The business world’s focus on machine learning (ML) may seem like an overnight development, but the buzz around this technology has been steadily growing since the early days of big data.

ML is beginning to deliver on the potential created by big data and analytics by turning raw data into useful, predictive tools for business. Innovation-minded business leaders are embracing ML as “the next big thing” and have already crafted ML strategies and initiatives that promise real benefits and return on investment (ROI).

The survey sought to reveal where organizations stand in terms of adopting ML strategies. Respondents included current ML strategists, representatives from companies planning to execute ML initiatives in the next months or years, and those with no ML plans for the foreseeable future.

Several key themes emerged from an analysis of the survey results:

ML is happening now. The majority of respondents (60 percent) have already implemented ML strategies, and nearly one-third considered themselves to be at a mature stage with their initiatives.

The majority of respondents (60 percent) have already implemented ML strategies, and nearly one-third considered themselves to be at a mature stage with their initiatives. ML provides marketplace advantage. According to respondents, a key benefit of ML is the ability to gain a competitive edge, and 26 percent of current ML implementers felt they had already achieved that goal.

According to respondents, a key benefit of ML is the ability to gain a competitive edge, and 26 percent of current ML implementers felt they had already achieved that goal. Organizations are investing in ML. Among current ML implementers, some 26 percent reported that more than 15 percent of their IT budgets was dedicated to ML initiatives.

Among current ML implementers, some 26 percent reported that more than 15 percent of their IT budgets was dedicated to ML initiatives. Early adopters are realizing ML’s biggest potential benefits. The top hoped for benefit among ML implementers and planners is the ability to extend data analysis efforts and increase data insights. Some 45 percent of respondents report success in meeting that goal. In addition, more than half of both early-stage and mature-stage users say their ML efforts have resulted in demonstrable return on investment (ROI).

The top hoped for benefit among ML implementers and planners is the ability to extend data analysis efforts and increase data insights. Some 45 percent of respondents report success in meeting that goal. In addition, more than half of both early-stage and mature-stage users say their ML efforts have resulted in demonstrable return on investment (ROI). ML implementers are pursuing a broad range of projects. The most common projects among current ML implementers are image recognition, classification, and tagging (47 percent); emotion/behavior analysis (47 percent); text classification and mining (47 percent); and natural language processing, or NLP (45 percent).

The speed with which respondents are able to demonstrate ROI with their ML initiatives is also notable, which, as mentioned earlier, was the not the case with big data analytics. Within the early-stage group, more than half report they are beginning to see a demonstrable ROI, and within the mature-stage group, more than half had demonstrated ROI.

For more findings and analysis, download the full report.