Machine Learning is a Hammer.

Is your product a nail?

But I’ve noticed a blind spot.

Ready?

Machine learning.

Before you get up in arms, let me explain.

At no time have I ever been called into a meeting to discuss how blockchain or AR could revolutionize my product. (OK maybe, in the context of a hackathon, with the explicit acknowledgement that the business would never support such a thing). We know that it is poor product management to start with a solution — especially a vague category of techniques, which can hardly be called a solution — and go looking for a problem to solve. When we do this in other contexts (how could we use chatbots to accelerate our business?) we are all but laughed out of the room.

But for some reason, we don’t feel this way about machine learning.

It is not unusual at all for executives to convene a task force to “find ways to apply machine learning in our business.” Or to send employees out to conferences, consultants, or collaborators to try and find a way to make this secret sauce their own. They seek an application instead of pursuing a problem.

Why is that? Why do we feel this obligation to one Silicon Valley buzz-term over others?

I have a few hypotheses:

There’s no denying that machine learning has played a significant role in making these companies successful. But it’s a role enabled by a strong core product with a clear funnel of user value to optimize. Matching people to ads, to the right products to buy (fundamentally ads), to the right movie previews (also fundamentally ads), and, wait, to ads again is a great job for machine learning.

But the ML-driven matching is supplementary to what makes FAANG products great: sticky social networking; fast and convenient shipping, with access to products people want to buy; a set of compelling movies and TV shows that people want to watch. Without these core product investments, there would be no role for machine learning. There would be nothing to optimize.

Companies are often clamoring for investments in machine learning, hoping that either the technology or the marketing message will save them. But what’s truly needed is core product investments… that solve a real problem… in a way that customers will pay for.

2. Real differentiation is hard — teams are looking for a halo effect where they can get it. Machine learning is an easy grab.

We understand. You need to stand out. Because machine learning is associated with success, and most people barely understand it anyway, you can slap it into your marketing materials for all (attention) gain and no pain…

…until you waste time and resources trying to figure out what that marketing message really meant. And then build something to try and fulfill the words on the page, even if you don’t fulfill the promise. And you edge out the more valuable work you could have done with the same resources.

3. It’s easy to have a shallow understanding of machine learning that misleads about its true capabilities.

With other categories of high-profile tech buzz — AR/VR, chatbots — it only takes a couple of interactions with real use cases to realize that the technology just doesn’t work as well as the hype.

Because machine learning is “in the background” optimizing a metric, it’s hard to say from a demo whether the application provides a meaningfully improved experience. Seeing the right video or product or friend recommendation could be a fluke, or a handcrafted human choice, or a really good rules-based heuristic. The success of ML is often evaluated using high-level business metrics (look at all the time people spend watching Netflix!), and it’s difficult to parse apart how much machine learning really contributed (vs. the content itself, the new UX design, a marketing campaign running when you launched your latest model…). If you’re optimistic, or you have a shallow understanding of the tech, it’s easy to assume an outsized contribution.

This allows for a lot more fanciful storytelling around machine learning than around other popular technologies. Vague references to the outputs of a machine learning model and the features used enable businesspeople to fill in the blanks with their imagination… and unfortunately, what they imagine is often far more sophisticated than what machine learning can actually do with the data that they have available.

If all of this is true, the solution to our problem is to create a layman’s approach to understanding if your team actually needs machine learning. My attempt is as follows:

What you actually need for a machine learning investment to make sense for your team:

What most companies have:

For the sake of clarity, this is not a hit piece on the value of machine learning or on the many extremely talented data science and engineering teams who enable that value. We need look no further than Silicon Valley darlings to see the impact that machine learning can have on a business.

However — this is a call-to-action for product managers and other business leaders to take off their rose-colored glasses and acknowledge that ML is just a technique, like many others, that is expensive to get up and running and should be utilized strategically. If you want to do exploratory R&D work with no line of sight to revenue improvement this year, that’s also fine. But call it what it is. And don’t use it as a substitute for doing your homework to understand where machine learning could make the biggest impact on your business.

It is very possible — likely, even — that to hit your numbers this year, you do not need machine learning. And that the increased efficiency of putting your dollars toward higher impact initiatives could do more for you than any bemused data scientist, looking at your 10,000 rows of user data, ever could.

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