Good AI strategies are built around multi-disciplinary internal teams, according to Bannister. To have a successful AI strategy, it’s critical for companies to connect their technology, data and business teams. It’s beneficial to have technologists working on business teams and people with a business background working within technology groups, he said.
“A clear strategy will add trust in the organization to overcome psychological barriers and add funding in the right places to overcome technological and infrastructure barriers,” said Omri Mendellevich, co-founder and CTO of Dynamic Yield.
Another issue is that some companies apply AI to problems where AI isn’t the appropriate solution, according to Arnab Bhadury, data scientist at Flipboard.
“AI may not be the right solution, and it may even create more issues, especially if you don't fully understand the algorithms,” Bhadury said.
Getting AI products off the ground takes a lot of effort. In a July 2018 survey of 200 US and European IT executives conducted by Databricks and International Data Group, 56% of respondents said that preparing large data sets is a very challenging aspect of moving AI concepts into production. About half of the respondents reported that deploying AI models quickly and reliably is also very challenging.
“AI adoption is similar to the adoption of other technologies,” said James McNamara, senior vice president of client strategy at Nielsen. “AI has generally outpaced the organizational readiness to put it to use.”