Published : 6 years ago, on 02/08/2018

Enterprises anticipate that machine learning (ML) will permeate and influence the majority of business operations. Over half (52%) of enterprises expect this impact in the next two years, according to a new research study unveiled today by HFS Research in partnership with Infinia ML.

HFS’ new report, “How to Avoid Your Looming Machine Learning Crisis,” finds only 29% of data science decision makers across the Global 2000 believe machine learning is overrated.

On the contrary, the majority (86%) believe machine learning is impacting their respective industries.

While many enterprises have started down the machine learning path, the study finds that the speed and intensity with which organizations are developing capabilities does not match the importance they place upon ML. Most enterprises have yet to make significant investments in ML (84% investing under $1M), have decentralized practices (8% have centralized ML functions), are mostly running a few projects (65% are running 1 to 3 ML initiatives), and believe that of those projects, only a fraction might deliver business impact.

Further, talent is emerging as a key challenge; 42% already recognize they have significant skill deficiencies, particularly when shifting from traditional IT to ML and data science skills. HFS anticipates that many enterprises have not fully discovered how acute these skills gaps are, but will as ML needs increase over time. The report addresses these challenges through the “HFS ML Execution Guide,” designed to help business leaders get started with ML, deliver value over time, develop more robust capabilities, and ultimately, build industrialized, ML-enabled operations.

“Over three quarters of respondents expressed optimism about the business value of machine learning – but over half agreed that a growing number of ML ‘experts’ are just capitalizing on industry hype,” noted Robbie Allen, CEO of Infinia ML. “Clearly, the future belongs to those who can separate hype from reality and take practical steps to implement machine learning. That’s what we do at Infinia ML.”