Data from your existing software automation can contain errors if it has never been vetted by humans. Ensure that it’s evaluated by your domain experts and later your AI experts before it’s used for model development.

When you have a new problem, you may not have data to start with, but there are workarounds. Think about what type of data is needed and if you can think of a way to generate the data either manually, through crowdsourcing, or other means.77 Just brainstorm ideas that you can discuss further with your AI experts in Step 3 of the HI-AI Discovery

You can think of a model as a computer program that can answer specific questions,

Here are a few scenarios when proactively searching for AI opportunities may be most beneficial: When you’re thinking about replacing legacy systems and business processes with modern solutions When you’re planning your company- or department-wide AI strategy When you’re trying to start a pilot project to gain AI experience

“proactive discovery”

Successful AI initiatives start with the right problems, but the right problems don’t necessarily come from your data scientists. They can come from leaders, domain experts, and innovators who sit close to the daily business challenges in your organization. Still, it takes practice to develop the vision for spotting AI opportunities, and this

working prototype model.

Because of the complexity of AI, you should never go from an idea directly to implementation.

if ReviewCrunch’s existing software automation was manageable, was not changing in complexity, and did not suffer from accuracy issues, the use of AI would be unnecessary. You’d incur additional costs with no apparent benefit. This will not result in a PAI—even if the data was available or could be collected.