If you're concerned (or super excited) about machine learning (ML) becoming mainstream, a recent survey by Oxford Economics on behalf of human resources (HR) and IT asset management company ServiceNow should pique your interest. The report, which surveyed 500 Chief Information Officers (CIOs) in 11 countries and across 25 industries, found that 49 percent of the companies are already using ML to improve traditional business processes.
Of the 500 CIOs surveyed, 200 said they're already beyond the pilot stage and have begun deploying ML in some capacity. CIOs are hoping to limit user error and errors in judgement by introducing automation. Almost 70 percent of CIOs said decisions made by machines will be more accurate than those made by humans. According to the survey, CIOs today are primarily focused on using ML to automate repetitive tasks (68 percent), make complex decisions (54 percent), recognize data patterns (40 percent), and establish links between events (32 percent).
"One of the reasons you're hearing so much about ML is that it's the wave of productivity that will separate companies from the competition," said Chris Bedi, CIO at ServiceNow. "It's faster and offers [the potential for] better decisions. Humans have biases, algorithms don't."
Bedi said he sees huge potential for ML in industries such as enterprise resource planning (ERP), inventory management and supply chain, among many others. Forty-one percent of CIOs in the survey cited a lack of skills as the main issue stopping them from deploying ML today. Conversely, only 16 percent of CIOs and their companies have plans for workforce size and role changes to accommodate ML.
ML and Jobs
The numbers released in the Oxford Economics survey are short-term projections, unlike a report by management consulting firm McKinsey & Company. Their report projected that half of today's work activities could be replaced by automation from 2035-2055, depending on various factors. The firm's report analyzed 2,000 work activities across 800 occupations and found that almost $2.7 trillion in wages are spent on jobs that could ultimately be automated.
"ML will change people's roles," said Bedi. "I don't subscribe to ML taking away people's jobs; it will change people's jobs. Mundane decisions will get automated, which will free people up. New jobs will get created."
Bedi said the key to leveraging ML to improve the bottom line while maintaining the rank and file is shifting current employee skill sets and hiring new talent to manage ML capabilities. "Talent is a big issue," said Bedi. "Data Scientist has got to be one of the hottest jobs out there. We really need to look at what is our three-year talent and skill road map? And be really purposeful about building those skills. We've got to train employees but also figure out alternative sources to that talent."
Bedi urged employers to hire and train employees to take advantage of ML-based processes. Once humans are comfortable with ML's ability to produce reliable data and make correct decisions, he said the industry will transition [to] machine decision making guided by human oversight.
The Late Adopter Dilemma
The Oxford Economics survey isolated 50 companies that were deemed "First Movers." The survey studied these companies' business processes and talent strategies to determine how and where ML would be advanced in the coming years. The study found that First Movers are more likely to have redefined job descriptions to focus on how humans work with machines, and have made plans to develop specialized teams focused on developing and using ML technology. Unlike their peers, these companies are more likely to have developed road maps for future processes, capturing errors and ensuring data accuracy.
Unfortunately, other reports indicate that the smaller the organization (and the fewer resources an organization has), the less likely it is to be prepared for the ML wave. A recent study by Bluewolf (an IBM company) found that only 33 percent of small businesses planned to invest in artificial intelligence (AI) and ML within the next 12 months. This is in contrast to the 30 percent of large companies that have already invested in the technologies and the 44 percent that planned to begin investing within the next 12 months. That's a total of 74 percent, or 20 percent more than the total of small businesses.
"We're early in the journey," said Bedi. "People and companies that are aggressive will separate themselves from the companies that aren't. It feels like there's a call-to-action to go do this. Companies that lean in are going to start to separate themselves from the competition. That separation will increase. CIOs will really start pushing on this in the near future."