AI works best when executives, stakeholders, and product owners understand what AI truly is and what it can actually do.

At a bare minimum, they need to understand concepts such as "training data" and "ML model," and that there's no guarantee that an AI project will pan out.

Trust me. Things go much more smoothly when they have realistic expectations.

You'll want to get everyone – stakeholders, product teams, data scientists – together to work through the following:

Product and vision: What, specifically, will we do here? What do you expect it to predict, or classify, or generate? How will that fit into the product functionality and UI/UX?

Project boundaries: How much time and money will you invest in this project? There's no guarantee that an AI project will work, and there's no definition of "done"; it's up to you to set your criteria for pulling the plug on the effort.

Model metrics: How will you evaluate the model's performance? And what performance will be good enough? Note that "100% correct" won't happen, so you need to figure out how often the model can be wrong before it harms your business, your clients, or even some complete strangers.

Risk assessment: Does this violate any laws, either where your company is located or where your clients are based? Even if your activities are legal, do they raise any ethical issues? And at a functional level, how does your business suffer when the model gives wrong answers?

Runtime costs: I'll get to the details on this in a bit. For now, just know that this model will cost money to run. Be sure to allocate extra budget for that.

Every AI project is built on a set of training data. This will probably be your internal, proprietary data (such as customer transactions) but you may also purchase a dataset from an upstream provider or draw from a public source.

However you get your data, do yourself a favor and make sure you actually have permission to use it.

And also make sure that you can get more data later on, if need be.

Your company's data scientists or ML engineers will give you the full story on what they're doing, but here's the short version: they'll need to iterate through a variety of techniques and tuning parameters, trying to build a model that performs well enough according to the metrics you defined during the planning stage.

It's tempting to remain in this phase in search of ideal performance. That's precisely why you defined a scope and budget during the planning phases. When the budget runs out, it's time to halt the R&D work. You either go with the model you have at that point, or you scrap the entire project.

Assuming you get a model that performs well enough, you'll need to deploy it to production so it can take requests and make predictions.

Your data scientists will work with your software developers and IT ops teams here.

As an executive, the important thing for you to note here is that a model running in production incurs ongoing costs. If your technology infrastructure runs on a cloud service (and, really, that's just about everyone these days) expect a bump in your monthly bill.

I use two main analogies to explain ML models:

1. They're like interns: full of energy, but minimal experience.
2. They're like factory equipment: they occasionally emit bad widgets.

Both of those lead to my main point, which is:

Never let the model run unattended.

Do yourself a favor and track the model's activities. Is its error rate – the number of bad predictions – increasing? Does it seem to give the same answer a lot? Has the outside world changed enough that it no longer matches what was in the training data?

Those are all signs that Something Is Wrong. It's now time to retrain the model on updated data. Or, in extreme cases, you may need to build a completely different model, using different data.

Point being: an ML model is not a static affair. So by building a project or product around AI, you've committed yourself to constantly reviewing the model's performance and occasionally refreshing it.