WALMART is a giant retail organization that managed to keep up with its customer’s changing needs in the digital era, delivering groceries and everyday essentials as efficiently as delivering on expectations.

At a recent conference, Business Insider heard Walmart Chief Digital Officer Bill Groves talk about the organization’s journey to digital.

Groves revealed that Walmart employs roughly 1,500 data scientists and 50,000 software engineers who support the 100,000-odd artificial intelligence (AI) and machine learning (ML) projects that the organization currently runs.

“I do more work in the AI and [machine learning] space then I have ever done in my life. We’re involved in robotics, we’re involved in micro-personalization, we’re involved in probably the biggest supply chain in the world.”

The important thing, however, is that Groves said that Walmart’s success rate with AI and ML projects is 75 percent. That’s a huge deal — and Groves believes it all comes down to following a three steps vetting process before taking on any project:

# 1 | Understand who is commissioning the project

While Walmart might have a strong budget for technology and plenty of net income to support ambitious projects, the company has a clear rule about AI and ML projects: They need to know who is paying for the project.

“If nobody will pay for, then why am I doing it? The business has to see the value, the business has to want it.”

The team might decide that the company is investing its resources in the project or a particular department or division is commissioning it — but that’s a conscious decision that is made, ensuring that AI and ML projects always have a commercial outlook.

# 2 | Find a way to explain the idea in simple words

One of the steps in the vetting process of AI and ML projects that Groves highlighted was raising the question: Can anyone explain it?

“If I cannot explain to an executive what I’m doing, then why am I doing it.”

It might seem like a simple question but the reality is that a lot of teams get started with projects without a common understanding of the project or what the intended goal is. However, when teams are forced to answer this question, the goal and objectives become evident.

# 3 | Have a plan to implement the project

Sometimes, data scientists get excited about AI and ML projects that seem feasible, until they look at the scale of the business and think about whether or not the project can really be implemented.

If something can’t be implemented, it doesn’t make commercial sense to invest time and resources into it.

“The data scientists talk to the business, they came up with an idea, they didn’t include the business or the technology team with the implementation. They come back, they have a model that stands no chance of ever making it into production with the systems you have. Definitely not cost affordable.”

Groves advises other businesses exploring AI and ML projects to think about real-world implementation when a new project is conceptualized.