Report reveals strong appetite for machine learning in Australian market

Report reveals strong appetite for machine learning in Australian market | Insurance Business Australia

Report reveals strong appetite for machine learning in Australian market

Despite many organisations going digital and becoming interested in machine learning (ML) since the COVID-19 pandemic started, only a few organisations have an ML project in production, according to an Australian survey conducted by tech consultancy DiUS.

DiUS conducted a pulse survey to find out insights into how Australian organisations are adapting to technological changes by using and driving success with ML, which automates simple and repetitive tasks at scale and delivers insights and experiences that were impossible before.

The survey found a strong appetite for ML in the Australian market, with 82% of organisations showing interest. However, only 21% have an ML project in production, and only 69% of organisations with models in production reported sufficient ML capability.

Those struggling to use ML found it difficult to move beyond the proof-of-concept or pilot stage – with picking the right problem, data quality and availability, model accuracy, and application integration often delaying or preventing ML project success, DiUS reported.

Read more: What should risk managers look for in a cyber policy?

Despite the gap in ML skills, 86% of the respondents saw ML as crucial or one of several important technologies in the future. Meanwhile, 49% of those who have not yet started using ML plan to do so in the next 12 to 24 months.

The respondents considered data-related challenges as either the top or second challenges once they started the ML journey – emphasising the significance of data quality, data engineering, and building appropriate data infrastructure and pipelines to enable ML initiatives.

On the bright side, DiUS said organisations can succeed with ML by prioritising it – with 79% of the respondents achieving success with ML having a strategy around focus and investment.

Currently, top ML use cases are internally focused, with the top two business areas being operational efficiency (48%) and business decision-making (46%). Going forward, the respondents said they might shift to both an internal and external focus: operational efficiency (57%) and customer experience (51%).

“A giant fast forward button has been pressed on ML in the market, yet it's hard to point to one application or business area doing disproportionately better than others. Advancements are being made across many fronts with unprecedented speed,” said DiUS co-founder and director Joe Losinno.

“We're seeing increased confidence from our clients across many industries, including mining, health, financial services, manufacturing, and retail to invest in ML. However, this has resulted in more requests for assistance in building ML-powered digital products and getting ML models into production with the level of accuracy needed to deliver the desired business value.

“Success with ML requires a focus on the right problems, taking an experimental approach, and investing continuously from a technology, people, and process perspective. It's something that businesses should be figuring out how to do well.”