Everyday AI Podcast – An AI and ChatGPT Podcast - EP 597: Do 95% of AI Pilots Fail? Why You Should Ignore MIT’s Viral New AI “Study”

Episode Date: August 26, 2025

You got duped.The MIT ’95 % of AI pilots fail’ study has taken over the internet, and it’s one of the worst studies I’ve ever read. (And I’ve read thousands.) ↳ So, what’s the truth?�� Is AI a bubble that’s about to pop? ↳ Why is this study rubbish? ↳ And how does it impact you? Join us and we’ll dish it all.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:MIT AI Study Claims 95% Failure RateBreakdown of MIT Study MethodologyImpact of Viral MIT AI Study HeadlinesFlaws in MIT Study ROI MeasurementComparison With Reputable AI ROI StudiesMIT Study’s Biased Participant SelectionNanda Project Marketing in MIT ReportFive Major Red Flags in MIT AI ResearchBusiness Implications of Flawed AI Pilots DataHow Media Sensationalizes AI Study ResultsTimestamps:00:00 "MIT AI Study Critique"04:16 AI Investments Trigger Stock Market Decline06:37 "Host's Background Overview"10:58 Flawed AI Study Critique13:28 MIT Study Highlights AI Implementation Challenges18:58 AI Work Trends & ROI Insights20:17 "Crossing the Gen AI Divide"23:25 Flawed Study with Misleading Claims29:34 "Uncritical Reposting Spurs Fake Study"30:30 "Read Studies, Not Summaries"Keywords:MIT AI study, 95% AI pilot failure, enterprise AI pilots, generative AI ROI, AI pilot success rate, AI project failure, state of AI in business, gen AI divide, MIT Media Lab, AI investment, AI implementation challenges, AI return on investment, AI research methodology, AI study critique, AI marketing, Nanda project, AI vendor solutions, agentic web, MCP protocol, A2A protocol, Fortune article, AI media coverage, stock market impact, NVIDIA stock drop, Palantir, ARM stock, qualitative AI data, AI structured interviews, AI industry surveys, IDC AI research, Snowflake ESG report, McKinsey AI analysis, Microsoft Work Trend Index, Boston Consulting Group AI study, AI adoption rates, enterprise AI transformation, sample size in AI studies, research limitations, AI productivity impact, AI workflow automation, AI business decisions, AI bubble, AI reporting in medSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. Do 95% of AI pilots actually fail?

Absolutely not. But if you've been paying attention to the business world, the media, or AI LinkedIn, or AI Twitter, you might think so. That's because a recent MIT study said exactly that. That 95% of enterprise AI pilots show zero. R-O-I and therefore they fail. But that study itself is actually a failure. So I'm going to be kind of nicely tearing it apart on today's episode of Everyday AI

and showing you why you should all but ignore MIT's viral new AI study and actually how its misconceptions can play to your advantage. All right. I'm excited for this one. I hope you are too. let's get into it. If you're new here, welcome. My name's Jordan Wilson and this is Everyday AI. This is your daily live stream podcast and free daily newsletter helping everyday business leaders like you and me, not just make sense of what's happening in the world of AI and keep up, but how we can

use it, actually dissect it and understand and separate the marketing from the BS from the real and how we can use this information to get ahead to grow our companies and our careers. It starts here with the unedited, unscripted live stream podcast, but if you want to take it on the next level, that happens on our website, your everyday AI.com. We're going to be recapping today's podcast in today's newsletter, but also keeping you up to date with all the other AI news. So if you want that, make sure to go check the newsletter. All right. So let's just start dissecting.

It's hot take Tuesday, y'all. So yeah, almost every single Tuesday, we do a hot take on something that's happening in the AI world and nothing has been bigger than this study, which says a lot. It's usually not a study from MIT, nonetheless, that grabs all the AI headlines, right? When Google's releasing this and, you know, Elon Musk is suing everyone that walks, this is the thing that grabbed the most headlines. And let's just go ahead and preview a little bit what we're going to be going over today. We're going to be going over the findings of this now mega viral MIT study that claimed 95% of AI pilots fail.

