More organizations are using machine learning for competitive reasons, but their results are mixed. It turns out there are better — and worse — ways of approaching it. If you want to improve the outcome of your efforts in 2019, consider these points.
Businesses are using machine learning to improve all sorts of outcomes, from optimizing operational workflows and increasing customer satisfaction to discovering to a new competitive differentiator. Despite the explosion of options in the last few years, the results vary from organization to organization based on several factors. If you want to avoid some of the common pitfalls and drive more value from your efforts, resolve to do the following in 2019.
Start with an appropriate scope
Many organizations are so focused on the potential benefits of machine learning that they forget to consider the potential risks. In glossing over the details, it’s tempting to transform the entire business when it’s wiser to start with a use case or two, learn from the experience and build on the successes.
“Hype leads people to believe machine learning can do anything. If you believe the media, we have artificial general intelligence, but the reality is we have artificial narrow intelligence, machine learning that does narrow tasks,” said Steven Mills, associate director of Artificial Intelligence and Machine Learning of Boston Consulting Group’s Federal Division. “If you want to go down this path, you should have a vision and objective for doing it, because it will lead you through a process of reimaging the business problems you face, how you can solve them in a different way, how your business model could potentially be disrupted, and how value could flow differently through your industry.”
During that process, Mills encourages clients to start identifying the first set of use cases.
Still, all the hype about machine learning and fears of being disadvantaged cause some organizations to attempt running before walking.
“Organizations say they have so much data and they think there’s a ‘there’ there without understanding how machine learning, deep learning, or automation is going to support the mission or organizational workflow,” said Josh Elliot, principal and director of artificial intelligence at Booz Allen Hamilton. “They’re starting too big, they have the wrong data or they have a lot of data but it’s unusable. We’ve also seen organizations that didn’t have infrastructure in place to accommodate what they wanted to do. Lastly, like most technology adoption, machine learning is constrained by culture.”
Two other things to consider are risk tolerance and whether the organization has the talent it needs to drive value from machine learning.
“You need to ask whether you have the talent internally not just to design and develop the algorithms but actually make it real for the end users and the customers that are going to be engaging or interacting with the machine in the future,” said Elliot. “We approach this technology in a very human-centered way, bringing user experience and UI-type experiential people into the conversations to make sure we have a full understanding of how the end user is going to engage with the machine or the output of the algorithm or model.”
Approach machine learning holistically
Machine learning is often approached as a technical problem when it’s really a multi-disciplinary effort. While the details of machine learning can get quite technical, the goals tend to focus on business impact. In addition, machine learning may impact business processes, the company’s culture, and the organization’s operating model.
“To get to value, you need to tease out how different it will be and some of the organizational challenges you might face so you can plan for them,” said BCG’s Mills.
A good first use case is one that can have a significant positive impact in a short period of time since a relatively quick win can help attract greater organizational support and continued investment. Don’t ignore longer-term projects, though, because some of them could be strategic to the business.
Read the source article in Information Week.