A recent survey conducted by IDC titled IDC Asia/Pacific Enterprise Cognitive/AI survey highlights that AI adoption in the region is on the rise. In fact, current AI adoption rates stand at 14% across Southeast Asia as compared to just 8% last year, marking a clear move by companies to embed some form of AI/cognitive intelligence into their operations.
For Malaysia, it was a 32 percentage points jump in planned adoption of AI in two years since 2017. According to the study, Malaysia’s increasing AI focus can be attributed to greater smart cities initiatives and applications in public safety and intelligent transportation. A lot of these initiatives would need more time to unfold and solidify.
However, the country’s adoption rate (8.1%) lags significantly behind Indonesia (24.6%) and Thailand (17.1%). Many Malaysian organisations have concerns on the cost of solutioning and doubts on the quality of model. Compared to North Asian economies, Malaysian organisations showed less enthusiasm in having in-house AI capabilities which can hinder their ability to understand AI solutions to strengthen their business.
Meanwhile, more than 32% of companies in Malaysia prioritised speech and image recognition interfaces to improve customer experience and enhance omni-channel know-your-customer. On that note, Andy Zook, vice president, ASEAN, SAS, said: “AI is becoming more pervasive in Asia and Malaysia is no exception. Organisations in Malaysia are recognising how AI and analytics can help solve complex problems and reveal unique insights, at the scale and speed required for our growing markets.”
“However, to really reap the benefits of AI, Malaysian companies must have a clear vision for their Big Data and AI investment. A key question organisations need to ask is: ‘How does AI enhance my current staff and technology to drive improved business outcomes?’ In the digital economy, AI and analytics are the drivers of organisational success and companies will need a clear path from data to innovation,” he continued.
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On a bigger scale, 52% of respondents cited the discovery of better business insights as the most important adoption driver (moving from third most important in 2017 as comparison). The number hinted towards a maturity in the way the region is harnessing AI to enhance their business. Other top drivers this year are enhanced process automation (51%), and improved productivity (42%).
The top use cases in Southeast Asia include algorithmic market forecasting (17%), and automated asset and infrastructure management (11%).
Chwee Kan Chua, global research director, big data and analytics and cognitive/AI, IDC Asia/Pacific, said: “With its positive impact already visible across banking, manufacturing, healthcare and government, there are clear opportunities for more organisations in Southeast Asia to leverage AI to create differentiating value. We expect investments in AI to continue to rise, as more organisations begin to understand the benefits of embedding AI into their business and how data and analytics can help uncover new insights. Organisations that do not incorporate AI in their business operations will lose out to their AI-enabled peers who will benefit from the greater predictability, efficiency and innovation that advanced analytics can bring.”
Despite the rise in adoption, organisations in the region are trailing behind those in North Asian countries, in terms of making AI a strategic agenda. For example, more than 80% of companies in China and South Korea believe AI capabilities will be critical for organisations’ success and competitiveness in the coming years, compared to less than 40% of companies in Singapore and Malaysia.
When it comes to the barriers of adoption, respondents cited lack of skills and knowledge (23%) and high cost of solutioning (23%) are among the most frequent. Hence, in solidifying their strategy to turn AI into a differentiator for the business, companies find data from sales, commerce and marketing to be the most ready, followed by that from customer service and support operations, and IT, security and risk operations.
For those already embarking on their data-to-insights journey, there are varied challenges across sectors. Organisations in the financial services space face more challenges in data federation and model building, while public sector organisations are hindered by data readiness issues.
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