Gen AI main use cases span in 3 areas but none of Gen AI initiatives would be considered a moonshot or a fundamentally disruptive new business model!

?? Only 15% of Gen AI initiatives pursuing more transformative changes such as expert task automation, AI agent-led customer interactions, and hyper personalization of offerings at scale!

??None of the initiatives would be considered a moonshot or a fundamentally disruptive new business model.

?? The top three key areas where GenAI is used in human capital are:

Talent Development, Productivity and Change Management.

So far, about 85% of large enterprises’ generative AI applications target more incremental improvements in products, services, and efficiency, according to a new interesting research published by 贝恩公司 using data from about 33% of Fortune Global 500 companies (235 of them) which had publicly announced generative AI initiatives as of the end of February 2023.

?Majority of GenAI applications target incremental improvements

Researchers discovered that about 85% of the 235 generative AI initiatives announced by Fortune Global 500 companies through the end of February focused on incremental improvements to products, services, and efficiency.

However researchers highlighted that it's important to note that this analysis doesn’t include all the ways AI is being infused into everyday office software tools, and there’s some variation in the value of productivity use cases based on industry (what the cost structure looks like) and geography (relative cost of labor).

?? Nevertheless, the initial bias toward incremental innovation is unmistakable.

Finally researchers provide four key actions can improve the odds of success for a company’s bold AI bets:

1?? Use early wins to gain conviction. While many executives recognize that the opportunity is real, some might be hesitant to push their chips in with generative AI moonshots after feeling burned by other hyped technologies in recent years.

2?? Quickly determine which proprietary assets will deliver sustained competitive advantage. Emerging leaders are identifying their key assets quickly, recognizing they need to capitalize before competitors develop a lead that’s hard to overcome.

3?? Find the right balance between buying, building, and partnering. One of the most heated debates in every boardroom right now is when it makes sense to build a custom AI solution in-house and when it’s better to acquire or partner with someone else. No company has fully cracked the code, but one key consideration is whether the solution is built upon a proprietary asset that will provide true competitive advantage over the long term vs. capabilities that will become ubiquitous over time (e.g., data science).

4?? Establish a winning operating model to incubate new ventures. One thing the emerging generative AI leaders have in common: sponsorship and sustained engagement from top executives.

?The three use cases of Generative AI in Human Capital Domain

In a recent webinar hosted by MIT Sloan Management Review , Lynda Gratton shared her views about how companies can use AI for people management and the creation of Head of Generative AI and she said that this role encourage use of the technology to help with managing employees and she believes that there are three main use cases:

?? Talent development. Companies can use chatbots and other tools to recruit people and help them manage their careers.

?? Productivity. AI can be used to create assessments, give feedback, manage collaboration, and provide skills training.

?? Change management. This includes both internal and external knowledge management. “We have so much knowledge in our organizations … but we don’t know how to find it,” Gratton said. “And it seems to me that this is an area that we’re really focusing on in terms of generative AI.”

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Businesses across industries have invested enormous amounts of time and resources since end of 2022 developing their initial use cases and as researchers highlighted in this wonderful research, it’s still too early in the technology’s journey to fathom all the ways AI could transform industries and what its full potential will be. But this research proves it, we are at the beginning of this new technology and the reality is that it is a little slow for executives...

Thank you ?? 贝恩公司 MIT Sloan Management Review researchers team for these insightful findings: Lynda Gratton Mikaela Boyd Dennis S. Jones Florian Hoppe Mohan Jayaraman

Michael Egan Alice Leonard Cyrille Vincey Todd Papaioannou

Dave Ulrich

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