Setting AI Projects Up for Success

HBR IdeaCast

Harvard Business Review

Leadership, Entrepreneurship, Communication, Marketing, Business, Business/management, Management, Business/marketing, Business/entrepreneurship, Innovation, Hbr, Strategy, Economics, Finance, Teams, Harvard

4.4 • 1.9K Ratings

🗓️ 5 December 2023

⏱️ 25 minutes

🔗️ Recording | iTunes | RSS

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Summary

Unfortunately, you can’t set up your organization’s artificial intelligence projects like just any other IT project. By their nature, AI endeavors are quite different and suffer high failure rates. But there are proven approaches you can take to increase your odds of success. Iavor Bojinov, assistant professor at Harvard Business School and former LinkedIn data scientist, breaks down five critical steps for an AI project to turn into an effective product: selection, development, evaluation, adoption, and management. He’s the author of the HBR article “Keep Your AI Projects on Track.”

Transcript

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0:00.0	Hey everyone it's Kurt we need your help with our annual survey this is your last chance to help us get to know you so we can make idea cast even better for you
0:09.8	it's easy just go to HBR.org
0:13.0	podcast survey.
0:15.0	Again, that's HBR.org.
0:17.0	And thanks for listening. Welcome to the HBR Ideacast from Harvard Business Review. I'm Kurt Nicky. Generative.
0:45.0	Generative AI has the potential to change the business landscape, everything from marketing to supply chain
0:54.8	analytics to customer service, the sales, the applications are so rich you can begin just
1:00.9	about anywhere.
1:02.6	But even if you have started experimenting
1:05.3	or your organization has,
1:07.1	there's a big chance that those projects might fizzle out.
1:10.8	In fact, some estimates find that around 80% of AI projects fail, which is double
1:16.7	the rate of corporate IT projects from a decade ago.
1:20.5	Our guest today has experience leading AI projects and he has some best practices to share.
1:26.0	Yavor Bojinov is an assistant professor at Harvard Business School.
1:31.0	He's also a former data scientist at LinkedIn and he wrote the
1:34.7	HBR article Keep Your AI Projects on Track. Hi Yavar.
1:39.6	Hi Kudt, thank you for having me. I want to start with that failure rate.
1:50.0	You would think that with all the excitement around AI, there's so much motivation to succeed.
1:57.2	Somehow though, the failure rate is much higher than past IT projects.
2:01.5	Why is that what's different here? I think it begins with the
2:05.4	fundamental difference that AI projects are not deterministic like IT projects.
	...

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