AI Project Outcomes, Part 2

In a previous post we talked about the desired level of autonomy for your AI project: Some manual work required, or fully autonomous?

Let’s now tackle a different question that’s just as important: 

What is the goal of the AI Project

“Duh, it’s to solve problem X.”

Fair enough. But what does it mean to solve it? What does a home run look like? What does the successful solution enable you to do? If we have good answers to this question, we can make the right trade-offs and downstream decisions.

Success could mean

• same quality, but lower cost

• better quality, at no cost reduction

• increased velocity at acceptable loss of quality and acceptable increase in cost

• same quality, same cost, but everyone on your team is happier

There’s no right or wrong when it comes to success criteria. But once you’ve picked them, there are right and wrong choices for building the solution. Many projects fail, not just in AI, because such criteria were never established or communicated to all stakeholders.

Before drafting a project proposal, we at AICE Labs work hard with you to uncover this critical question: What does total success look like, and what value does that unlock for you? With those questions answered, we have many knobs to turn and can present a number of options that would get you closer to that goal.