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APAC to Invest Billions in AI Systems

APAC to Invest Billions in AI Systems

The Asia-Pacific (APAC) region’s spending on AI-based systems and technology is expected to reach US$5.5 billion in 2019, according to a new market report.

The year-on-year increase of almost 80% in AI investment, as forecasted by the latest Worldwide Semiannual Artificial Intelligence Systems Spending Guide, is motivated by the rising number of projects that will utilize AI software capabilities.

In addition to this, the International Data Corporation (IDC) expects spending to rise to $15 billion by 2022.

David Moosbrugger, Kuenz, discusses how automation and humans can work together in a recent Port Technology technical paper

Swati Chaturvedi, Senior Market Analyst at IDC Asia/Pacific, said: “Artificial Intelligence is changing the world as we speak. In fact, Asia/Pacific is quickly driving the growth in adoption of artificial intelligence because of its fertile & nascent digital ecosystem.

“Countries are developing economically with the help of technological advancements, increasing the talent pool of millennials and growing number of tech-savvy businesses, to stay in this competitive market.”

A breakdown of the forecast reveals that 70% of this investment in AI will be led by retail industry, including supply chain and logistics projects based on automation.

 

 

In terms of AI uses cases, intelligent process automation is expected to receive more than $350 million in funding.

Jessie Cai, Senior Research Manager at Cognitive Computing/Artificial Intelligence, commented on the hurdles that must be overcome with the technology: “From an application development and deployment perspective, AI still faces many challenges.

“Having great dependencies with multi-tiered technology stacks and many different skill sets, successful implementations require organizations to beef up their capabilities in different dimensions including data, people, process, and infrastructure.

“They are recommended to consider cloud-native infrastructure, review data readiness and practice data-driven decision making.”

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