Home
You’re currently viewing our content for All Regions.
You can switch to a different region:
April 7, 2024
In technology, talk is cheap. Enthusiasm often supersedes execution as businesses clamber on-board the latest marketing bandwagon, kick-starting a hype cycle of emotions and expectations.
Today, artificial intelligence (AI) is the focal point of such enthusiasm in Singapore.
But the true test of potential lies in side-stepping the rhetoric to assess practical applications and real-world implementations.
In this dynamic and rapidly evolving field, an authoritative and balanced approach is required to consider how organisations can harness the benefits of AI technology.
“Enterprises are poised to take AI from ideation to scale,” observed Sumash Singh, Senior Director of ISVs across Asia Pacific and Japan (APJ) at Dell Technologies.
“Enterprise AI is simply the application of AI technology to a company’s most impactful processes in its most important areas to improve the productivity of the organisation.”
According to Singh, such an approach requires businesses to:
“That way, businesses can leverage that investment to solve all future AI problems,” Singh added.
Through an editorial-led initiative by Moxie Insights, Dell and Intel, the most influential technology executives in Singapore gathered to explore the landscape of AI adoption in the context of notable initiatives, challenges and opportunities.
Assessing AI sentiment
In 2025, AI will rise to even greater prominence in Singapore as businesses position for full-scale deployments – triggered by plans to either transform, innovate or modernise. That’s according to findings from AI in Action: Singapore Edition – Moxie Research commissioned by Dell and Intel.
Based on the data, 90% of organisations will allocate additional funding and budget to AI projects during the next 6-12 months.
“The top drivers for AI adoption in Singapore are to improve employee productivity and accelerate new product introduction,” detailed George Chacko, Head of Southeast Asia and Australia and New Zealand (A/NZ) at Intel.
“This is in addition to a desire to reduce costs, improve operations efficiency, improve risk management and generate new revenue.”
Underpinning this will be executive-level decisions setting business strategy, assessing technology stack capabilities and identifying impactful solutions and use cases.
Within this timeframe, 80% of companies are prioritising the development of an AI strategy and roadmap by building a cross-functional team (76%) and setting clear AI business objectives (79%).
“However, regarding actual AI execution in Singapore, employees and individuals are adopting it at a much higher rate than businesses,” noted Tony Do, Founder and CEO of Palexy.
“This is largely driven largely by the concentration of high-skilled and semi-skilled workers in Singapore labor, and the discretion of Singaporean businesses.”
As the leader of a software-as-a-service (SaaS) vendor selling solutions primarily in the sectors of retail and food and beverages (F&B), Do outlined that AI execution is “indeed happening but quite slowly”.
“While the pace does not lag too far behind other Southeast Asian nations, it falls below what you’d expect from Singapore’s wealth, technological advancement and modern image,” Do clarified.
Common consensus now exists across the market that businesses recognise AI as a strategic priority, as outlined by Ben Stein – CEO of Staple. But such recognition comes with a caveat.
“The enterprise acknowledges the potential of AI to drive efficiency, boost productivity, reduce costs and enhance decision making,” he detailed.
“However, AI implementation and execution is often delayed by several challenges, and many organisations struggle to move beyond exploration due to technical debt, data silos and a lack of internal expertise.”
Stein said successful AI adoption requires “clean, structured data and seamless integration” with existing systems but these common challenges are often underestimated.
Building on this, Julien Lauret – CEO of Openseal – accepted that while business leaders in Singapore are making AI a priority, executives remain “bogged down in decision paralysis”. Notably in the area of large language models (LLMs).
“LLMs are a general purpose technology and they can do pretty much anything,” Lauret explained. “Therefore, most organisations end up stuck figuring out where to actually use them.”
In those scenarios, Lauret said all businesses default to the same basic use cases:
“We haven’t seen many companies actually trying to transform their core operations with AI,” Lauret added.
Mirroring Lauret’s observations, Singh of Dell assessed that while AI has a “tremendous amount” of hype, the focus should now move beyond basic efficiency and productivity. In other words, how can businesses unlock the millions of dollars of impact that AI will make?
“To execute prioritised projects, enterprises today have multiple off-the-shelf tools from which to choose,” Singh shared.
When assessing AI sentiment for buy vs. build in Singapore, Singh summarised that the preferred path for organisations is to “buy and implement AI tools in private infrastructure”.
