AI Adoption in Singapore: What to Expect in the Year of the Pig
Author | Charles Ng, Vice President of Enterprise AI, Appier
Much continues to be made of artificial intelligence’s (AI) capabilities, and it has proven its value in business in areas such as cost-cutting and efficiency. Singapore is undoubtedly a hub for AI in Asia Pacific (APAC), particularly in Southeast Asia (SEA), and in 2019 we will continue to see steady progression towards AI adoption in Singapore.
According to a survey conducted by Appier and Forrester in September 2018, 50 percent of businesses in telecom, insurance, finance, IT and retail in Singapore have adopted AI solutions somewhere in the organization. Thirty-one percent of those who have not yet adopted AI plan to do so by mid-2019. Businesses in Singapore are interested in applying AI to improve existing solutions and processes, rather than using AI to drive innovation (which is the case in places such as Taiwan, South Korea and Indonesia).
Business leaders continue to understand that AI is a critical part of transforming and staying competitive, though many will face challenges as they find that AI adoption and implementation is harder than they have been led to believe. Critical research into business processes and problems- and the right AI solutions for each one- will be required for success with AI.
The right talent is also vital for successful AI adoption. The Singapore government will continue to encourage people to study computer science and will work with the technology industry to train both current and future employees on AI trends and techniques. The talent pool in Singapore will continue to grow, particularly as it is an attractive place to live and it’s easier to hire people from other countries in Singapore than it is in other parts of Southeast Asia. Geographically, Singapore is a good place for access to multiple regional languages. We can therefore expect natural language processing (NLP) to become a strength in Singapore and further cement Singapore as an AI hub.
Fintech is also a big focus in Singapore thank to its reputation as a banking hub. AI is one of the main trends in fintech (alongside cryptocurrency) and there are several applications of AI including insurance, lending (i.e. credit prediction) and wealth management. The strong finance industry in Singapore and the government’s progressive plan to push AI into all industries means we can expect to see more AI in finance and fintech, which is therefore likely to fuel growth of fintech startups.
AI will also continue to solve more sophisticated problems, particularly as we see advancements in deep learning, the most advanced branch of AI technology. The ability to collect and process huge amounts of data will be vital for maintaining momentum in this area, and ‘data readiness’ will be a key focus for Singapore in 2019. The Appier-Forrester study also found that the biggest problem organizations face across APAC, including Singapore, is gathering, unifying and integrating data. Large organizations will work towards setting up data centers in the region, and Singapore is an ideal location considering factors including the thriving technology industry and a legal system conducive to such initiatives. At the same time, as government continues to drive Singapore towards becoming a Smart Nation, a strong infrastructure is critical. Much of this relies on being able to access and use large amounts of citizen data, upon which key AI applications can be built.
We will also see AI and automation work more closely together. Automation has already proven effective in getting repetitive (and often boring) tasks done quickly, but not always intelligently. For example, automation is useful for sending out mass emails. Supported by AI, content can still be sent broadly, but with more effective targeting when it comes to timing, content, recipients, etc. The application of AI will enhance automation so that tasks can be done fast and at scale, but with more useful outcomes.
All this said, businesses in Singapore will remain somewhat cautious about AI adoption. AI providers must verify their solutions and demonstrate transparency. Consumers everywhere are becoming more vigilant with regards to how brands and services are using their data, and in turn, these businesses need to be assured that any AI tools they employ or data experts they hire will be equally careful and responsible with personal data, while at the same time optimizing it to increase relevance and timeliness for customers.
In 2019 therefore, the biggest impact of AI will not come from large-scale applications such as self-driving cars, but rather from continued advancements and developments that will see AI further grow as an invisible and powerful force in business and in people’s everyday lives.
* This article was originally published on Enterprise Innovation.
WE ARE HERE TO HELP
YOU MIGHT ALSO LIKE
Customer churn prediction can help you see which customers are about to leave your service so you can develop proper strategy to re-engage them before it is too late. This is a vital tool in a business’ arsenal when it comes to customer retention. Wondering what churn prediction is, and how it actually works? Read on, and all will be explained… What Is Churn Prediction? Churn quantifies the number of customers who have left your brand by cancelling their subscription or stopping paying for your services. This is bad news for any business as it costs five times as much to attract a new customer as it does to keep an existing one. A high customer churn rate will hit your company’s finances hard. By leveraging advanced artificial intelligence techniques like machine learning (ML), you will be able to anticipate potential churners who are about to abandon your services. Why Is It Important? The truth is you probably already have more customer data than you know. By leveraging this data, you are able to identify behavior patterns of customers who are likely to churn. This knowledge will enable you to segment those customers and take the appropriate measures
While artificial intelligence (AI) often dominates business and technology headlines these days, the coverage generally centers on the development of the technology. Yet for all the celebration around AI, the main concern for enterprises is still how they can get started with the technology, in order to generate actionable insights from their data. Large technology-oriented organizations tend to build their own in-house data science team, which might not be the best approach for smaller companies with less resource or technical expertise. This is where AI-as-a-Service (AIaaS) could just be your alternative passage to AI-powered business. In-House Data Science Team Is Not for Everyone Although data science is one of the most in-demand professions worldwide, companies are facing a massive talent gap: There are more positions to fill than there are qualified data scientists available to fill them. Quality data scientists require a wide range of skills, which sometimes could vary depending on the nature of data to be analyzed and the scale and scope of work. Apart from finding the right talent with the right expertise, recruiting them usually involves high salary, additional benefits, perks and a host of other incentives, as you might be competing with some of
Putting the customer at “the heart of everything we do” has long been a stated aim for businesses. Now thanks to the revolution of artificial intelligence (AI), many industries are finally making it happen, and the financial services sector might be one of them that are doing the most to harness tools like machine learning and data analytics. Get Personal The financial services industry has been hit by the same digital trends disrupting other sectors, such as declining foot traffic to retail banks as customers shift to using apps and secure sites on their mobile devices. This has opened the door to new digital entrants that have seized market share from the incumbents. The shift has highlighted the importance of using technologies such as AI that enable financial institutions to place customers at the center. “We can really transform the customer experience with better use of technology,” says Harish Agarwal, Head of Marketing at Prudential Assurance Company Singapore. “We have been on this transformation journey in the past year, seeing the increased use of AI methods such as machine learning in delivering better end-to-end experience to our customers.” From analytics to personalization, customer service and real-time insights, the insurer