In a study by Forrester Research made available this month, the market research company shows the increased maturity of machine learning deployment by enterprises.
Surveying 162 data scientists and IT practitioners building ML models in U.S. enterprises, it found 76 percent expect their ML use case to increase in the next 18 to 24 months.
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The majority (64 percent) expect only slight increases to their use case, while 20 percent expect moderate (between 15 and 50 percent) increases. Only six percent of people surveyed believe their use cases will decrease in the same time period.
“What we are seeing in the enterprise is that businesses are on-board with artificial intelligence, we have seen many successful use cases,” said Mike Gualtieri, principal analyst at Forrester Research. “This is not the sci-fi version of AI, it is the pragmatic version, which is ready for enterprise use.”
The three key goals for ML deployment include improved customer experience, increased profitability, and revenue. This shows a maturity in ML deployment by enterprises, according to Gualtieri:
“Customer experience, profitability, and revenue growth are always in style. The reason why it’s surprising is that three years ago those goals would not be at the top. AI was often associated with new products, services, and research and development. While these use cases are totally valid, they’re much harder to invest in because they’re new.”
The ROI is more associated with the business plan than reality, which you can calculate when you’re focused on operational efficiency. So this is a good thing for artificial intelligence because that leads to more adoption of these use cases,” he added.
Other key goals included security risk mitigation and increased business efficiency, while increased product innovation and market share were lower down.
This information was made available through a webinar by Forrester Research and HPE. The study will be made available later this month.
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