We're going to uncover why it was an abysmal. study that was actually just marketing in disguise show you what real reputable studies actually say about Gen A.I return on investment. I'm going to give you the five biggest red flags of this study. And I feel weird calling it a study. And I'm going to expose how this was actually just marketing for an MIT project. All right. And this isn't on repeat. Yes, I did have to tear apart a different ill-conceived MIT study before their brain rod study. So, yeah, If you didn't hear that one, go listen to that episode 553. So this viral study was called the state of AI in business 2025, what they called the Gen AI Divide.

This was done by a group out of MIT. And like I said, the study went viral overnight. It started with a Fortune article. The study itself was actually in July, was released in July. It wasn't until August 18th that Fortune picked up the story. and within hours, literally almost every single big publication that I knew was covering it, from the Economic Times and Axios to the Hill and Forbes, right? Just about any big online publication cover this story.

And unfortunately, many of them just copy and pasted that attention-grabbing headline. 95% of AI pilots fail. So here's kind of what happened and what unfolded. So like I said, in July, MIT released that report that showed that 95% of enterprise Gen AI pilots showed no measurable Gen AI gain. And despite company spending $30 to $40 billion on AI. So like I said, the Fortune article dropped on August 18th. And that really started the media storm. The findings kind of also spooked investors.

I'm not going to get into that. I think plenty of people have covered this on the financial side. And it wasn't the only thing happening, right? There's been a lot of talk on this AI bubble, some things happening with the Fed, some interest rate news. But this was also one of the things that really caused the stock market to lose hundreds of billions in dollars in market cap. So, Nvidia fell by three to half points. Arm fell by almost 4%. Palantir, nearly 9%.

Right. This is one of the things. that a lot of investors pointed to as one of the reasons why. But smart people who actually read the entire study realized after all this media storm had kind of come and gone and the dust had settled, wait, this study is absolutely terrible. Like, why is no one talking about that? And there actually had to have been some follow-up articles from all these media publications that kind of got spoofed, right?

And they're like, oh, wait, we probably should. to read this a little more closely, but don't worry. That's what I'm here for. So here is the executive summary from MIT's findings. All right. So I'm just going to read the first sentence or two. So they said despite $30 to $40 billion in enterprise investment into Gen. A.I, this report uncovers a surprising result in that 95% of organizations are getting zero returns. All right. The outcomes are so starkly divided across both buyers and builders that we call it the Gen A.I. Divide. Brilliant. Brilliant. All right. Oh, gosh. Lifester audience. I got to take a sit. Should I, it's hot take Tuesday.

Should I just burn all bridges or should I be kind of nice? Let me know. Personally, feeling a little spicy. We'll see. So a little bit more on these findings. And yes, I'm putting findings in quotes. So, actually, no, let's let's skip forward. because before we get into those findings, I want to talk a little bit about my background, so you understand. So if you're brand new to the show, some of these things I've shared,

some of these I haven't. So I've interviewed hundreds of people on the everyday AI podcast. You know, we're almost at episode 600 here. So I've interviewed hundreds of people on the quote unquote air, and I've had thousands of conversations outside, obviously. outside of the everyday AI show. I've been doing this for almost three years.

Every single day, this is all I do. Formerly, I was an award-winning journalist. So, won some awards like ACP Store of the Year, Pulitzer Fellow, etc. That's important. I'm going to explain why. Also, an important part about my background. I spent six years earlier in my career, literally, my job, was reading and manually recapping studies.

Thousands of them. Let me repeat that. Before this whole AI thing, now this is what AI did. I used to have to read and take notes and annotate thousands of just boring studies. So keep that in mind. Talk about AI all the time. I've had thousands of conversations on AI. And for a big chunk of my earlier career, all I did was read and recap studies.

And I also want to put this in, on the record. I'm not against this study because it's anti-AI, right? Like, oh, Jordan, this ruins your narrative. You're talking about AI. I could care less, right? People always think, oh, I'm anti-AI. No, I'm not.

Or people think I'm like pro-AI. No, this is just the direction that the world is heading, right? Not like a huge AI, whatever they call them, you know, accelerator, right? Like, I think it would be better if the pace of AI would slow down a little bit, right, to allow people to catch up, but it is what it is. But I want you to know, I'm not against this study because it seems anti-AI. I'm against this study because it is anti-intelligence. No one, no one that is an intelligent person.

