An Australian survey conducted by technology consultancy DiUS has found there’s a strong appetite for Machine Learning (ML) in the Australian market with 82% of organisations interested in ML, but only 21% have an ML project in production.

ML’s ability to identify patterns in data promises a transformation in how work gets done and who does it to generate real business impact. From automating simple and repetitive tasks at scale through to delivering insights or experiences that were just not possible before, ML is on the mainstream business agenda.

Despite this, the report finds that organisations struggle to move beyond proof-of-concept or pilot stage. Picking the right problem, data quality and availability, model accuracy and application integration are often big blockers, either delaying or preventing ML project success.

Other key findings in the report include:

• ML adoption is going to accelerate. 86% of respondents see ML as critical or one of several important technologies going forward, and 49% of those who have not yet started plan to do so in the next 12-24 months.
• Invest in data. Data-related challenges are either the top or second most reported challenges once the ML journey is started. The importance of data quality, data engineering and building appropriate data infrastructure and pipelines to enable ML initiatives cannot be overstated.
• Australia could be facing a ML skills gap. Only 69% of organisations with models in production report sufficient ML capability.
• Top ML use cases are internally focused… for now. The top two business areas are operational efficiency (48%) and business decision making (46%). Going forward, respondents plan a shift to both an internal and external focus: operational efficiency (57%) and customer experience (51%).
• Organisations can succeed with ML by making it a priority. 79% of respondents achieving success with ML have a strategy, suggesting that focus and investment drive outcomes.

“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 Joe Losinno, Co-founder and Director, DiUS.

“We’re seeing an 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.”

On the positive side, the majority (81%) of those organisations that are in production are reporting successful business outcomes. Some examples include:

• bolttech built a new kind of customer experience. Using pioneering remote diagnostics  technology, bolttech can quickly and easily onboard customers onto device protection plans.  Customers simply hold their smartphone in front of a mirror and move through a sequence of  tests, powered by next-gen machine learning and computer vision technology. The result is a  zero-touch risk mitigation tool for bolttech and a best-in-class experience for customers. Link to  case study. 

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  Datarock analyses digital photos to extract new value in mining. Datarock is a machine  learning-powered, cloud-based drill core image analysis platform that provides accurate, fast and consistent high-resolution information about a mineral deposit’s geology. Using computer  vision and image analysis, Datarock supports more efficient decision making throughout the  exploration and mining process, delivering important productivity and throughput savings to a  mine’s bottom line. Link to case study. 

The survey is available for download on the DiUS website. It outlines each stage of the ML journey and provides some key considerations and tips for organisations to consider when pursuing ML projects.

The survey, fielded from December 2020 through February 2021, helped identify the key challenges and priorities for ML projects. The 205 respondents were predominantly senior executives and technology practitioners across 18 industries. Notably, 70% of respondents were directly involved in their organisation’s ML initiatives.