Scales Momentum of Almost Three-Times Team Growth and Numerous Patents Filed

SINGAPORE 鈥� March 19, 2025 鈥� 麻豆原创 Labs Singapore today announced its collaboration with National University of Singapore (NUS) under the Industrial Postgraduate Programme (IPP). Supported by the Singapore Economic Development Board (EDB), the IPP is designed to enable globally-leading companies to nurture postgraduate talent with critical research skills. Through this program, 麻豆原创 Labs Singapore will hire and complete training for nine researchers in its AI team in Singapore by end-2030, adding to the continued growth of the lab.

Since its establishment in 2022, 麻豆原创 Labs Singapore has not only achieved its commitment to hire 200 AI engineers in Singapore but has also almost tripled its total headcount over the past three years to 420 鈥� hired mostly from local universities and institutions of higher learning, including the NUS and Nanyang Technological University (NTU). 麻豆原创 Labs Singapore today plays a pivotal role in driving innovation, with numerous AI-related patents filed by inventors based locally. New researchers hired through the IPP will also be recruited from NUS, and will work on new research areas, complementing the work done by incumbent team, to accelerate AI adoption in the industry.

The rapid scaling and achievement of its hiring and innovation targets have been pivotal to enable 麻豆原创 Labs Singapore to test, refine, and deploy AI advancements that offer tangible ROI across core 麻豆原创 solutions including HR, finance, and supply chain software.

鈥淥ur vision for 麻豆原创 Labs Singapore was to become a vibrant engine of innovation in Asia, creating cutting-edge solutions for our customers worldwide,鈥� said Manik Saha, Managing Director, 麻豆原创 Labs Singapore & Vietnam. 鈥淭oday, that vision has turned into a reality: not only have we nearly tripled our staff while continuing to nurture future talent, we鈥檝e also pushed the boundaries of what AI can do for enterprises, going beyond experiments to deliver AI solutions that truly make a difference in people鈥檚 day-to-day work.鈥�

Agentic AI: Leading the Next Wave Beyond Generative AI

麻豆原创鈥檚 engineering teams in 麻豆原创 Labs Singapore are already playing a central role in driving Agentic AI, an evolution that moves from basic automation and predictive models to autonomous agents capable of managing entire workflows.

By harnessing Agentic AI, such as , enterprises can turn AI investment into real business outcomes, including:

• End-to-End Process Automation: From procurement requests to invoice management, entire sequences become self-driving.
• Smarter Decision-Making: AI agents collaborating across end-to-end processes to help businesses make better decisions in real time.
• Scalable ROI: With applications, data and processes harmonized, organizations see faster time-to-value compared to traditional AI pilots.

鈥淥ur team at 麻豆原创 Labs Singapore has co-created numerous client features, from one-click, guided, employee development goals creation in 麻豆原创 SuccessFactors, to embedded analytical capabilities in finance. And the next step will be new agentic innovation, demonstrating that AI can transform traditional business challenges into opportunities,鈥� said Saha.

The success of Agentic AI is underpinned by a unified data foundation. The newly launched 麻豆原创 Business Data Cloud brings together large-scale datasets from multiple sources, ensuring that AI agents have the context they need to drive more intelligent automation.

鈥淒ata is the fuel that feeds AI, and we鈥檝e made it our mission to deliver a seamless way to connect disparate data,鈥� said Verena Siow, President and Managing Director, Southeast Asia, 麻豆原创. 鈥淎s 麻豆原创 Business Data Cloud breaks down silos and scales AI capabilities across lines of businesses, regions and industries, 麻豆原创 Labs Singapore serves as an innovation engine to translate AI investments into tangible, measurable ROI for customers so that they can continue to Accelerate to Innovate and achieve their business ambitions.鈥�

The rapid growth at 麻豆原创 Labs Singapore spotlights how Asia, with Singapore at its epicenter, is becoming a magnet for advanced digital technologies.