We'll read this front to back, maybe unless you work at MIT, we'll read this study front to back and be like, yeah, this is sound. Yeah, let's put this out. No. This is one of the worst studies I've ever read, and I've read a lot. So let's first talk about the testing methodology. Oh, my gosh, I'm laughing. Okay, so they used 300 publicly disclosed AI initiatives.

So they didn't say what those sources were. But presumably, right, they're looking at filings from public companies, you know, the big tech companies building AI earnings calls, press releases, etc. So they didn't disclose what those publicly disclose AI pieces were, but presumably that's what they are. They had 52 structured interviews with executive sponsors and frontline users, and they surveyed, sorry, I wish this was a typo. I checked so many times that this wasn't a typo. They surveyed 153 senior leaders. Oh, gosh. Another big bone that I had to pick is like, you couldn't just go and get this study.

It's almost like they really were gatekeeping it for some reason. You had to go fill out a Google form. I never got this. Luckily, people who got access to this study shared it online because they just weren't sharing it. Like, who puts out a study? And you gatekeep it. And you're like, nah, you're not going to get it.

Anyways, it's laughable, right? If you couldn't tell in my voice, the fact that they put out a study, this resounding, this with, with, this much emphasis that got so much play in the media. And then you read the notes and you're like, they surveyed 153 people. I could have done better. I think I did a LinkedIn poll on this very topic yesterday.

And I got like twice the number, like twice the number of responses. That doesn't mean I should go write a study. Right. We could have done better. We have a bigger audience. We could have. Maybe I should.

But that's not even the worst part. That's not even the worst part. laughably small this was. You know, it's almost like walking into a bar and just asking random people about AI and then writing a 32 page study about it or however many pages this was. Most people wouldn't know because they couldn't get their hands on it because you really got to be able to find this study. How many pages was this thing? 26 pages, right? Yeah. So it's kind of like walking in a bar asking a couple people about AI and deciding to write a 26 page paper on it. Then 95.

percent failure stat and how they got to that is actually even worse than their terrible methodology. Okay. So again, let me repeat a little bit about what this 95% failure rate stat was and tell you how they got to it. So they said that 95% of organizations are getting zero measurable financial return despite, you know, tens of billions of dollars of Gen AI investment. So the study defined zero measurable measurable financial return as the point where the vast majority of integrated AI pilots remain stuck with no measurable P&L impact. Yeah, profit.

We're talking profit and loss statement. That's how they're measuring it. All right. Okay. So the ROI impact was measured six months post pilot. Yeah, six months. Meaning zero ROI indicated no measurable financial return within that period. So this 95% failure rate was primarily concluded, though,

Uh-huh, from the 52 structured interviews. It wasn't from the, uh, yeah, so it, it, it wasn't from the, you know, the 300 public documents or the 153 senior leaders they interviewed, uh, or surveyed. This was from the 52 interviews. All right. Um, and these figures, according to MIT, were directionally. accurate based on individual interviews. So they derived that 95% failure stat from these 52 conversations, right? I had a busy, busy week of interviews a couple of weeks ago.

Like I talked to more than 52 people. Anyways, let's talk about what that means and read some of the fine, some of the fine print here. So this is from MIT's study, which you have to be, uh, a, uh, Sherlock Holmes to find. So in the research limitations, it says these figures are directionally accurate based on individual interviews rather than official company reporting. Sample sizes very bad category and success definitions may differ across organizations. Then they also put in this section on the next page on the research note, we define, so talking about the 5% success rate, the flip side of the 95%. So they say, we define successful implementation for task-specific gen AI tools as one's users or executives have remarked as causing a marked and sustained productivity and or P&L impact.

The 95% failure rate for enterprise A.S solutions represents the clear as manifestation of the Gen AI divide. So in other words, that 95% is its directional accuracy from. 52 interviews. So it means that that figure indicates a general trend or scale. But it is not precise. They are not audited numbers derived from formal company reports or simple, you know, before-after survey. It's just from conversations. And they are kind of the ones that are dictating something by their ability to decide on the directional accuracy. So the researchers, say that they're confident in the direction of their finding that successful implementation rates for custom enterprise AI tools in these pilots are very low, but they also acknowledge that the

exact percentage is in the estimate based on gathered qualitative data, not quantitative data. That is terrible. Who approved this? Who approved this study? Did no one outside of this little group look at this study and be like, nah, someone's going to laugh at this. Yeah, smart AI people are laughing at this, right? Go, you know, I'm sure there's, if you follow other AI sources, like actually smart people, academics, they're laughing at this, right? They're saying, yeah, this isn't a real study.

