Generative AI has the power to radically transform entire industries, but implementing it at scale will require significant changes to the way companies do business. Organizational change is never easy, and most change initiatives ultimately fail. With generative AI, the rapid pace of innovation and widespread fear of the technology heightens the risk of failure. But generative AI doesn’t just have the capacity to replace workers, it can add immense value to their work.

“There is going to be a lot of resistance to implementing AI, and a lot of different reasons for that resistance,” says Yongah Kim, an associate professor of strategic management at the Rotman School of Management.

One pocket of resistance is workers’ reluctance to adopt a technology they believe could replace them. But Kim argues AI’s real value to a company is enabling growth.

“Many people talk about use cases that cut costs, because generative AI could replace some human work,” Kim says. “But the most successful use cases have been oriented towards growing the business or better capturing insights about customers.”

She gives the example of a U.S. insurer with a nationwide broker network, which had available a massive amount of data for each city where it operated. Using AI, the insurer created a model to predict areas of highest growth potential, allowing it to direct brokers to focus their efforts to maximize sales. But the insurer needed to overcome resistance from brokers first.

“There were brokers who had spent thirty years in their market, and believed they knew it much better than any machine could predict,” says Kim. “But the company overcame that resistance by changing its pay structure to temporarily pay higher commissions for sales in areas recommended by the algorithm. And once they got their brokers to actually adopt what the model was telling them to do, revenue and broker productivity both increased.”

In the end, it was a win-win scenario. The insurance company was able to identify opportunities without reallocating resources, and brokers were able to focus their sales efforts more effectively.

But not every effort to adopt generative AI will be so successful. Many won’t yield results, or will be less efficient than processes they sought to replace. Kim argues that it is essential that management foster a culture of experimentation that rewards the successes, but also values the failures.  

“Management needs to create a culture of being open to experiments — where people can dare to fail. It can be a good thing to celebrate failure, as long as you learn from it,” says Kim. “Being nimble is cultural. There needs to be a feedback loop to learn from experiments.”

Pay and bonuses are one way to incentivize experimentation with generative AI, but so is employee recognition. Kim recommends celebrating small wins, and embedding experimentation into performance evaluation.

“You might have a couple of small wins, and it is very easy to stop there. To be able to scale, you need to deal with the risk factors, build the culture and the capability, and align the incentives,” says Kim. “Many companies will say they want people to experiment, but then management often penalize the experiments that they deem less successful. People need to feel that it is okay to take risks. You need to reward those that do.”

And it doesn’t stop there. Making the most of those experiments takes additional effort. Identifying use cases for generative AI, building new data systems and new AI models to execute them is only the first step.

“When you invest a dollar to build the required data system and develop the AI model, you need to invest at least another dollar to enable the implementation, especially building the capability to use it. You need to change behaviours, processes and organizational structure to make sure it is actually implemented,” says Kim. “Many companies invest a lot in data systems and AI models and declare victory when it works. They assume it will be used, but that's not necessarily the case. Successful companies invest at least an equal amount to ensure technology is actually implemented. That requires a lot of effort. Because if new AI models are cumbersome to use, people will just go back to their old way of doing things. That is just human nature.”

A new course offered by Rotman Executive Programs will help leaders navigate the transition. Generative AI and Organizational Transformation will demonstrate what AI can do – and what it can’t. The three-day course will build understanding of the implications of generative AI, and help leaders develop a strategy to leverage the once-in-a-generation opportunities that are only beginning to present themselves. Generative AI and Organizational Transformation course will be held October 30 to November 1, 2024.