鈥淐ompanies in Southeast Asia aren鈥檛 just catching up鈥攖hey鈥檙e often leapfrogging into the most sophisticated AI use cases,鈥� added Siow. 鈥淥ur success at 麻豆原创 Labs Singapore is proof that, when you combine visionary public policy with forward-thinking R&D investments, you can fast-track innovation that resonates around the globe. More and more customers in the region, such as Olam Agri, Bank Danamon, and Darussalam Assets, are already incorporating 麻豆原创 Business AI capabilities into their business processes.鈥�

鈥淭he expansion of 麻豆原创 Labs Singapore and the addition of a new R&D team exemplifies how companies can leverage Singapore鈥檚 world-class AI talent to solve complex enterprise challenges,鈥� said Mr. Chan Ih-ming, Executive Vice President, Singapore Economic Development Board. 鈥溌槎乖粹�檚 collaboration with NUS through the Industrial Postgraduate Programme will further strengthen the pipeline of AI talent in Singapore. It is a tangible example of an industry-academia partnership that equips our researchers with deep research capabilities to develop innovative AI solutions that are enterprise-centric.鈥�

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Customers may not see what鈥檚 behind the scenes as what we do is embed AI into the solutions and business processes, but the importance is how AI is propelling end-to-end business processes. The current state of AI is enhancing the capabilities of organisations by adding to the efficiency and of their business model.

In the enterprise, there is one way to organise the AI initiatives and that is into three distinct buckets. First, refers to the ability to analyse thousands of variables to determine patterns which provides decision makers useful insights for agile decision-making that are essential in today鈥檚 highly volatile world. Second, with , it is all about learning to supplement human operators to work more efficiently and with increased productivity. These could include robotic process automation and invoice processing. Finally, rethinking the and leveraging AI/Machine Learning to completely change the incumbent business models is part of AI-driven business process transformations. It is in a budding stage, but we can already see some emerging signs in the digital supply chain, business networks and some industries that are asset intensive.

Overcoming Challenges One Strategy at a Time

Transformation is not without challenges and one could be the lack of a clear and standardized strategy to guide the process. It would be ideal to have a clear definition of a problem statement and an achievable outcome. But an issue is that in most of these cases, there鈥檚 a lot of data and that comes with an uncertainty as to what the problem to be solved is. Of course, there are no fixed solutions to problems. In the long term, the key performance indicator for us is always adoption. When one looks at business processes from an endpoint, it is also easier to measure the productivity and efficiency of each process and once measured, that can provide some numbers to show returns.

With every project, there is a need for a sponsor in the company who recognises in the long term benefits and who is willing to invest in the project even when results may come in the mid-long term. AI projects sometimes don鈥檛 always succeed in a certain timeframe, but in most organisations quick results are usually preferred and switching to the next big idea is likely without a long-term sponsor. With AI, it is a long journey so there is a need to keep chipping away at the idea for weeks or months. An important aspect to remember about AI is that the return on investment gets better over time as the underlying AI models adapt and improve. Therefore, the ability to stay on course is a very important factor in determining the success of AI projects.

A lack of easy data integration with machine learning systems can make it difficult for businesses to switch to integrating AI swiftly. Project costs increase as interoperability decreases, especially with maintenance fees. There can also be an increase in data silos occurring and the likelihood of poor data precision which could cause cascading quality and model accuracy issues along the line. Companies can be comfortable with existing business models and processes which means end-to-end transformation for to harness the full power of new technologies such as AI in order to disrupt the way they do business is not a priority. In fact, AI loves big data. While thinking about an AI strategy, companies need to also think about their overall data strategy 鈥� what should be kept, what should be leveraged, and what data can be archived.

There鈥檚 always been a challenge to find qualified developers because the skill sets are different and are in high demand. For example, developers of AI/Machine Learning need to be highly skilled in mathematics, statistics and data science as well as be competent in development/data architecture to be able to bring projects to life. Contrasting this to 20 years ago, a developer was more likely defined by the programming languages they know.

It鈥檚 a very competitive market and has been the case for the last decade in this space. In Singapore, the government is working with a number of local universities to embed AI, machine learning, and data science in a lot of the courses being run so there鈥檚 a number being pumped into the industry these days which is a great start to overcoming the current shortages.

Where Is This All Leading Towards in the Next Few Years?

These challenges are not independent of each other and are not isolated among different organisations. Implementing AI requires different skill sets and knowledge compared to computer engineers/coders/developers, it also requires an understanding of data science and mathematics. With that in mind, there is a need to recognise that both public and partnerships are important to foster AI skill sets for a

With the collaborations between organisations and local universities鈥� computer science courses such as the National University of Singapore and National Technological University, we are working to ensure that the future workforce is well-equipped with the necessary skillsets through industry upskilling and hiring of STEM talents as the gateway for product engineering in Asia Pacific and Japan.

