Here's a question I've been getting a lot lately how do we create some feedback systems that enable us to refine the AI solutions that we're using while bolstering our innovative thinking that's behind how we use those AI tools?

The first thing that we can do is to create a dual-loop feedback system. I know it sounds a bit complex, but the first one is how can we look at the data around the algorithms that our AI is using. So refining and improving them. The second feedback system we want to have is to look at a qualitative system on how people think the AI is working and how they're responding and working with it.

The second way that we can do this is to use an idea exchange platform. Now, I'm not a huge fan of idea crowd platforms, because we tend to get a massive dump of all of these ideas, and they might not necessarily be well thought out. But if you give people a template that self assesses their ideas on potential new AI applications or new AI tools that they could be using, that becomes very, very useful.

So do that and you'll find it starts to help you get some great results.

And the third thing that we can do to create great feedback is to do some good old A B testing. So we do an A test, maybe with the innovative use of our thinking, and our creative problem solving, and the B test might be using AI platforms. And so when we do that, we can see which one's more effective, or maybe it's a blend of both of those.

I hope these three techniques are going to help you to look at ways that you can get better feedback mechanisms into incorporating AI in your organization. Thanks for watching. And if you'd like to learn more about what we do, make sure you check out our 12-month innovation program called Breakthrough Innovator.

You can find us at breakthrough innovator.com.