So this is, this one headline that's been setting the business world and the AI world on fire was essentially a vibe study. Right? We talk about vibe coding. it's, directionally, yeah, that's accurate,

95% based on some interviews. But it's, I'd say it's more marketing for MIT's NANDA project. That's what I think, but more on that in a couple of minutes.

But that's not what a real study looks like. Talking to 52 people and taking some direction from them and being like, yeah, we feel confident on this qualitative data. So let's go ahead and mark it up. That's not what a real study looks like. This is what a

real study looks like. All right. So podcast audience have a couple, have a screenshot here on my screen. I'm going to walk you through it. So I have different studies from 2025 from reputable organizations. Their key finding in the survey details. See if you can find a difference here. All right. So the MIT Nanda or MIT Media Lab survey that we're talking about here said 95% of organizations get zero return from generative AI. All right. And that's from talking to 52 people, right?

But in the survey or sorry, in the study, they also have the 153 people surveyed, the 52 interviews, and then the 300 AI initiatives that they discovered or used, right? Okay. Here's what real studies look like. like the International Data Corporation. Hmm. This is weird. The International Data Corporation,

one of the more reputable organizations in the world, I cite them a lot on this show because they put together real research. They surveyed 4,000 decision makers, and they found that there's an average ROI of $3.70 for every $1 invested in Gen. That's weird. Huh. $3.70 is a pretty freaking good ROI, but MIT says 95% get zero. That's not adding up. Let's look at more. Snowflake and ESG talked to 1900 business and IT leaders. They said that 92% of early adapters are seeing a positive return of return on investment on AI.

Hmm. Very different from MIT. EY, one of the biggest management consulting companies in the world, said 97% of senior leaders investing AI report experiencing positive ROI. Huh. Okay. McKenzie, they're legit. They're real. 92% of companies plan to increase their investment. You wouldn't do that.

You wouldn't get 92% of them increasing their investment if 95% of them were getting zero return. All right. The Microsoft Work Trend Index, great report. One of the best out there by far. 31 is surveyed 31,000 professionals. And it said 66% of workers report measurable business benefits from AI. Then you had the Boston Consulting Group, their BCG AI at work. Study said that 75% of employees see value and momentum from AI.

That's from a survey of 10,600. Even just looking at the first couple, IDC, $3.70 for every $1, positive R.O.Y. The EY study, 97% of leaders said that they are seeing a positive ROI. It gets worse for this MIT study. Don't worry. So here's where it starts to get worse. All right. There's a big problem, right?

The MIT study, they got all this, you know, all this positive momentum. Everyone's talking about it. Oh, no. The bubble's going to burst. What are we going to do? Generative AI is bad. What's the solution, MIT? Hmm.

MIT Nanda. All right. So that was on their cover. And then you can read about MIT Nanda, but I'll save you the time. All right. So this is from, I kid you not, this is from MIT's report that hardly no one could get their hand on. All right.

So from their conclusion, they said organizations that successfully cross the Gen A.I. Divide. Do three things differently. They buy rather than build, gosh, empower line managers rather than central labs, and select tools that integrate deeply while adapting over time. All right. And then they go on to say, just as the original web,

decentralized publishing and commerce, the agentic web decentralized action, moving from prompts to autonomous protocol-driven coordination. systems like Nanda. MCP and A2A represent early infrastructure for this web, enabling organizations to compose workflows, not from code, but from agent capabilities and interactions. Then they go on to say next page,

four organizations currently trapped on the wrong side. The path forward is clear. Here's the marketing, right? Stop investing in static tools that require constant prompting, start partnering with vendors who offer custom solutions, right? No one's heard of Nanda. Like the audacity for this report to just go from straight like, oh, yeah, we're super serious. We're MIT, right?

Just copy, copy paste this headline. Run with it. Right. And then at the end, we're just going to go ahead and throw in this Nanda thing that hardly no one's ever heard of. But we have the audacity to put it next to MCP, right? Anthropics protocol. And A2A, Google's protocol.

