Great potential to grow S'pore's adoption of AI

AT THE recent 2019 World Artificial Intelligence (AI) Conference, Tesla chief executive officer Elon Mask and former Alibaba group executive chairman Jack Ma went head-to-head in addressing the threats and opportunities of AI.

While they may disagree on how AI may develop in the future, it is clear that it has made a positive impact on many aspects of our lives. AI is able to harness data from various sources to fuel new and innovative applications that are readily available. As a result, it is getting democratised across industries and has become less of a specialty of deep-tech organisations that focus on scientific advances and technology engineering innovation.

The International Data Corporation (IDC) report says that world spending on AI systems will reach US$79.2 billion in 2022, more than double the US$35.8 billion forecast for this year. AI adoption rate in Asean has also almost doubled from 2018.

We see the trends in AI impacting Singapore too, as evident in the rise of "unicorns" such as Grab, promising startups such as Biofourmis and Taiger, and the digitisation transformations that a number of established companies are undertaking (more on this later).

We can therefore expect a steady progression of AI adoption in Singapore and a consequential rapid growth in economic value for the national economy. However, despite the excitement and, indeed, several individual success stories of applied AI, there are a number of hurdles that should be overcome before we can achieve true scalability of AI adoption across the Singapore economy.

CHALLENGES AND OPPORTUNITIES

The first challenge is to adopt a capability and value-centric mindset instead of a technology-centric mindset in AI adoption. This would mean identifying the right issues and opportunities first, and working backwards to find suitable AI solutions.

Technology is just one part of what should be a holistic strategy for AI-driven value creation. The other key factors, such as people and processes that AI will impact, should also be considered.

The second challenge is in creating the right policies and governance for AI adoption within organisations. This is essential since introducing AI could significantly change a business' operations; organisations need to assess if they have the structure, skills and management framework that can support the change.

Since AI is a relatively novel and not well understood concept for most business functions, it is essential to create an open dialogue between business experts and AI specialists. This is so that business functions can understand how AI can help unlock new value through improving their current operations, processes and services.

Such partnerships should be based on a common organisational vision on AI and on shared outcomes. Securing the leadership and management teams' support for such cross-functional working teams is critical for success.

Another way is by having AI-savvy champions within business functions who can connect business objectives with technology solutions. Champions will play a key role in identifying the right business opportunities. They will also be the lead adopters of AI within a business function, thus sowing the seeds of effective change management.

The third hurdle is the limited availability of the right combination of skills and experience to enable a truly scaled adoption of AI. As it gains in popularity, demand for AI specialists will balloon. However, the skills required to deliver AI at scale within a business are not available in abundance. While academic and professional training programmes in AI exist, deploying it as a business capability is still an emerging expertise.

To ensure that the opportunities of AI can be turned into real solutions quickly, efficient skills development frameworks are necessary. These should address both technology and business aspects of AI through relevant, industry-oriented learning and development opportunities.

To do so, businesses should develop career development frameworks jointly with the industry, academia and R&D sectors, creating a sustainable ecosystem for longer-term talent development.

In Singapore, growth in AI adoption is led by the Singapore government, which has identified AI as one of the four core technologies essential to the country's push towards being "digitally ready". Investments in Smart Nation initiatives, the proposed Model AI Governance Framework, the development of AI at tertiary institutions and the launch of AI Singapore in 2017 have not only built local expertise, but also created opportunities for global multinational companies to set up AI centres of expertise in Singapore.

Alibaba and Salesforce each opened their first AI research facilities outside of China and the US respectively, focusing on building AI solutions for ageing societies and training postgraduate students in various fields of AI.

Tapping into the supportive ecosystem, Sembcorp has set up a data-science function in Singapore as an integral part of its digital transformation strategy. It has built an internal AI team with specialist skills in technology and business-domain experience. Using the latest advancements in AI, the team is partnering with the company's global businesses in developing solutions to optimise operational processes and improve business performance, in turn, creating positive transformation.

While Singapore is positioning itself to be an AI leader in the region, more can be done to achieve its full potential.

Although some industries and a handful of companies have started to benefit from AI, making it easily accessible to all industries is essential. This can be addressed by reducing the current "class imbalance" in AI adoption maturity across organisations.

One way to achieve this is by creating collaborative industry-focused programmes for AI competency development. These should be designed and managed by experts with relevant industry knowledge and by AI professionals with experience of field-ready solutions.

These programmes should create productive and win-win outcomes to incentivise all participating entities. This is often a challenge in collaboration involving businesses and academia, since the commercial interests of a business and academic pursuits of a higher-learning institute are not always aligned.

SUSTAINED VALUE CREATION

Furthermore, business entities engaged in AI adoption programmes need to define plans for not only creating initial success, but also effectively retaining and growing the capability for sustained value creation.

Internal staff upskilling through professional development programmes aimed at plugging specific and strategic skills gaps should be key parts of the plan.

This can be facilitated through the right governance of these programmes so that they demonstrate value quickly and help businesses articulate long-term value returns from the investments in building AI capability.

Existing collaboration programmes such as those offered by AI Singapore have started to address some of these, but more can be done.

The policies of the Singapore government have created a solid foundation for companies that are in the early days of their AI journey to start building more impactful portfolios, but more active participation from Singapore firms in scaling up AI adoption will create further dialogue among ecosystem partners. This, in turn, will further develop the current collaboration frameworks through well-planned, results-oriented and scalable initiatives.

If implemented correctly, there is great potential for Singapore-based businesses and the AI technical community for partnering and creating sustainable value creation through truly scaled adoption of AI.