New report finds Australian organisations slow to adopt machine learning

Melbourne, Victoria – An Australian survey conducted by 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

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 Machine Learning National Pulse report 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. 


About the National Pulse report 

DiUS conducted a pulse survey to capture insights into how Australian organisations are adopting,  using and driving success with Machine Learning. 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. 

The report is available at: 

About DiUS 

DiUS is an Australian technology consultancy that specialises in using emerging technology to solve  difficult problems, get new ideas to market or disrupt traditional business models. 

With a cross-functional team of 150+ people across Sydney and Melbourne, DiUS provides game changing approaches to cloud enablement and product development— coupled with expertise in  Internet of Things (IoT), big data, Artificial Intelligence (AI) and Machine Learning (ML). 

In 2019, DiUS was the first to attain the AWS ML Competency in A/NZ off the back of a world-class  delivery process and several successful AI/ML client case studies. The team build, productionise and  scale ML-powered applications that can be deployed to the web/mobile or IoT devices such as  wearables, drones and monitoring equipment. DiUS drives innovation and impact through leveraging  AWS ML-as-a-Service as well as building custom state-of-the-art ML models. 

Contact information: 

Angela Gallo 

Marketing & Communications Lead, DiUS

0413 084 054

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