So they're like, all right, yeah, trust us. We know this study's going to get legs, right? Because we have this bold claim that 95% of Gen AI pilots are failing. And don't worry, the solution is stop doing this Gen. I thing, right? You got to get to Agenic Web. And Nanda is the leader in Agenic Web, along with MCP from Anthropic and A2A from Google. I can't make this up.

This is like one of those like punchlines that writes itself for Saturday Night Live for AI dorks like us. Yes, they literally went into, like, as seen on TV mode for organizations currently trapped on the wrong side. The path forward is clear. That's, oh, gosh, is no one at MIT is no one else that didn't work on this study, like hanging their head? Like, I can't believe this happened. Or did no one just bother to read past the headlines? There's more. All right.

I'm going to give you five huge red flags from this study. right. And I actually just thought of an additional one, right? Pilots are precursors, right? So if 95% of AI pilots failed and didn't show ROI, then why is 90% of Enterprise Fortune 500 organizations using Gen AI, right? The math isn't mathing there, right? Oh, if your pilot fails and it blows up in your face, you're not going to use it anymore, right? No. All right. Anyways, five huge red flags from this study. Again, study in quotes. Number five, the absurdly short success timeline.

In ROI measured at six months when enterprise transformations take one to two years with archaic tools trying to measure ROI on a Gen A.I. Pilot in six months, actual ROI? Nah, that's just setting up a study ahead of time that you know is going to fail. Here's another one. How about their microscopic sample for their gigantic claims? Right. 52 interviews.

That's what they used to get to their 95% of organizations. That's like talking to 52 people, one from each state and one from, I don't know, D.C. and some other territory and being like, yeah, we have a good now representation of the entire USA. say. We talk to one person from each state. It's laughable. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience. Meet Firefly AI Assistant, now live in the Adobe Firefly app, the all-in-one creative AI studio. Powered by Adobe's creative agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as

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Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com. I would never put my name on this. All right. Number three. It's contradicted by just about every single other real reputable peer research organization out there. I already gave you the examples. But like I said, recent studies show anywhere

from, you know, mid-60s to 97% positive outcomes with samples that are 10 to 600 times larger, right? So there's a reason MIT is a dramatic statistical outlier. That's because they want you to get their Nanda, whatever that is. Also, they only recruited companies with problems. Another thing hidden in their report that no one could read, and they knew that. They knew, well, people are only going to be able to read the headlines. So the study explicitly recruited organizations, quote unquote, ready, organizations willing to discuss AI implementation challenges, end quote, talk about a biased pool of participants, right? Yeah. Hey, are you willing to discuss AI challenges organization? Yes. Okay. Let's have an interview. Do you want me to fill out a huge.

No, no, I just want to talk to you. And I'm going to decide the direction of this one. So, of course, you're going to find extremely high failure rates when you ask for organizations willing to discuss their AI implementation challenges, right? The hordes of companies, the literally tens and tens of thousands of enterprise companies who are successfully using AI, if they looked at that, they'd be, be like, oh, well, no, we're good. And number one, the research, quote unquote, is actually, again, marketing disguise as science.

Yeah, Apple fell victim to this with their illusions of thinking research a couple of months ago, and MIT did it again. So Nanda, like they said at the end, hey, 95% of AI projects fails, but Nanda is the answer. You know, Nanda is in line with MCP and A2A, right, from Anthropic and Google, respectively. So Nanda is a project developed at MIT Media Lab, which charges reportedly $250,000 for corporate memberships to commercialize their agentic AI technology. So this study, you know, obviously identifies a learning gap problem, which was a predetermined conclusion from the terrible methodology of their study.

And then it concludes that companies need exactly what Nanda sells is the solution, right? So it's not research. This is an elaborate sales pitch. So why did this happen? Remember when I gave you my background earlier? As a journalist, I used to read these studies for breakfast. But if it bleeds, it leads. It's the same thing in journalism.

That's how AI research. is now. People in AI research, they want to make a name for themselves, right? Because now you have AI researchers getting NBA salaries. But this is how journalism works as well, right? And I get it. And I feel for the journalists that kind of just blindly copy and pasted what they saw someone else, right? I remember, right? Being a journalist and you know, you have two stories for that day and then a third one gets thrown on your desk, this one, you're like, I, you're like, I got 30 minutes to research this thing. read someone else's report, you retype it up yourself and you're like, are you?

