Ep 648: How 74% of Enterprises Get Real AI ROI While Pundits See Failure

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.

Is it AI failure or AI success? I mean, yesterday OpenAI announced it has 1 million business customers, making it the fastest growing enterprise platform in history. Also this week, AI bedrock NVIDIA became the first company ever to be valued at $5 trillion. But, but a few months ago, a viral MIT study claimed 95% of all AI pilots are complete failures. And this week, a famous investor predicted that the AI world would bust, which sent global stocks tanking due to AI fears. So is Enterprises are left scratching their collective heads because both of these things can't be true. So which is it? Is enterprise AI a massive success or a catastrophic failure waiting to bust? Well, a new 3-year AI study out of Wharton looked at 800 companies, and it just gave us the definitive answer. And it reveals a truth almost no one is talking about in That's exactly what we're going to be talking about today on Everyday AI. What's going on, y'all? Welcome. If you're new here, we do this every single day. Everyday AI is an unedited, unscripted livestream podcast and a free daily newsletter helping everyday business leaders like you and me make sense of all this AI, this dichotomy, all these studies and stats pulling us in different directions, and it helps us grow our companies and our careers. So if that's what you're trying to do, it starts here with the unedited, unscripted livestream podcast, but To take it to the next level, go to our website, youreverydayai.com. There, make sure to sign up for the free daily newsletter. We're going to be recapping the highlights from today's podcast as well as all of the other AI news that you need to get ahead. But let's just get straight to the good stuff. New Wharton study showed that 74% of enterprises are getting a real return on AI when it comes to generative AI. So on today's show, we're going to spill the brutal honest truth about how those 74% of winners are getting a real ROI. We're going to explore the massive disconnect between the AI failure narrative and the AI boom narrative, and we're going to go deep, uh, on that new 3-year Wharton study of 800 leaders that cuts through the noise. So yeah, uh, we shared about this in our newsletter right when it came out last week, but you need to go read it if you haven't already. If you're listening to the podcast, feel free, put me on pause, come back. It takes I don't know, maybe an hour to read. It's not too long. But this is the, the name of the report is Accountable Acceleration: GenAI Fast Tracks into the Enterprise, Year 3 Full Report. So this is from the Wharton Human AI Research, and this is the third year that they've done this. So it is a 3-year tracking study, and that is important. All right. And it is of 800 enterprise decision makers, and it reveals that 74% of those 800 enterprise decision makers have reported a positive ROI on GenAI. All right. So I think we can put that piece to truth. Is there an ROI on GenAI? I think this is probably, uh, at least a top 5, top 10 study, um, of the last couple of years, maybe one of the top studies of 2025 that definitively answers that question. Yes, enterprise decision makers at a whole are seeing an overwhelmingly positive return on investment on generative AI. But there's a gap between the headlines and the on-the-ground reality that has never been wider. All right, and we're going to explore that, uh, as we get into the details of this study. But why is it even a debate, right? Why are we having a conversation on is there an ROI on generative AI? I mean, you've used it, right? Like if you know what you're doing, right? If you're using the right model, the right mode, basic prompt engineering techniques, right? You've seen large language models with, you know, 99.5% accuracy do the jobs that we humans do, but like 10 times faster, right? And around the clock and without coffee breaks. So why is there still this ongoing tug of war on is AI worth it or not? Well, I think there's money to be made. That's why. And the, this failure narrative really took off with this MIT's viral study back in the, in the late summer that claimed 95% of enterprise AI pilots fail. We're going to pick that one apart a little bit more. So that's kind of the failure narrative. And then we have the success narrative that we just talked about. OpenAI just announced 1 million business customers, which is a mind-boggling amount of business customers in an AI platform. And then we had this new narrative, the skeptic narrative. So from The Big Short fame, Michael Burry is betting more than $1 billion, right? Putting up the cash, putting the money where his mouth is, that AI stocks are massively overvalued and betting against AI stalwarts like Nvidia. So Those are, right? Michael Burry's a well-respected investor. MIT is a well-respected organization. So why the divide? Well, there's money to be made on the other side, even on the Big Short side, right? After that story came out, a lot of these stocks went down 2 to 3%, which is a ton. And if you're putting money on it, right, there's money to be made there just by causing a media firestorm. The same thing with the Deepseek saga that wasn't, that was a big fluke back in January, right? The market cap of the top 6 US companies, all AI companies, took a multi-trillion-dollar valuation hit over the course of a couple of days. Just so happens the parent company of Deepseek, specializes in algorithmic trading, uh, in kind of shorting stocks. So there's money to be made by being a skeptic. And even on the MIT side, I'm going to get to that in a couple of minutes. But we have to get down to this Wharton study. And yes, it's gotten to the point now where these majority of studies, you have to ask who commissioned them, who worked on them, and what is their methodology. Right. Another one that's been making the rounds recently. Maybe I'll have to do a Hot Take Tuesday on this one from Menlo Ventures. Right. Very well-respected venture organization. But they did. Yeah. Everyone's, you know, their, their big finding, you know, was based on talking to a couple dozen people and it showed Anthropic skyrocketing, OpenAI dropping. And oh yeah, Menlo also has, I think, a $100 million venture with Anthropic. So yeah, it's going to show that. But you have to always look at a study's methodology. And this Wharton study did it the right way, right? It's a 3-year tracking study of 800 US senior leaders. So it's not a one-time poll. And this is from companies that have at least 1,000 employees and $50 million in revenue. And the work was conducted by Wharton Human AI Research and GBK Collective in July of this year. But the top finding and what's grabbing headlines in all the right ways is that 74% of enterprises in this study are already reporting a positive ROI from generative AI. So from the use of large language models in their organization. And the other big one is usage has gone mainstream. 