Machine learning has captured the CIO’s imagination – largely because its potential to automate processes and improve decision-making could speed up difficult digital transformations. But how exactly are your peers using machine learning, and what are their plans for the future? What are the big obstacles? A recent study by ServiceNow and Oxford Economics offers up some interesting data points, based on their queries of 500 CIOs in 11 countries, across 25 industries. (See the full study: “The Global CIO Point of View.”)
“We see three kinds of processes as targets for machine learning—anything requiring rating, ranking, or forecasting,” said Chris Bedi, CIO at ServiceNow. “Everyday work such as the assignment of IT tickets and prioritizing sales leads are already delivering results.”
Remember, machine learning is a set of tools that will be used very differently among companies depending on business goals, as Vijay Raghavan, executive vice president and CTO at LexisNexis Risk Solutions, told us recently. This is not an application or a technique; the formula for applying machine learning tools will vary widely even within vertical industries.
That can be daunting to CIOs and IT teams. “There is no right time to use machine learning, other than understanding what problem you’re going solve and whether machine learning is going to help solve that problem,” Raghavan said. “It could be revenue growth, cost reduction, time to market, risk mitigation – it all depends on what your priorities are as a business.” (See our related article, Machine learning: 3 considerations from LexisNexis CTO.)
The study found that CIOs are increasing their investment in machine learning, but they face several barriers to achieving their productivity, revenue, and innovation goals.
[ See our related story, 5 TED talks on AI to watch. ]
“Unless CIOs turn their attention to updating not just technology, but talent and business processes, the full value of machine learning cannot be realized,” the study states. In other words, machine learning will not let you skip over the difficult talent and culture change issues associated with it.
Let’s peek at some thought-provoking stats from this study:
How does machine learning play into digital transformation?
- 72 percent of CIOs surveyed are leading digitization efforts, while 53 percent say machine learning is a focus of those efforts.
- Almost 90 percent of respondents say more automation will increase the accuracy and speed of decisions.
- Who’s spending? During the next three years, the number of respondents making at least some investments in machine learning will nearly double, to 64 percent.
How will IT staff change in the era of machine learning?
- 27 percent of CIOs surveyed have hired new people with intelligent machine skills.
- But just 40 percent of responding CIOs have rewritten IT job descriptions to focus on work with intelligent machines.
- 41 percent of responding CIOs say they lack staff skills to manage intelligent machines; 47 percent say they lack budget to develop those new skills.
What are the barriers to machine learning adoption?
- 51 percent of responding CIOs say data quality is the top barrier to adoption, while 48 percent cite outdated processes.
- How do you catch machines making errors? Just 45 percent of CIOs have developed methods for monitoring mistakes made by machines.
What do the early winners do differently?
A select group of CIOs, dubbed “first movers” by this study, outperform their peers in their use of machine learning. One difference: These CIOs have already focused on people and process challenges:
- More than 70 percent of first-mover CIOs have developed a roadmap for future business process changes, compared with just 33 percent of others.
- More than three-quarters have redefined job descriptions to focus on work with machines, compared with 35 percent of others.
One bonus data point: These first movers have their eyes on top-line revenue growth, not just cost-cutting: Almost 90 percent of first movers expect decision automation to support top-line growth vs. 67 percent of others.
