How to Set Your AI Project Up for Success

Picking the right AI project for your company often comes down to having the right ingredients and knowing how to combine them. That, at least, is how Salesforce’s Marco Casalaina tends to think about it. The veteran artificial intelligence and data scientist expert oversees Einstein, Salesforce’s AI technology, and has made a career out of making emerging technologies more intuitive and accessible for all. With Einstein, he’s working to help Salesforce customers — from small businesses to nonprofits to Fortune 50 companies — realize the full benefits of AI. HBR spoke with Casalaina about what goes into a successful AI project, how to communicate as a data scientist, and the one question you really need to ask before launching an AI pilot.

You’ve been working in AI for a long time now. You worked for Salesforce years ago, then at other companies, and now you’ve come back to lead. How would you describe what it is you do in this work? 

I bring machine learning into the things that people use every day — and I do it in a way that aligns with their intuition. The problem with machine learning and AI — which are two sides of the same coin — is that most people don’t know what either really mean. They often have an outsized idea of what AI can do, for example. And of course, AI is always changing, and it is a powerful thing, but its powers are limited. It’s not omniscient.

The point you’re making about how imagination can take hold explains a lot of the issues businesses run into with AI. So, when you’re thinking about the kinds of problems that AI is good at solving, what do you consider?

When I talk to customers, I like to break it down into ingredients. If you think about a fast food taco, there are six main ingredients: meat, cheese, tomatoes, beans, lettuce and tortillas. AI isn’t that different: there’s a menu of certain things that it can do. When you have an idea of what they are, it gives you an idea of what its powers are.

I’m intrigued! So, what are AI’s ingredients? 

The first ingredient is “yes” and “no” questions. If I send you an email, are you going to open it or not? These give you a probability of whether something is going to happen. We get a lot of mileage out of “yes” or “no” questions. They’re like the cheese for us — we kind of put that in everything.

The second ingredient is numeric prediction. How many days is going to take you to pay your bill? How long is it going to take me to fix this person’s refrigerator?

Then, third, we have classifications. I can take a picture of this meeting that we’re in right now and ask, “are there people in this picture?” “How many people are in this picture?” There are text classifications, too, which you see if you ever interact with a chatbot.

The fourth ingredient is conversions. That could be voice transcription, it could be translation. But basically, you’re just taking information and translating it from one format to another.

The tortilla, if we’re sticking to our analogy, is the rules. Almost every functional AI system that exists in the world today works through some manner of rules that are encoded in the system. The rules — like the tortilla — hold everything together.

So how do you, personally, apply this in your work at Salesforce? Because I think people often struggle with figuring out where to start with an AI project. 

The questions I ask are, “What data do we have?” And, “What concrete problems can I solve with it?”

In this job at Salesforce, I started with something every salesperson tracks as a natural part of their job: categorizing a lead by giving it a score of how likely it is to close.

Data sets like these are a key source of truth from which to develop an AI-based project. People want to do all kinds of things with AI capabilities, but if you don’t have the data, then you have a problem.

Getting into the next phase of this, let’s talk about the lifecycle of finding a project and deploying it. What are the questions you find yourself asking when thinking about how to get from pilot to rollout?

What problem you’re trying to solve — that’s the first question you need to answer. Am I trying to prioritize people’s time? Am I trying to automate something new? Then, you confirm that you have the data for this project, or that you can get it.

The next question you need to ask is: Is this a reasonable goal? If you’re saying, I want to automate 100% of my customer service queries, it’s not going to happen. You’re setting yourself up for failure. Now, if 25% of your customer service queries are requests to reset a password, and you want to automate that and take it off your agents’ plates, that is a reasonable goal.


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