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In-Ear Insights: Why Enterprise Generative AI Projects Fail
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why enterprise generative AI projects often fail to reach production.
You’ll learn why a high percentage of enterprise generative AI projects reportedly fail to make it out of pilot, uncovering the real reasons beyond just the technology. You’ll discover how crucial human factors like change management, user experience, and executive sponsorship are for successful AI implementation. You’ll explore the untapped potential of generative AI in back-office operations and process optimization, revealing how to bridge the critical implementation gap. You’ll also gain insights into the changing landscape for consultants and agencies, understanding how a strong AI strategy will secure your competitive advantage. Watch now to transform your approach to AI adoption and drive real business results!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-why-enterprise-generative-ai-projects-fail.mp3
Download the MP3 audio here.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, the big headline everyone’s been talking about in the last week or two about generative AI is a study from MIT’s Nanda project that cited the big headline: 95% of enterprise generative AI projects never make it out of pilot. A lot of the commentary clearly shows that no one has actually read the study because the study is very good. It’s a very good study that walks through what the researchers are looking at and acknowledged the substantial limitations of the study, one of which was that it had a six-month observation period.
Katie, you and I have both worked in enterprise organizations and we have had and do have enterprise clients. Some people can’t even buy a coffee machine in six months, much less route a generative AI project.
Christopher S. Penn – 00:49
But what I wanted to talk about today was some of the study’s findings because they directly relate to AI strategy. So if you are not an AI ready strategist, we do have a course for that.
Katie Robbert – 01:05
We do. As someone, I’ve been deep in the weeds of building this AI ready strategist course, which will be available on September 2. It’s actually up for pre-sale right now. You go to trust insights AI/AI strategy course. I just finished uploading everything this morning so hopefully I used all the correct edits and not the ones with the outtakes of me threatening to murder people if I couldn’t get the video done.
Christopher S. Penn – 01:38
The bonus, actually, the director’s edition.
Katie Robbert – 01:45
Oh yeah, not to get too off track, but there was a couple of times I was going through, I’m
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By Trust Insights
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In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss why enterprise generative AI projects often fail to reach production.
You’ll learn why a high percentage of enterprise generative AI projects reportedly fail to make it out of pilot, uncovering the real reasons beyond just the technology. You’ll discover how crucial human factors like change management, user experience, and executive sponsorship are for successful AI implementation. You’ll explore the untapped potential of generative AI in back-office operations and process optimization, revealing how to bridge the critical implementation gap. You’ll also gain insights into the changing landscape for consultants and agencies, understanding how a strong AI strategy will secure your competitive advantage. Watch now to transform your approach to AI adoption and drive real business results!
Watch the video here:
Can’t see anything? Watch it on YouTube here.
Listen to the audio here:
https://traffic.libsyn.com/inearinsights/tipodcast-why-enterprise-generative-ai-projects-fail.mp3
Download the MP3 audio here.
[podcastsponsor]
Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher S. Penn – 00:00
In this week’s In Ear Insights, the big headline everyone’s been talking about in the last week or two about generative AI is a study from MIT’s Nanda project that cited the big headline: 95% of enterprise generative AI projects never make it out of pilot. A lot of the commentary clearly shows that no one has actually read the study because the study is very good. It’s a very good study that walks through what the researchers are looking at and acknowledged the substantial limitations of the study, one of which was that it had a six-month observation period.
Katie, you and I have both worked in enterprise organizations and we have had and do have enterprise clients. Some people can’t even buy a coffee machine in six months, much less route a generative AI project.
Christopher S. Penn – 00:49
But what I wanted to talk about today was some of the study’s findings because they directly relate to AI strategy. So if you are not an AI ready strategist, we do have a course for that.
Katie Robbert – 01:05
We do. As someone, I’ve been deep in the weeds of building this AI ready strategist course, which will be available on September 2. It’s actually up for pre-sale right now. You go to trust insights AI/AI strategy course. I just finished uploading everything this morning so hopefully I used all the correct edits and not the ones with the outtakes of me threatening to murder people if I couldn’t get the video done.
Christopher S. Penn – 01:38
The bonus, actually, the director’s edition.
Katie Robbert – 01:45
Oh yeah, not to get too off track, but there was a couple of times I was going through, I’m
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