“Businesses can also buy tools that accelerate data modernisation – such as data meshes – and with the Dell AI Factory advancements over the past year, the infrastructure is now simple to adopt and implement,” Singh continued.
“2025 is the year where enterprises will be hyper-focused on building and buying the platforms and tools they need to become the most competitive in their industry.
“While GenAI tools are still maturing, organisations who don’t figure out the right AI strategy and architecture will be at a disadvantage. 2025 is a year when it becomes easier to know what to do and how to do it when adopting AI in the enterprise space.”
Overcoming AI challenges
According to Moxie Research, the top challenges organisations in Singapore face when
deploying and managing AI projects are:
While the issue of cost is unsurprising given ongoing economic, societal and geopolitical factors, escalating expenses are causing companies to reassess IT environments in the pursuit of efficiencies.
Within this context, Bell Beh – Co-Founder and CEO of BAE, formerly BuzzAR – described Singapore as a “pilot level” market in terms of AI deployment maturity.
“We have to treat AI as a once in a lifetime opportunity,” Beh added.
According to Beh, key barriers to AI implementation centre around over-regulation, pilot purgatory and a risk-first mindset in Singapore – this is notable in sectors such as fintech and tourism where regulatory frameworks are still evolving.
“AI regulations globally are tightening which involves data privacy, fraud detection and compliance issues,” Beh explained. “Governments want to control AI risks before enabling full-scale adoption, making execution slow.”
On the “next big hurdle” of pilot purgatory, Beh said enterprise organisations currently test AI in “small and isolated environments” but rarely integrate it into core operations.
“Businesses struggle to define measurable ROI early which leads to abandoned pilots,” Beh expanded.
Unlike in the SaaS space, Beh said AI projects require “deep infrastructure and behavioural shifts”, which can take time to show returns.
This is supported by Moxie Research, which highlights that data strategy and infrastructure are viewed as the most important pillars underpinning AI strategies in Singapore – as noted by 75% of companies surveyed.
“Many corporates see AI as a risk – compliance, security, job displacement etc – before they see its potential for efficiency, revenue and new business models,” Beh added.
“Instead of betting on long-term value creation, decision-makers optimise for short-term risk mitigation, slowing AI adoption.”
Stein of Staple also acknowledged the importance of data in driving informed and inspired AI decisions in Singapore.
“Poor quality data creates ‘garbage in, garbage out’ challenges,” he referenced. “This can also extend to low data availability for training in certain use cases. We also see challenges with technical debt and an inability to integrate with some legacy systems.”
According to Moxie Research, 29% of organisations in Singapore are also hindered by scalability issues when deploying and managing AI projects.
For Chacko of Intel, this roadblock extends beyond technology into geographic regions, on-the-ground resources and access to skills.
“Scaling and deploying AI solutions across Southeast Asia presents unique challenges due to the region’s diverse markets, infrastructure disparities and varying levels digital readiness,” Chacko observed.
“Organisations are adopting a multi-faceted approach focusing on technology, skills, resources and partnerships. The individual countries within Southeast Asia are at different stages on their AI journey based on strategy, human capital, data, processes and technology.”
In summarising the challenge statement facing many businesses in Singapore, Kartikeya Jaiswal – Chief of Staff at Bifrost AI – noted that organisations in the physical AI domain remain “bottle-necked by the cost and time of collecting real-world data”. This barrier is most profound where autonomous systems interact with the real world.
“This creates challenges in training performant computer vision models, and decelerates the pace of development in critical industries like robotics, national security, maritime, earth observation and space exploration,” Jaiswal explained.
“Developers are overcoming this hurdle by opting for photorealistic 3D synthetic data for model training. It enables robust simulations before deployment at a fraction of the cost and time, thereby securing allocation within AI R&D budgets.”
Building an AI stack
With AI sentiment and strategy checked, business focus is now shifting to the technology stack and the importance of modernisation.
For many organisations, this is centred on getting their IT house in order to overcome escalating financial, technical and operational concerns.
“Dell has been innovating at every level of the AI technology stack and across the AI estate to provide businesses in Singapore and the region with a modern data centre of the future,” Singh of Dell said.
“This has been re-plumbed and redefined at every level from the node to the rack to the full data centre – optimised to support a new class of AI workloads.”
As the market demand for AI enabled solutions continues to climb in Singapore, Singh said Dell is “well-positioned” to help businesses capitalise on new market opportunities across four key areas:
According to Moxie Research, 69% of organisations in Singapore are prioritising the roll-out of “modern, scalable data infrastructure optimised for AI” in 2025.
