The economics of artificial intelligence are moving aggressively in the direction of consumers of the technology, as enabling technologies become more affordable due to cloud computing.

That’s one of the key messages senior executives attending SAP’s recent Real World Transformation event in Melbourne received from Nigel Watson, JAPAC Regional Leader – Technology Partners at Google, who identified three such developments.

“One of the interesting things about machine learning is that it is not new to our era. It’s been with us for ages. What was missing and what’s been missing for a long time were some of the enabling technologies and capabilities that make that accessible.”

One big change, he said is that business now has cost-effective access to a very large number of CPU cycles. “And machine learning requires a lot of computer power. That used to be very expensive. Now with our technology, you can pull out on your credit card, sign up, and get access to 1,000 or 10,000 CPUs, use them for an hour then turn them off as you like.”

Cloud computing also solved the problem of access to the huge pools of data needed to train algorithms.

Intelligent enterprises effectively use their data assets to achieve their desired outcomes faster – and with less risk.

“What cloud has provided and what the new technology has provided is continuous access to huge amounts of storage for a very low cost. With the advent of the internet, and of broadband networks, it’s now possible to amass enough data to do all the training in the machine learning model.”

Nigel Watson, Head of Cloud Technology Partners, Japan and Asia Pacific, Google Cloud

Then the third trend relates more directly to machine learning itself. “It used to be that two years ago, if you wanted to do anything with machine learning, you had to go out can get a data scientist, who tend to be rare and rather expensive commodities. What’s happened over the last few years is that cloud vendors like Google have greatly democratised access to machine learning.”

By that he meant that companies such as Google and others have automated much of the heavy lifting done by data scientists in the early stage of AI.

Watson also pointed to the emergence of new ecosystems ofstartupss and technology partners, who leverage cloud platforms to build specific capabilities that businesses interested in AI and machine learning can now buy as services, rather than having to build those capabilities themselves.


The basics matter

Andrew Walduck, a C-Suite business advisor, and former EGM innovation and product at Australia Post cautioned attendees at the Real World Transformation event that the basics still matter, especially around data.

“Just because you’ve got the data, doesn’t mean that people know what’s actually in it. Can they for instance identify that the top 20 per cent of your customers actually contribute the most value or is there a better way of personalising the services for them, because you need that?”

According to Walduck, there is a fundamental lack of people demanding insight. “The technology is sufficient mature now, as you just heard, but it can be deployed in so many different instances.”

About The Author

Andrew Birmingham is the director of the Which-50 Digital Intelligence Unit of which SAP is a member. Members provide their insights and expertise for the benefits of our readers. Membership fees apply.


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