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Interest in artificial intelligence (AI) has been brewing across the Asia-Pacific (APAC) region, with just 7% of CIOs in the region having no interest in the technology, according to a Gartner study released earlier this year.
This trend was evident during a roundtable discussion held at SAP Asia-Pacific in June 2019, where some members of Computer Weekly’s APAC CIO advisory panel shared their approach to AI in their organisations.
At MyRepublic, a regional fibre broadband and mobile service provider that recently formed a data science team, AI has been key to understanding its customers better, said group CIO and chief operating officer Eugene Yeo.
“We’ve focused a lot on gathering customer data, as well as external data, to understand our customer segments, including their values, interests and connections with other people over time,” he said. “This will enable us to create better product bundles catered to their needs.”
Meanwhile, German software giant SAP has been using the SAP Cloud Platform and AI capabilities from its SAP Leonardo portfolio to power its business and IT processes. Today, every business problem that SAP is trying to solve is underpinned by AI thinking, said Manik Narayan Saha, CIO of SAP Asia-Pacific.
SAP’s AI efforts were spearheaded several years ago by a centre of excellence that led the way in building AI models not just for analytics, but also to automate transactions, effectively moving from rule-based to pattern-based automation, said Saha.
In Singapore, SAP’s headquarters for the region, the company has formed an AI team of experienced data scientists. “We’re starting to reach the hockey stick moment where we’ve built enough organisational competencies that can be used across the board,” said Saha.
For example, a digital assistant is being developed to help SAP’s sales teams keep track of their accounts and opportunities in their sales pipelines using natural language processing. “The next step would be to use external data to provide our sales teams with news and other information about their customers,” said Saha.
While many organisations grapple with data issues in their AI initiatives, this was not the case for SAP and MyRepublic. Saha said the lack of compute power was a bigger bottleneck, particularly when running highly specialised algorithms and models that could take days to generate an outcome.
It was the same case for MyRepublic, which had to prioritise the compute capacities it needed to drive sales and marketing campaigns powered by AI models, for example. Yeo said that to address the issue, MyRepublic is using cloud-based serverless computing services to ease the compute crunch.
Nigel Lim, a regional IT manager at a Japanese multinational conglomerate, said his firm is still in the early stages of implementing AI, as it is running disparate systems across different businesses that make it hard to pull data together.
Manik Narayan Saha, SAP
While the challenge is being addressed, Lim said he is exploring the use of textual analysis to mine helpdesk information in a bid to improve the productivity of support staff. “If it goes well, we can clean up the data and release it for use by users and chatbots,” he added.
Yeo’s thinking around chatbots differs. Instead of powering chatbots with a knowledge base, his team is working with Google to build a chatbot powered by user stories.
“It can help users troubleshoot network issues by accessing our network infrastructure to see if there is a fibre cut, or if there is a circuit problem in our GPON [Gigabit Passive Optical Networks] equipment, all the way to looking at your accounts to see if you’ve been suspended because you haven’t paid your bills,” said Yeo.
“If there is no problem with all of that, it will assist you, step by step, to troubleshoot your issues. The plan is to drive all support calls to live chat. People increasingly want digital ways of interaction – no one likes to call and we can see that in our user reviews in Australia.”
At SAP, which receives a million IT support requests from employees each year, 10-15% of those requests are now handled by chatbots, said Saha, who emphasised the importance of achieving scale in AI deployments.
“To get full value from AI, you need to deploy it at scale, simply because the upfront investment is significant,” he said. “If you are only doing it in one subsidiary or country, it is unlikely that you will get a good return on investment.”
That said, Saha warned that AI projects do not always deliver guaranteed results based on a certain metric and within a specific period of time.
Read more about AI in APAC
- The Singapore government has released an AI governance framework to help businesses tackle the ethical and governance challenges arising from the growing use of AI across industries.
- Microsoft has expanded AI capabilities of its Xiaoice chatbot, which is now designing images and patterns for China’s textile industry.
- As the enthusiasm for AI gathers pace in Australia, the country’s chief scientist has sounded a note of caution and called for more regulation of AI.
- Choose the parts of AI to deploy to demonstrate return on investment and differentiate your brand, says Volkswagen Australia’s chief customer officer.
“But one thing that’s different between AI and other projects is that the returns from AI will get better as the machine gets smarter and more optimised over time,” he said. “Just look at Amazon’s Alexa, which can now handle exponentially more scenarios than when it was first released.”
The need to understand how AI algorithms achieve specific outcomes or make certain decisions has been a topic of interest in academia and industry for a while now, and Saha said SAP has embarked on explainable AI projects to look into the factors that influence AI decisions.
“One area where this is very useful is medical diagnoses,” he said. “Today, there are AI capabilities that can detect cancer better than human doctors can. But we don’t know what the AI sees in an image that tells you it is likely to be cancer or not.”
MyRepublic’s Yeo also struggled to understand how one of the company’s AI algorithms concluded correctly that a staff member had switched religions by going through three months’ worth of web-browsing history. “We couldn’t tell how the algorithm came up with that conclusion,” he said.