Evidence of Machine Learning's potential to change the world is now everywhere, from lights-out factories through to Netflix's recommendation engine. However, adoption is still not reaching the levels many predicted. In one SAP survey of 3,100 companies, just 7% said they are investing in the Technology, compared with 50% of companies defined by the SAP Center for Business Insight as 'leaders'. This follows a number of other surveys that have yielded similar results, including Belatrix Software's ‘Powering the Adoption of Machine Learning’, to which only 18% of companies who were asked if they had already started a machine learning initiative said they had done so, 40% that they were investigating it but hadn’t started, and 43% that they had no plans to start one at all.
This has serious implications. It allows competitors who adopt earlier to gain competitive edge that may prove impossible to recover from and puts you constantly on the back foot, struggling to catch up. It then makes it harder to attract the best talent when you finally do decide to look to the Technology, because who wants to work for a Luddite? However, you also need to be careful not to blindly rush in. The hype around machine learning, and AI in general, is such that it is tempting to rush in, desperately crowbarring in the technology anywhere with no sense of how to actually implement it successfully. This has already happened before with AI. In the 1980s, inflated expectations and subsequent disillusionment led to the so-called AI Winter, bringing investment to a grinding halt and pushing research ‘underground’. If this were to happen again, it could prove devastating for the technology.
We have outlined five questions you need to ask yourself before you set about adopting machine learning.
Do You Really Need It?
As with any new technology, there is a sense of keeping up with the Joneses, of adoption for adoption's sake. This could lead to valuable resources being diverted away from areas and projects that could really drive growth. If the Joneses get a fancy new lawnmower, there's no point blowing all your money on one too if you don't have a garden. Look out the window first, check there's grass to cut. Often, you'll find there isn't any. As David Linthicum recently wrote on infoworld.com, ‘Machine learning is valuable only for use cases that benefit from dynamic learning - and there are not many of those. The problem is if you have a hammer, everything looks like a nail. Vendors pushing machine learning cloud services say it's a good fit for many applications that shouldn't use it at all. As a result, the technology will be over-applied and misused, wasting enterprise resources.’
Machine learning requires board level representation. This needs to be someone who sees its potential and believes in the project. They must have vision, openness, ability to change, and have a strong understanding of the business. They must also have, at the very least, a good understanding of the technology.
This could mean that someone already in the company takes charge, such as the Chief Information Officer, Chief Data Officer, or Chief Technology Officer, all of whom have a viable claim for ownership. Alternatively, it could be that an entirely new position exclusively focused on AI technologies is needed, particularly at larger organizations. Chief scientist at Baidu Research and AI guru Andrew Ng argues that a Chief AI Officer (CAIO) can fill a number of important functions necessary for ensuring that an organization is well positioned for adoption. They can take a view across the entire company to best understand its potential application in each department and the scale of the implementation challenge, designating AI expertise and technology from a centralized team according to need. A good CAIO will set out a roadmap around how to integrate AI with the company's overall strategy and fight to ensure resources are available where required.
