Successful AI Adoption Requires Clear Strategies

Successful AI Adoption Requires Clear Strategies

One of the most crucial aspects of setting up artificial intelligence (AI) products is making sure that a clear strategy is driving its adoption.

In a survey by McKinsey & Company of 2,135 professionals at companies from various industries worldwide, 43% of respondents said a lack of a clear strategy was one of the most significant barriers their organizations faced in adopting AI. Just 18% of respondents said their companies had clear AI strategies in place.

“Strategy is a huge component for many companies because it’s hard for people to wrap their heads around what AI can be used for,” said Paul Bannister, executive vice president of strategy at CafeMedia. “Unlike prior industry transformative technologies—like moving from paper to digital—it’s hard for many business people to understand how the tech works and how to apply it to their business needs, so the strategy part is really about connecting the dots there.”

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.”

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