Take a look at the hot technologies for the enterprise in 2018, and machine learning is right up at the top. Organizations are looking to get an edge on the competition by deploying this technology, even if many may not be ready for it. There are dozens of surveys out there about how many enterprise organizations have deployed this technology already or plan to deploy it. But just because they deploy it doesn't mean it will be successful. Just because they deploy it doesn't mean that the technology will solve entrenched problems.
Yet another new survey has pulled back the covers on some other aspects of machine learning in organizations, focusing on the best practices of those that are succeeding. The survey polled 360 senior executives across four geographic regions: North America, Europe, Asia Pacific and Latin America, and was conducted by The Economist Intelligence Unit. The Economist Intelligence Unit then collaborated on a report about the results with enterprise software vendor SAP.
The report identifies what it calls five traits of "machine learning leaders," or "fast learners." These are companies that are "already seeing substantial benefits from machine learning" that go across the entire organization and include higher profitability and revenues, greater competitive differentiation and faster, more accurate and more cost-efficient processes, according to the report.
These organizations range from small (defined as revenues as low as $50 million) to large. However, the report notes that the biggest companies may struggle the most with the change necessary to be so-called fast learners. More of these organizations cite organizational resistance to machine learning implementation as a challenge compared to smaller organizations. Some executives and workers may view machine learning as a way to replace people to accomplish certain tasks and jobs.
However, larger organizations also have an advantage because they have more access to the necessary talent for machine learning implementations. Smaller companies are making up for the lack of resources by tapping into what's available in the cloud, according to the report.
"Smaller organizations now have access to substantial computing power at a fraction of the cost of maintaining such hardware on-premises," the report said. "They also have access to wide bodies of external AI and ML knowledge through roughly a dozen open source innovation platforms that big technology organizations and research institutes have been creating."
You're probably wondering if your organization possesses any of these traits. The following is the list of fast learner traits compiled by The Economist Intelligence Unit in collaboration with SAP:
- They make machine learning a C-level strategic priority. Senior management at these organizations are more open to change because they see the strategic value of the technology, according to the report.
- They drive competitive differentiation and innovation. According to the report, 31% of these fast learner companies say that machine learning yielded business model or business process innovation.
- The potential for new revenues and profitability are driving forces. A full 48% of these fast learner companies say increased profitability is the top benefit of machine learning, and another 48% of fast learners say they expect revenue growth of more than 6% from 2018 to 2019.
- The key processes stay inside the company. According to the survey, 58% of fast learners say they spend more than half their budget for business processes locally -- not outsourced -- compared to 39% of non-fast learner machine learning users. They may end up spending more to keep those processes in-house, but realize greater customer value in the process, according to the report.
- They implement machine learning across the enterprise, rather than just in pockets. The report says that machine learning implementations aren't just in individual business units or functions. Plus, these fast learner companies are integrating machine learning into customer-facing and product development functions, with 41% saying that machine learning is translating into higher customer satisfaction.
The report also has the following recommendations for getting started with machine learning:
- Organize a machine learning bootcamp for the executive team to help business unit heads understand how machine learning can help the business.
- Identify external sources of machine learning knowledge -- look at examples of other organizations' machine learning initiatives
- Pilot the first machine learning initiatives in small sets of processes where the risk is low, but then spread the successful projects across the rest of the business processes.
- Direct your marketing and communications teams to put together a handbook for directors that they can use to answer internal questions about why machine learning is being adopted and what it will mean to their teams.
- Reassess long-term offshoring arrangements for business relevancy. Some processes should be localized after machine learning applications are implemented.