A CIO’s guide to AI: How can Australian organisations make the technology work for them?

In part 2 of the CIO’s guide to AI, we discuss the practical steps and ideal mindset organisations need to adopt for successful deployments. Think big, but act small while being both pragmatic and experimental.

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One of the reasons for the general AI impasse in Australia – and to a lesser extent amongst our developed peers such as the US, UK, Germany and China – is that projects still tend to be cloistered within IT departments.

This creates a number of problems including lack of executive buy-in, isolation from the core business and its objectives, too narrow a set of perspectives across different departments and disciplines in the organisation, and a heightened fear of failure that comes from lack of shared ownership.

All of this contributes to what experts believe is the greatest obstacle to AI deployment: wrong perceptions of what the technology can and can’t do.

Generally, organisations have unrealistic expectations that the technology will have some sort of magical impact on operations. This can result in development of projects that are too large and ambitious in scope, amplifying the risk of failure and disappointment.

Other organisations may baulk at AI, seeing it as too expensive and/or complicated to consider. Then there’s the perceived ethical and legal risks, which tend to receive intense media scrutiny and coverage when they’re exposed; more than is warranted according to some.

“Don’t talk to someone who thinks everything looks like a nail,” warned Professor Michael Blumenstein, associate Dean with the University of Technology’s faculty of engineering and IT. “AI to some people just looks like this giant hammer: It’s not like that.”

According to Gartner VP and distinguished analyst, Whit Andrews, AI should be viewed as “augmented intelligence” with humans “in-the-loop”. And key to deploying it successfully he said is having a pragmatic, yet experimental approach starting with three core pillars.

The first is data collection, or system training.

One of the first major revelations for corporates is that for all their perceived cleverness, AI systems are generally only as good and effective as the data that are fed into them for training purposes. Many a project has been known to fail for neglecting this simple principle.

Recently, however, the pendulum appears to have swung the other way.

“Companies are getting bogged down in data prep,” Andrews said, as they try to manage and input too much information.

whit andrews Gartner

Whit Andrews

It can’t be viewed as an exact science, however, he added with some of the best-run projects and top teams often reporting data issues.

“Organisations successfully using AI are still likely to say they have data problems which tells us everybody does. I’ve never spoken to any organisation in 25 years that was completely satisfied with its search project: There’s always a middle way,” he said.

Secondly, Andrews advised organisations to synthesise data using simulations.

“Create something to perform a simulation within itself to apply outside of itself. Start with a small number of data sets. Do the first project. Do the second project with same data sets.

“And the third with data sets that are similar. Don’t stray too far from your initial investments and take advantage of every step you build. Get started using methods [like] programmatic or logical shifting to probabilistic models,” he said.

He cited the example of a university that started out a project with 200 ‘answers’. Using machine learning it was able to apply so-called semantic vectoring to match questions to these answers, scaling the project up to eventually produce 2,000 answers.

Data vs code

Getting AI right requires a shift in mindset; effectively an acceptance that data is different to code.

“Most technologists are familiar with the work in which humans convert their unstructured data to the logic of code,” Andrews added. “But in AI what we do is train computers to convert reality into immutable substitutes for code.”

Andrews recommended organisations develop a portfolio of four “experimental projects”. One might be high-visibility, the other low visibility, with another perhaps being high-risk and a fourth only low risk. No more than one should be a chatbot.

“Pick carefully and invest in them strongly,” he said.

Next, measure outcomes, and do so in different parts of the company in so far as it’s possible with the same lessons from the data. This is the most likely set of habits to result in success, he said.

The Melbourne Cricket Club (MCC) is running an Azure-based AI solution designed to better predict and manage fan attendance, in the hopes of reducing queuing and streamlining ticketing and sales of food and drink. It’s been working with Microsoft partner Revenite to build a “digital twin” based on Azure Data Warehouse, Stream Analytics and Data Factory.

Business intelligence officer James Aiken told CIO recently that while a big effort was made to collect not only the MCC’s own data, but also data such as AFL league stats and weather info, the system has developed an appetite for more data of better quality, while demanding it be more organised.

“We were finding we were trying models and getting 80-90 per cent accuracy,” he said, noting while this was nothing to sneeze at, there was serious room for improvement.

Deloitte Australia’s national analytics lead, Alan Marshall agreed that getting the data right is a critical first step to building something meaningful.

“It’s not just ‘we’re going to implement some AI’: we’re creating an ecosystem,” Marshall said.

He recommended a three-layered approach to doing this.

The first is the digitisation process creating the initial data ‘footprint’.

It needn’t be complicated nor expensive Marshall insisted. For example, Deloitte recently developed a solution for a Sydney Hospital, which used Amazon’s Alexa to effectively replace the nurse call button.

“We were able to create a data footprint where one didn’t exist; converting speech to text. It was an optimisation opportunity, giving us an understanding of whether we have enough nurses and orderlies,” he said.

The second layer is the data analysis platform to capture, analyse and curate the data lake, such as AWS or Azure.

The third layer is the algorithm itself. This is where the real work of inputting data and training the system comes in.

Another key consideration is whether to build or buy.

“Most organisations will have a buy v build decision point,” Marshall noted. The first option will likely be faster and cheaper, but the trade-off is that you’re paying for someone else’s intellectual property.

“DIY takes longer but you get something that’s truly your IP,” he said.

Next up: Hits and misses

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