To learn why Australia is a laggard in AI innovation, you need to first understand the prominent use cases that drive the Australian AI experience. For many Australian enterprises, the principal use cases are robotic process automation (RPA) and asset optimization, with many relying on the cloud to drive these projects.
“Australia has some unique characteristics around AI adoption compared to the rest of the Asia Pacific. The first is that Australia has been extremely focused on optimizing their processes, that is optimizing the way that they do business,” said Jeff Olson, associate vice president for projects, AI, and analytics practice in Cognizant’s digital business and technology business.
Olson was the co-author of the Cognizant whitepaper “Getting Ahead With AI: How APAC Businesses Are Replicating Success,” whose data was used to develop this perspective on Australia
The focus in Australia makes many of the AI initiatives inward-looking. In comparison, enterprises in other Asia Pacific countries were more outward-looking, often focusing on customer-driven use cases.
Olson sees this as a tussle between cost containment vs. revenue optimization. Australia is in the former categories.
It may be tempting to explain away Australia’s cloud-driven RPA focus for cost containment as a labor cost issue. But Olson cautioned that this was an oversimplification.
“It’s not really labor costs, but the cost of labor access. Particularly last year, access to the sort of talent that can drive AI adoption and innovation has become more challenging for every Australian enterprise,” said Olson.
Many Australian enterprises are also looking to free their labor from mundane and repetitive tasks and participate in the decision-making process — a powerful argument for RPA adoption. With hiring freezes over the past year, this approach to workforce optimization makes sense.
Another dimension to the cost containment angle is Australia’s broad adoption of IoT across industries. Sensors and intelligent devices create a treasure trove of information, especially in construction, manufacturing, healthcare, mining, and agriculture/fishery/forestry industries. Government and energy industries are also adopting smart metering and sensors for their various smart-city projects.
Live streaming IoT data can make Australian enterprises more competitive and agile in the global and regional market arenas. It improves operational visibility into the daily operations for streamlining costs while enabling enterprises to adopt concepts like predictive maintenance to reduce outages and associated operating costs.
“Australia has been, in a few industries, a strong leader in the IoT space, using machine-based information to improve maintenance and machine performance,” said Olson.
So, it comes as no surprise that many AI projects are focused on unlocking the information potential within IoT data.
Making the jump to revenue optimization
While many AI initiatives have been inward (optimizing business processes, streamlining workforce productivity, etc.), Olson felt that it’s time for Australian enterprises to start looking outward.
It is also where Australian enterprises are behind. Based on the study that supported the Cognizant whitepaper, Olson observed that many Asian enterprises are looking to use AI to drive customer experience amid heightened competition.
“For example, large parts of the finance industry in the Asia Pacific, especially in China, are extremely well known for transforming the digital experience. This sort of change will be coming to Australia and become part of the next wave of Australian innovation,” said Olson.
“Australia has been focused on doing digital inside the business. And I think the country is very close to focusing on digital outside the business,” he added.
A big part of looking “outside” is driven by initiatives like open data sharing. Here, Australia has several initiatives going for it.
At this writing, Australia was introducing the Data Availability and Transparency Bill to the parliament. It had also appointed a new National Data Commissioner to modernize government data sharing. For its part, the Australian Banking Association pushed for the sharing of banking data that is driving banking innovation and is now widely cited as a blueprint for data sharing in the financial services industry.
Such initiatives offer the building blocks for AI-driven revenue optimization. After all, all AI algorithms ingest large amounts of data to learn, and opening up new data stores will only get better.
However, it does not mean Australian enterprises will make the shift. Firstly, Olson observed that there is no clear correlation to open data sharing and AI adoption rates. “It's an interesting topic. But I’m still not quite sure exactly how it'll change the [AI] agenda. But it is definitely, building momentum. And that's not just in banking, but in every industry, the openness of data is becoming more important,” he said.
Open data sharing also introduces new challenges that Australian enterprises need to overcome, especially data privacy and ethics. Here, many companies are taking a cautious step forward.
“AI introduces a lot of jurisdictional, regulatory issues around privacy issues. There are also concerns about who accesses the data and ethics around data usage in an open environment. So these are some of the coming challenges for Australia,” said Olson.
The change will come, but will it be too late?
Olson described the country as being at the “trailing edge of the digital transformation wave.” A significant reason is because of the few examples available that show wholescale digital change. “I think that will soon change; the pressure to change is building within Australia,” said Olson.
But will it be too late? It remains to be seen as “AI-led transformation of business models are slower here than elsewhere in the region,” Olson noted. “It does not mean AI is not used, but it's largely used to support the existing business models,” Olson concluded.
To shift to revenue optimization, Australian enterprises need the corporate grit to change their business models or create new ones.
Winston Thomas is the editor-in-chief of CDOTrends, DigitalWorkforceTrends, and DataOpsTrends. He is always curious about all things digital, including new digital business models, the widening impact of AI/ML, unproven singularity theories, proven data science success stories, lurking cybersecurity dangers, and reimagining the digital experience. You can reach him at [email protected].
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