Almost a quarter (23%) are tackling data infrastructure that supports AI in certain areas but remain bound by limitations.
Many important considerations exist regarding the various hardware infrastructure components required to run an effective AI system. This includes high performance computing and high-speed networking, plus scalable, high-capacity and low-latency storage.
At present, 71% of companies are using hybrid infrastructure as the primary technology stack upon which AI systems and solutions run, while 50% of organisations are using on-premises infrastructure to run AI workloads.
“Dell is in the perfect position to help businesses in Singapore and the region navigate AI workloads,” Singh added. “We’ve always believed in providing choice and have been doing it through the various evolutions of emerging technology, including AI, understanding the challenges that come with them.”
For example, the Dell AI Factory is designed to help accelerate AI innovation in organisations of all sizes across Singapore.
“It gives customers access to the industry’s broadest AI portfolio and our open ecosystem of partners to create AI applications tailored to their needs,” Singh said.
Supporting this technology stack is an extensive partner ecosystem that is “skilled, well-equipped and focused” on delivering AI-powered solutions built on the Dell AI Factory architecture.
“By collaborating with the ISVs, we help businesses tackle their most complex challenges,” Singh expanded. “Our commitment to supporting ISVs is pivotal in driving innovation and unlocking new growth opportunities for success.”
In this context, Dell has a deep and long-standing partnership with Intel that extends from the network, compute and storage resources, through to delivering advanced AI solutions.
“The Dell AI Factory will soon include new Dell Generative AI Solutions with Intel,” Chacko added.
“Dell Generative AI Solutions with Intel combine the power of Intel Gaudi 3 AI accelerators with Dell servers, storage, networking, professional services and open-source software to provide jointly engineered, tested and validated solutions that ensure a seamless deployment experience – all on your terms.”
As summarised by Singh, the Dell AI Factory is helping accelerate AI innovation through a portfolio of products, solutions and services optimised for AI workloads.
“It brings together these AI-optimised technologies with an open ecosystem of partners, validated and integrated solutions, expert services and best practices to help customers achieve AI outcomes faster,” Singh added.
“The Dell AI Factory provides targeted and repeatable success for deploying AI by considering both technical and business requirements, and it is flexible and can operate in various locations, including clouds, data centres, workstations, AI PCs for knowledge workers and edge locations.”
Highlighting AI best practice
Adhering to best practices when deploying AI solutions is crucial to ensure reliability and accountability throughout the process, while minimising risks and maximising returns.
According to Do of Palexy, organisations in Singapore can build an effective AI strategy by following three core principles of best practice:
Building on this, Stein of Staple advised businesses to remember that the market conditions are ripe for further AI innovation – backed by strong government support, digital infrastructure and robust regulations.
“Businesses based in Singapore have a considerable head start in this respect,” he guided.
“Remember that AI is a business enabler. In a business setting, AI is an enabler, not an un-targeted experiment. It should deliver real tangible outcomes and solutions for identified problems.”
In this respect, businesses must consider “high-impact use cases” for automation, customer engagement and decision making while also preparing in-house data.
“Clean, structured and well-governed data is crucial for success in any implementation,” Stein added. “Without it many AI solutions will deliver outcomes of limited value, if any.”
For AI to be successful however, the technology has to also inspire. For Beh of BAE, that translates into three key areas:
Based on market experience, Lauret of Openseal recommended that businesses look ahead by 1-3 years instead of just focusing on short-term gains.
“But most importantly, reset expectations,” he cautioned. “Using AI should be as normal as using Excel. People will not be replaced by AI but they might lose out to someone using AI better.
“Therefore, ensure business foundation are solid first – data infrastructure, governance and regulations. That stuff isn’t exciting but it’s what makes everything else possible.”
Moving forward, Singh of Dell outlined that AI capabilities will become “more advanced” as next-generation CPUs, GPUs, NPUs and accelerators handle more complex AI workloads.
“Dell is working closely with all these ISVs – from Microsoft to CrowdStrike to Adobe – to advance greater capabilities and enhance functionalities,” he added. “We expect to see a lot of innovation with both hardware and software.
“Our customers benefit from our advanced AI capabilities earned through years of investment and experience in AI and data as well as our extensive global experience and comprehensive knowledge in deploying AI at scale for our customers.”
Inform your opinion with executive guidance, in-depth analysis and business commentary.