Yep, this checks out, done. That's how journalism work. And unfortunately, this is how studies are working now. AI studies are becoming sensationalized. They are becoming marketing tools, which stinks. But that's where the money is. So just because you see a study, a research, even from a big organization, do not blindly believe it.

Read it. use your brain and research. And yeah, unfortunately, I think this was kind of pushed and fueled by the media. But it makes sense because right now news organizations are getting crushed because of AI. They're getting crushed because people are using chat GPT and Google's AI mode and perplexity instead of going to their websites. So they need clicks at all costs. So when they get a story like this and someone, you know, from tech blog.1.com throws the fortune article and says, we need this in, you know, in hour, they're going to put it up there.

They're going to say 95% of AI fails, 95% of AI fails, copy paste, copy paste. I literally showed on my screen. Even the headlines were essentially the same. So they need clicks and journalists are overworked and underpaid. But just as bad, just as big of the reason is why this thing spread like wildfire. It's because you had people out there not even reading the study yet. reposting and perpetuating the same claims, right? Like my feed was littered with people that I knew didn't read this study, right?

Because if you read the study and if you have a brain, you have an opinion probably somewhat similar to mine. Maybe mine's a little too hot, right? But anyone is going to read this and be like, yeah, this isn't a real study. Okay. So why is this map? Why did you just waste 30 minutes talking about this? well, and what should you do about it? Why is this matter?

Well, number one, don't just take my word for it. Go read the study, right? And read all actual studies that you care about or impact you. Right. If you're going to believe something or use it as a decision to potentially make a decision for your business, go read the actual study. Don't read the media reports, social media. Most of it is rubbish like this MIT study was.

All right. Also, if there's anyone out there, you know, maybe it's someone in your workplace, maybe it's someone you follow online, maybe it's a newsletter you subscribe to. If anyone is just blindly parroting these points, ignore them. Right. And if this comes up as a discussion around AI implementation at your company, you need to educate people. All right. You don't need to send a my podcast or I'm not going to tell you, go promote my stuff, but just go be like, hey, here's the study. Go read it.

It's 26 pages. takes an hour of analytical thinking. And here's the thing and why this is important. Yeah, we talked about hundreds of billions of dollars of fluctuation on the stock market. But more than anything, this is impacting real organizations, real companies' AI efforts. And that's what I want to end with. This study is going to cause more fence sitters. right people that were yeah we we have our AI pilot and they're going to be like oh look at this

study let's cut it or we're not going to extend it we're not going to go from pilot to production nope it ends here there's literally going to be probably hundreds or maybe more of large enterprise organizations that are just going to read the headlines they're just going to read the social media posts right and they're going to make their decision on that and they're going say it's not worth it. This is a bubble. It's going to burst. We got to stop. Look at this MIT report. Don't fall victim to that. Take advantage. So much of what's happening in the AI business world right now, it's first mover's advantage, right? That's long gone by now. But now, you're going to have some new fence sitters. You're going to have some companies that maybe had

successful pilots but just didn't know how to measure ROI. And they're going to be like, oh, well, looks like it didn't work. Take advantage. When others pause, you can go forward. I hope this was helpful, y'all. So no, 95% of AI pilots don't fail. But 95% of people who believe this study, I wouldn't be trusting them with business decisions.

And, you know, 95% of people who actually believe just the headlines, I wouldn't, I wouldn't just, let's let's add it there all right but actually no let's not end it there so if you repost this story there was so much i couldn't get to my goal was to keep this hot take tuesday uh under 35 minutes i'm going to make it there all right but there was so much information that i couldn't include i literally had so much research so many notes i literally made a small little website uh inside uh google jemini canvas with so much more information that i literally just couldn't get to All right. So if you want access to that, and that includes that chart that you should probably show your organization, right?

Don't spend 10 hours going to find all of that information. I'll just give it to you. So go repost this on LinkedIn. So if you're listening on the podcast, thank you for your support. We always put in our show notes. The LinkedIn post, so this goes live on LinkedIn. So just click that repost. And I will share this long website with you as a recent. source. So make sure you go do that. And then after that, if you haven't already, make sure you go to your everyday AI.com. We're going to be giving you the too long didn't read version of today's show. But I hope it was helpful. So thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks y'all.

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