Company leaders, at least on earnings calls, say they want to use generative AI to boost the bottom line, and Microsoft recently estimated that nearly 60% of Fortune 500 companies are using its Copilot AI assistant.
But speakers at Fortune Brainstorm AI Singapore last week warned that companies need to overcome a gap in value, confidence, and expertise if they want to leverage the benefits of employing generative AI.
“There’s a lot of excitement about AI, but translating that into business outcomes is not easy,” said Debanjan Saha, CEO of DataRobot, referring to what he called the “value gap.”
When it comes to the “confidence gap,” businesses are “not confident enough to take those AI applications or models to production, because they are not sure about the accuracy,” he continued.
Fortunately, Saha noted, companies don’t need their workforce to have a deep level of expertise in AI and building models. Instead, “you really need people who can use these models to actually solve business problems.”
Another important element of the “confidence” gap? Figuring out how to “stay out of jail,” Saha said.
But government scrutiny isn’t stopping companies from trying to adopt AI. “Surprisingly, the industries which are more regulated are actually using [AI] a lot, contrary to what you might think,” said Vivek Luthra, senior managing director for growth markets and ANZ data at Accenture. (Accenture is a founding partner of Brainstorm AI.)
Luthra said that companies can approach the gaps in AI from a workforce transformation perspective. Companies need to think ahead, determine what work they will be doing in the future, and then cultivate the workers they’ll need to achieve that.
Training the right talent could result in huge value for a firm. Luthra cited the example of a food and beverages company, an Accenture client, that used AI to create a year’s worth of marketing content in just eight days.
“That is phenomenal in terms of productivity enhancement,” he said.
But workers will need to be trained in more than just generative AI and large language models. “To be able to scale, you need to think about the competencies. Having a large language model is a small part of that,” Luthra said.
Both speakers warned that not every firm will be able to adopt AI at the same rate. Tech and asset-light companies can be nimbler, while asset-heavy firms like manufacturing may need more time to use the new technologies.
Saha reminded attendees that the point of generative AI is to return value to businesses, and that the “honeymoon period” is not going to last long.
“Showing near-term value and showing some return on investment, showing some early successes—I think that’s very, very important,” Saha said.
