It seems like almost every article written recently about business and technology mentions the concept of “machine learning” somewhere in the article, usually in the same sentence as “AI” (artificial intelligence). In fact, last year the NeurIPS conference -- one of the biggest machine learning and computational neuroscience conferences in the world -- accepted nearly 2,000 papers about machine learning. There is some concern in the academic community that too many papers are being published about machine learning, which is creating too much “noise” around the concept.
In other words: do business leaders really know how machine learning can impact their specific industry, or do they just know it’s a hot buzzword of the moment? That’s a central question right now. (Oh, and PS -- how good are business leaders at logic puzzles?)
There are almost too many to list. Broadly, it’s about massive data consumption for better decision-making. One of the most cited papers on machine learning aimed at a business audience, which dates all the way back to 2001, lays it out pretty clearly: “data analysis and pattern discovery.” Basically, businesses are always looking for some type of advantage, and where to find that advantage usually is within the data. In fact, you could make the argument that companies aren’t really “disrupted” by new technology -- instead, they are disrupted because they make poor decisions about products and services, and those decisions are poor because they looked at the wrong data, or analyzed the existing data in an illogical way. Machine learning helps with that.
That said, only about 15% of enterprise companies claim to be actively using machine learning programs right now, with about half in that poll saying they are “exploring” what can be done with ML programs.
By 2022, businesses are likely to spend about $78 billion on machine learning platforms and applications, and there is return to be had: ML is expected to add $2 trillion in value to manufacturing and logistics fields, for example.
There is sometimes fear that concepts like AI and machine learning will strip jobs away from humans. That’s actually not true.
Ideally, machine learning will allow more data-focused work and rote task work to be handled by technology, freeing up humans for more interesting, connective tasks. That’s the hope at least. There are some companies that might be very cost-cutting focused and they will use new technology to terminate human employees, yes. Most companies will use it to better the work that humans get to do.
Actually, in writing this article, we went on LinkedIn and searched for “machine learning,” then sorted by “content.” There were a lot of articles about acquisitions in the space, but mostly it was people asking for a job and claiming to be machine learning experts. But see, machine learning is a skill very much in demand right now. If these people were truly experts, they would likely have jobs right now at companies trying to do more with ML. Why don’t they?
Lots of potential reasons, but in all likelihood, they are not experts. The reality is that there are a lot of people in the world now claiming to be experts in these new technologies -- but they’re not.
Machine learning is evolving as a field every day. To capture the most business benefit from it, you need a team of actual experts -- and you get that with outsourcing to a team that has already worked on machine learning projects. Then they become an extension of your team and they are consultants to you at the same time, because they have knowledge from previous projects with other companies. They’ve seen what strategies worked in other places.
You get a consulting partner and a partner that executes on the work at the same time. This helps you keep up in the market.
If you are curious about what factors to consider when selecting a consultative outsourcing partner, download this checklist we put together. It will walk you through what you need to think about to get the most value and cost-effectiveness from the relationship: