It's tempting to see artificial intelligence (AI) and machine learning (ML) as magic of sorts. In reality, though, you should think of them as a set of tools that can enable new capabilities. At Manifold, we do our best to avoid jargon, and work hard to demystify AI right from the first conversation with new clients. Particularly, when we’re talking to executives, which we often do, we try to get them to dial back a little bit on the pixie dust aspect of AI, and look at their project within the context of a more traditional product development spectrum.

In this case, by the product development spectrum we mean a starting point of the right questions to ask and the right types of business strategy and go-to-market questions they should consider. When having this conversation, it helps if we're all working with the same vocabulary. In this way, it's helpful to talk about software engineering as opposed to AI.

Admittedly, there is a massive marketing wave that is much larger than what we choose to do, and that does contribute to the context someone has when coming into a conversation with us. More important than the specific words we use, we make sure to take people from that known context into a world where we feel we can have a much more real conversation—focusing on things that are grounded and the actual work we do.

A lot of people are uncomfortable with terms they don’t understand, but they believe they’re supposed to understand them, and continue using them. Once you unpack the marketing terms—“Let’s talk about what you mean by AI, and let me unpack what I mean, and make sure we have a shared vocabulary”—they start to feel more comfortable. One way we do this is by walking through our Lean AI process and explaining what each step looks like in practice.

As a final note, hype isn't always a bad thing. It does motivate organizations that traditionally wouldn’t look at technology as enabling components of their business strategy—“Oh, well, let’s at least take a look because it seems everybody else is getting some value from it.” The hype does at least stir up things inside organizations where you get some creativity going and people are willing to at least step out of their day-to-day and take a look.

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This article is adapted from Vinay Seth Mohta's appearance on The Designing for Analytics podcast. Check out the full episode for more on how to approach machine learning projects in the enterprise.