Is Singapore ready for Artificial Intelligence?
Information and Communications Technology & Media | 20 Dec 2019
Is Singapore AI ready?
As the 4th industrial revolution progresses, businesses must become high-tech to survive. At the SME Infocomm Conference in Aug 2019, Minister Chan Chun Sing belied the point that while technology may seem like the magic solution for enterprises, you need to 1st know what needs to be solved. It is important to get data fundamentals right before launching AI capabilities to get Singapore AI ready.
According to recent IDC research, Indonesia is leading AI adoption at 24.6%. Thailand comes in 2nd and Singapore ranks 3rd at only 9.9%. Across Asia Pacific, only 41% of companies are using AI. In Singapore, only 24% of sales teams have put AI systems into action and 37% are waiting to see what benefits AI will bring before investing. This is surprising as Singapore is normally a trailblazer in the region when it comes to embracing new technologies.
A study by Microsoft and IDC Asia/Pacific estimated that AI will nearly double the rate of innovation and employee productivity improvements in Singapore by 2021. The majority of people in Singapore believe that AI will either help to do their existing jobs better or reduce repetitive tasks. The adoption of AI is pervasive with promise for powerful capabilities, from manufacturing to healthcare, financial services and the public sector. This trend is not going to slow down soon, with businesses, startups and the government taking serious steps to reposition themselves as leaders in the AI market (link).
In recent years, Singapore’s education system has increasingly emphasised fields such as mathematics, statistics, computer science and information technology. Business schools have also woven data science and AI elements into its curriculum. This positive trend will expand our talent pool in the field of data science. Beyond schools, some including financial institutions have also started offering training programmes and development opportunities to support their in-house data scientists.
However, data science is an applied subject. Time will be needed to build the experience of the influx of data science graduates in the workforce in applying machine learning and AI techniques across the public and private sectors in meaningful ways. The sooner we are able to implement, iterate and validate data science models in business, the faster we can make data science and AI more useful and relevant.
AI Adoption: Where are we now?
It’s no surprise Singapore’s AI efforts stretch back years. The government partnered with Microsoft in 2016 to explore chatbot integration for public services and invested $2.82b in digital infrastructure as part of its Smart Nation programme. Last year, AI Singapore (AISG) made multiple steps towards increasing usage; including launching two initiatives to increase national capability: AI for Everyone (AI4E) and AI for Industry (AI4I). This year, the government released its Model Framework for AI governance; the first ethical AI guidelines in Asia.
Even with all this development, Singapore is not leading with implementation. The eagerness to embrace AI isn’t translating into everyday use, and the likely cause is uncertainty. For example, the Model Framework is limited on finer details of how to keep intelligent machines under control, leaving many companies unsure how to proceed safely.
Next steps: Powering the AI evolution
Asia Pacific is rapidly becoming a hotbed of AI activity; with spending on AI systems due to hit almost $7.5b (US$5.5b) in 2019 – and China set to lead the way. If Singapore is to maintain its market position, companies must pick up the pace of AI execution, starting with developing a robust preparation strategy.
While AI technology is ready for adoption, regulations and policies will need to catch up with the rapidly changing technology landscape. The government can play a pivotal role by having open dialogue on a range of machine learning and AI topics that concerns citizens and industry players and how AI can support the business. This will ease some of the uncertainty over the adoption of AI. As a region, governments will need to provide the right regulatory environment for businesses to operate and stay competitive, while protecting citizens’ personal data privacy. Organisations will need to review information security policies to find the right balance between enabling business to implement AI initiatives and the safeguarding of customers’ data.
A McKinsey Global Institute study showed that while there is advantage gained by those adopting AI, the ecosystem for doing this is still immature in Southeast Asia compared to the US and China, with several sectors lagging. Half of the current AI use cases in Southeast Asia are in high tech and telecom, and financial services.
While the new technology is catching on, banks and startups in Singapore and the region will have to develop new skills and innovate to benefit from the opportunity. They will need to accelerate digitisation of customer interactions because AI tools must be fed enormous amounts of data, especially as consumers are by and large digitally savvy, particularly in Singapore, Malaysia, and Thailand, which are well-connected online, as well as Indonesia where smartphone usage is rising fast.
The biggest hurdle companies face is in finding the right talent. Data scientists, people who design, deploy, and train AI engines, are in short supply even in Silicon Valley. The shortage is far more acute in Southeast Asia.
One way is to adopt a build-operate-transfer model which is to allow experts from specialist firms to come in and be embedded within project teams of an organization. They will gain operational understanding by collaborating with employees from the host and the company employees would gain new skills to be able to manage scaling and improvements after the initial deployment.
Singapore is certainly keen to join the AI arena and reap the rewards of enhanced automation and efficiency. However, we see to remain in the initiation phase for now with businesses waiting to see how disruptors fare before taking the leap. By preparing and focusing on the vital pillars for AI success (1. following official and ethical rules, 2. keeping watch on machine bias, and 3. protecting the access cycle), businesses will remain at the forefront of tech innovation. By appreciating and following guidelines with their own data security measures, companies can ignite their journey to an AI-centric future.