According to IDC, global spending on AI technology will likely . This shows that it鈥檚 even more important than ever to build a technology strategy, incorporating AI and Machine learning, and leveraging the right software partners who can stay the course in the next three to five years with you.

Manik Saha is managing director of 麻豆原创 Labs Singapore.

The post Overcoming Challenges to Adopting Artificial Intelligence in Organisations appeared first on 麻豆原创 Southeast Asia News Center.

Operationalizing AI 鈥� Why It Remains Challenging?

According to聽, AI will be a defining technology of this decade. Yet for many organisations, the benefits of AI remain elusive. Only 26 per cent of the 405 global executives surveyed by聽聽Analytic Services in December 2020 and who work at organisations that have an active interest in AI, including those that have initiated AI projects, have met most of their AI operationalisation goals within the stipulated timelines. Only five per cent of them completed the projects in full.

One of the most pertinent reasons why few companies have deployed AI with scalability in mind from the onset but聽ended up rolling it out聽as an experimental initiative is due to the lack of visibility on return on investment. Other challenges include accuracy in reporting, lack of accessible quality data and investment continuity in a phased AI project.

Another crucial factor lies in the project objective of AI implementation: is it solving a qualitative issue or improving the business bottom line? At聽Singapore Tech Week 2022, my colleague Manik Saha, Managing Director of 麻豆原创 Labs Singapore, shared these sentiments with other AI practitioners during a panel discussion. The panellists unanimously agreed on the need to have a comprehensive understanding of the mission and purpose of AI and as such, have embedded intelligence into business models with the end goal in mind.

To realise the full potential of AI in an organisation, a 鈥渇ull adoption mindset鈥� needs to be cultivated.聽 In the HBR article, it highlighted that 80 per cent of those who succeeded in fully rolling out their AI applications found it worthwhile at the end of the day.

Overcoming the Challenges聽

Here are four ways enterprises can consider getting AI projects over the hump to benefit operations:

1. Define a clear and standardised AI strategy to guide the process: A clear AI strategy is needed to guide the process in聽understanding the value that can potentially benefit the organisation and outline the steps needed to achieve the goal. A typical AI strategy should clearly state the problem statement, the impact of the solution and the reporting required by the stakeholders.
2. Retire legacy systems and start improving interoperability internally: To redesign an existing business model, retiring legacy systems and improving interoperability across the technology infrastructure must remain a key priority in the endeavour. Only by overtaking the current IT applications with new systems, can businesses develop the right capabilities in using data, models, and software to address the problems detailed in聽the strategy. Project leaders will also be required to fundamentally聽prepare sufficient and quality data聽and start labelling them into useful assets to power the AI model.
3. Data quality, quantity, and labelling 鈥� Another critical element involves hiring and developing specialists in data engineering, data science, data and AI ethics, model security, and machine learning engineering.聽They should work as a team to optimise the complex navigation around the interplay of software and hardware. Before acquiring the right capabilities to operationalise AI, it is also important to set up internal processes for end-to-end and top-down management of the project.
4. Ensure scalability in the project scope 鈥� Finally, as with all projects, the owners need to ensure scalability in the project scope from the onset and work towards achieving the complete purpose. Not realising scalability after the first phase will lead to poor reporting, resulting in difficulty in securing future investments.

The Road Ahead

Operationalising a promise into a reality is what sums up a typical AI deployment. In the current decade, many businesses believe AI is key to their organization鈥檚 future, with聽聽聽of聽the global executives surveyed by HBR stating that successfully deploying AI is critical to achieving their organisation鈥檚 strategic goals. This suggests that some organisational disconnect exists when it comes to conceptualising AI鈥檚 role, especially given how effective operationalising AI is when achieved and how important it is for companies going forward.

Today, we are seeing various industries deploying AI to improve their value chain to stay resilient in the fast-changing macroeconomic uncertainties. At聽麻豆原创 Labs Singapore, we are bringing in engineering talents to realise our vision of becoming the AI hub of the region, with an emphasis on embedded AI that runs directly on devices rather than in a data centre. AI has the potential to scale for huge business impact, and I am optimistic about its endless possibilities to drive innovations to better the way we work and live in the future.

This article was originally published on on December 6, 2022.

Nivedita Salam Mohapatra is Chief Operating Officer for 麻豆原创 Labs Singapore.

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