It’s those who are focused on applied machine learning and will recognize that the technology is only valuable to the extent to which it replicates or scales some manual process in the physical world. It’s those who are able to connect the technology to the real-world impact and are first and foremost prioritizing the real-world impact that have the biggest impact on your organization. That’s the single biggest thing that we’ve learned and that I’ve personally observed.
That’s really important because there are some very intellectually stimulating and exciting applications of ML that are highly creative but don’t actually result in real-world impact and that that’s great. There’s a lot of value in those explorations. When it comes to moving the needle and transitioning your company from a SaaS 1.0 to SaaS 2.0, or from a company that goes from being scared of customer reactions to craving them, it requires a deep prioritization of the real-world impact that you’re looking to materialize. That’s something that can be coached, but it’s something hiring mangers who are looking to hire or data scientists and ML researchers should be aware of.
Now, the ML toolkit is rapidly expanding, which is very exciting. But if you think tool first and not problem first, then you run the risk of not applying ML the right way and you run the risk of applying it too often.