A CIO’s guide to AI: Australian artificial intelligence suffering from arrested development

In part 1 of this guide, we provide a view of the AI landscape in Australia. Where is AI being deployed? Expectations versus realities? Are adoption rates falling behind the rest of the world? What is holding AI back in this country?

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Australia is lagging other developed economies in the deployment of artificial intelligence technologies thanks to a perfect storm of lower confidence levels, skill shortages and higher levels of general anxiety around ethical, legal and security challenges. 

“The first hurdle we’re dealing with is the topic of AI fluency,” Deloitte Australia’s national analytics lead, Alan Marshall told CIO Australia. 

While there are plenty of examples of successful AI deployments in Australia, few organisations seem to have a firm grasp of what AI can do and what it can’t, and therefore how to intelligently plan for and deploy the technology, and measure the outcomes. 

“We need businesses to develop an intuitive understanding of what AI is good at,” Marshall said. “But in my experience that’s not happening on the ground with organisations.”

It’s a sentiment reflected in Deloitte’s report ‘How countries are pursuing an AI advantage’, published last year. It asked early adopters of AI in the US, UK, China, Germany, France, Canada, Australia and New Zealand about their experiences deploying the technology.

The upshot was that Australian organisations have less confidence about AI’s potential to develop real competitive advantage, while they are also more worried about the technology’s downsides, especially when compared with China.

For instance, Deloitte found that early adopters of AI in Australia were less ambitious about the potential impact of AI on their businesses, viewing it more as a means to ‘catch-up/keep on par’, rather than ‘widen lead/leapfrog ahead’. Deloitte reports only 22 per cent of Australian companies in the second group, compared with 55 per cent for China, 47 percent for Germany, 44 and 37 per cent respectively for the UK and US. Overall Australia ranked 7th on this scale, with Canada and France also ahead. 

Further, 41 per cent of Australian respondents said their organisation either has no real AI strategy or only disparate departmental strategies, compared with the global average of 30 per cent.

This is despite the fact 79 per cent of Australian organisations surveyed by Deloitte reported AI will be “very” or “critically” important to their business within two years.

Although the Deloitte report appears to score Australia quite low on key indicators compared with other countries, an article by Gartner last year suggested low levels of AI maturity internationally.

One of the analyst’s ‘strategic planning assumptions’ is that throughout 2021, “75 per cent of AI projects will remain at the prototype level as AI experts and organisational functions cannot engage in a productive dialogue.” 

Professor Michael Blumenstein, associate Dean with the University of Technology’s faculty of engineering and IT, attributes this apparent arrested development to vendors over-promising and under-delivering, especially when it comes to off-the-shelf AI products. 

Natural language processing is one area he feels warrants a little more scepticism, despite its being highlighted by Gartner’s recent AI hype cycle report as being the most important and advanced area of AI currently.

Blumenstein said this is a big area needing further research and fine-tuning.

“There are apps out there that work and ones that don’t. I don’t want to disparage international companies that make claims that aren't there, [but] some are making very big claims around what their technology can do. The reality is there’s a lot of fine-tuning in order to translate into results. There are no off-the-shelf solutions,” he said.

But while CIOs in some organisations are finding it difficult to convince the board and bean-counters to stump up for AI projects, others are finding themselves in the opposite position, coming under increasing pressure to deploy AI.

“Boards are now are putting pressure on CIOs and other members of the executive,” Blumenstein said.

The latter scenario is playing out especially in industries where huge data sets are being generated, and where there is a growing acceptance that making better use of them will breed efficiencies and competitive advantage.

The majority of projects have been in the financial services, healthcare, agricultural and energy/resources industries, roughly in line with the three pillars identified in Data61’s AI Roadmap report, published last year:

-   Natural resources and environment

-   Health, ageing and disability

-   Cities, towns and infrastructure. 

The roadmap highlights a number of promising AI initiatives, including the University of Queensland’s Agbot II, which uses computer vision and machine learning to classify weeds and determine the best way to eliminate them, potentially saving Australia’s farm sector $1.3 billion a year.

Or there’s Data 61’s ‘Spark’ system using machine learning to map fuel loads, terrain, climate and fire fronts to help minimise risks and improve emergency response.

The University of New South Wales recently revealed details of a project applying machine learning to ‘second guess’ the intent of hospital patients unable to communicate, for instance basic things like ‘sit up’ or ‘move left arm’, that would be sent to devices such as computerised wheelchairs.

The Australian public sector has also begun to look at AI. For example, Queensland’s Treasury became the first public sector agency in the world to deploy SAP’s Leonardo machine learning technology, which is being used to assess around 200 million tax payer records in the state to better understand who would and wouldn’t pay and why. It has reduced land tax debts by five percent.

And in something of a reubuke to Blumenstein's skepticism about natural language AI, several Australian organisations are reporting encouraging results with the technology, including Suncorp Bank which has been using voice capabilities in IBM's Watson to analyse customer sentiment during call centre interractions and develop better CX. 

Next up: Days of miracle and wonder 

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