to digitise processes (76%) and analyse documents (55%). For 59% of companies, this also extends into decision making through access to strategic data insights while 52% of those surveyed cite executive intelligence as a key benefit.
From a customer experience standpoint, 61% of businesses in Singapore are currently embracing some form of AI personalisation, as well as chatbots (56%) and voice assistance (55%).
Improved productivity (61%) and workforce optimisation (57%) are cited as the most impactful positives for employee experience.
As outlined by Chua of EssilorLuxottica, three key AI modalities exist:
“Automation is a great starting point with large content loading, quality analysis, workflows and information distribution,” Chua added. “AI bridges a significant gap as it comes with an added edge to manage and extract value from unstructured data.”
At CDL, Ng said the business has experienced “tangible benefits” in AI around customer engagement.
“Coupled with existing customer information, this can help us better understand their concerns and proactively address them, leading to better customer satisfaction,” he added.
“AI for facilities management is also a good example in which predictive maintenance is widely used to anticipate issues, and pre-emptive maintenance can be scheduled.”
In a direct message to fellow CIOs in Singapore, Ng advised technology leaders to understand that AI is “not a silver bullet”.
“Understand the limitations of its technology at this juncture, while staying open to its potential growth in the future,” he recommended. “This will allow CIOs to align a clear AI strategy to business outcomes, based on current capabilities and future potential in subsequent phases.
“Like other technology transformations, AI is not just about the best technology. It’s about training, up-skilling and ensuring that stakeholders understand where AI can help them in their work or businesses.”
Even though a low percentage of businesses in Singapore currently consider their level of AI adoption to be mature, Chua of EssilorLuxottica argued that the impact and value of the technology cannot be denied even at this early stage.
“To realise the potential of AI, a dedicated executional team is key with phased roll-outs and an established roadmap to drive adoption – continually tracked with well-defined KPIs,” Chua expanded.
On the topic of KPIs however, only 73% of organisations in Singapore currently have a “systematic process” in place for measuring and analysing AI performance.
“Build awareness and momentum through internal communications about the value that AI has created,” Chua said. “Provide the required training to enable employees on how to use AI capabilities appropriately and create a feedback mechanism on AI performance to improve over time.”
According to Moxie Research, 92% of businesses in Singapore are currently running internal AI workshops to shape strategy and drive adoption.
By order of importance, the key benefits are:
Within this context, 70% of AI projects are also fully cross-functional through a unified AI strategy.
“Establish compelling change around the need for AI adoption,” Chua claimed.
“Ensure that senior leaders are actively engaged by embedding AI solutions into business processes effectively. Externally, it’s also crucial to develop approaches to foster trust among customers as with all new technology.”
One more thing…
To summarise, Chua advised technology leaders to “move from thought to action” by taking strategic steps towards “meaningful change”.
“Every deployment encounters different barriers,” Chua accepted. “Recognise those barriers and help your team overcome them. Realise the value of AI and consider the psychological barriers to adoption while also mitigating AI related risks.”
Meanwhile, Das of Liberty Mutual stressed the importance of simplicity.
“Start with the basics,” he shared. “Build successful POCs and as you do that, build a cross-functional team which is re-looking at the business from a data angle.
“Then ask… what data is the business generating to train the models and ensure that it is reevaluated?
“One analogy I use for AI to work is that the business must run like an opera with hundreds of musicians in sync by the conductor. It’s important to be in sync and explain the models – explainable AI is a complex endeavour and will fundamentally change how organisations operate.”
As an influential technology leader in market, Ng of CDL is aware that CIOs are constantly faced with many emerging technologies. This is not new territory for a profession at the sharp end of the innovation curve.
“But value isn’t created by simply using the best technology, rather by its use in solving business problems,” Ng reminded. “Starting with the business problem helps us avoid building ‘white elephants’, when something is technologically elegant but does not provide business value.
“To maximise adoption, CIOs must champion AI and lead from the front. Simply, if it’s indeed transformational to the business, it deserves our attention. And it’s likely the only way to drive sustained change.”
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