Predictive Analytics and Machine Learning tools that can help improve forecast accuracy are readily available, but adoption is still in the early stages with only 14–16% of respondents saying they have adopted these tools. But 35–40% are considering or planning to adopt these tools.
With all the buzz in the information technology industry around artificial intelligence (AI) and machine learning (ML) you’d think that every organization was using these tools or planning for how they are going to use them. After all, the promise is that AI and ML will help organizations harness the ever-growing volumes of data being generated by automating and augmenting human analytic processes and decision-making.
But is that really the case? What percentage of companies are actually using these tools? In what functions and for what applications? That was the focus of a market survey OneStream Software launched in July of 2019. And the results are somewhat surprising. Read on to learn more.
OneStream’s Predictive Analytics, AI and ML Survey
The OneStream survey on predictive analytics and AI/ML usage was launched and executed in July and August of 2019. The majority of respondents (69%) were Finance professionals in North America and EMEA while IT represented 24% of responses. In terms of company size, 80% of respondents were from companies with over $500M in revenue. So we felt the sample was a good representation of our customer base and target market.
So what did the survey results reveal? First of all, forecast accuracy is clearly a challenge for many organizations, and a large majority say improving forecast accuracy can have a medium to high impact on their business. (figure 1) Lack of management input, lack of access to necessary data and ineffective software tools are the primary drivers holding them back.
Predictive Analytics and Machine Learning tools that can help improve forecast accuracy are readily available, but adoption is still in the early stages with only 14–16% of respondents saying they have adopted these tools. But 35–40% are considering or planning to adopt these tools.
The target use cases for Predictive Analytics and ML are mostly on the revenue side of the business with Sales Forecasting and Demand Planning in the top five, along with Customer Service, Intelligent Process Automation and Anomaly Detection. (figure 2) And it does appear that many organizations are starting to re-tool their skill sets with Data Scientists already in place at roughly a third of organizations.
To be prepared for the use of Predictive Analytics and Machine Learning, organizations should make sure they are selecting software partners that are investing in these technologies and making them accessible and applicable to the business processes that can most benefit customers.
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The industry has clearly gotten beyond the “hype” stage, and we are now seeing positive results from practical use cases for Predictive Analytics and ML. The next few years should be exciting as more organizations adopt Predictive Analytics and Machine Learning tools, achieve initial success and expand their adoption across departments and additional use cases. Download the survey report to see the complete results and register for our upcoming webinar to learn how OneStream is making Predictive Analytics accessible and practical for finance and line of business users.
