Four Steps To Removing Risk In AI Applications

Dr. Steven Gustafson is Noonum’s CTO and an AI scientist, passionate about solving hard problems while having fun and building great teams.

I’ve learned to recognize patterns for success while developing solutions using artificial intelligence for different industries like finance, healthcare, aviation and social media. In this article, I’ll share one pattern that reduces the risk of project failure in AI applications—identifying and validating business assumptions with internal resources or customers, then identifying and validating technical assumptions with external resources who are usually more capable to build AI prototypes efficiently and successfully.

First, identify the biggest business model assumption.

Behind every successful AI application is an idea from the business or the customer on how some process or decision could be improved. The business assumption is whether that improvement would be adopted and result in a significant enough improvement to justify the cost to change. Usually, someone in the business will see a better way, or someone in the business who works with a customer can see how the customer experience could be made better.

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