82% of leaders use it weekly and 46% use it daily. Right. And those numbers have more than tripled since the first year of the study. Right. So first year of the study, Eh. AI moves too fast to follow, but you're expected to keep up. Otherwise, your career or company might lag behind while AI-native competitors leap ahead. But you don't have 10 hours a day to understand it all. That's what I do for you. But after 700+ episodes of Everyday AI, the most common questions I get is, Where do I start? That's why we created the Start Here series, an ongoing podcast series of more than a dozen episodes you can listen to in order. It covers the AI basics for beginners and sharpens the skills of AI champions pushing their companies forward. In the ongoing series, we explain complex trends in simple language that you can turn into action. There's 3 ways to jump in. Number 1, go scroll back to the first one in episode 691. Number 2, tap the link in your show notes at any time for the Start Here series, or you can just go to starthereseries.com, which also gives you free access to our inner circle community where you can connect with other business leaders doing the same. The Start Here series will slow down the pace of AI so you can get ahead. A couple business leaders had their toes on it. Now, 82% of those surveyed are using it weekly. And here's the interesting part. 72% of companies say they now formally measure generative AI's return on investment, which I found actually a fairly high number. If I'm being honest, I thought the 76% was low, but I guess that makes sense if 72% of companies can only formally measure it and 76% say they do find a positive ROI, right? There's maybe a slight disconnect there, but I was actually shocked that I think 76% or sorry, 74% getting the positive ROI is a little low. And the 72% measuring, I thought was actually high, right? Maybe they're just fibbing a little bit, but it's actually higher than that when you start breaking it down by sectors, right? In tech, 88% saw a positive ROI. In banking finance, 83%. Right. Slower sectors like retail lag behind, and I think brought the overall average down quite a bit, obviously. But there's a big perception gap, and we're going to get into that a little bit later when you talk about the C-suite and VPs versus mid-managers. Now let's get into it, right? It's not Tuesday, but I might have a hot take in the tank. Hot take in the tank. That's hard to say, uh, when the second coffee hasn't hit. You might be thinking 74% found return on investment. That seems to conflict with a study that grabbed way more headlines. And this is one of the reasons, y'all, why I'm doing a dedicated episode on a study that you probably didn't see, hear, or read about aside from our newsletter, because almost everyone saw that MIT study, right? And study is in quotes, right? That 95% of AI pilots fail. 95%. So how do those jive? Well, let's quickly debunk that. It was, uh, hot garbage. So the 95% failure stat from the MIT study went crazy viral. As a former journalist, If it bleeds, it leads, right? MIT knew what they were doing. They were ultimately selling something. Um, and they knew that that was gonna grab a lot of headlines. And a lot of this, journalist's fault. They didn't read anything. They didn't. 'Cause if you read anything, if you have one, uh, one hundredth of a brain, you know this wasn't a real study. So this claim was based on just 52 interviews, 52 interviews with authors calling it only directionally accurate. So that essentially, uh, whoever was interviewing decided if it was accurate or not with 52 interviews, which I could do a more well-rounded and scientifically, uh, sound study, a more sound study in 10 minutes, literally, right? Could send it out to our Everyday AI email newsletter audience, a real study, real survey, and get something better than this. But hey, you get a stamp from MIT, and MIT's selling something obviously at the end, and the media just copied that big headline, uh, without reading the fine print and the actual explanation. And then panic outpaced the truth. And the truth was, well, the study's fatal flaw— it called, it called any AI pilots failures if they showed no profit, literally on P&L, no profit in just 6 months, which is— no one measures any kind of pilots like that, let alone AI pilots that no one understands. And this ignores the real timeline, which is usually 18 months to 3 years. And that is exactly what the Wharton study tracks. That's what enterprise transformation, that's the road it travels. But obviously the MIT report had a hidden agenda, right? Essentially they said, oh well, all these AI pilots are failing and you actually need agentic AI. And hey, MIT, has that covered, right? And so here, get, get this membership, uh, right to our, to our, uh, Agentic AI offering here at MIT. So yeah, it was a bad marketing study. Uh, it's enough on that. You can go, if you want more on that, go listen to episode 606, Inside MIT's Viral AI Study: The Reason Why 95% of AI Pilots Do Not Fail. All right. So got that hot take out. So a little bit more about how this study actually has unfolded over the years and what they've found. So essentially they've labeled it exploration 2023, 2024 was experimentation, and 2025 is accountable acceleration. So like I said, in 2023, they found that only 30— it was only 30% usage, right? Now in the 80%. And it's in— back then it said that leaders were fascinated but cautious and wondering if AI worked. Last year in 2024, they found a 72% usage. Spending was up 130% and they're asking, where's the value or where's the ROI? Right? And then this year they found it because they found 74% reported ROI, 82% usage. And then they're asking, well, how can we scale what works? Spending is increasing. And here is the AI return on investment truth that no one talks about. All right. Boring productivity gains are what wins. That is where you get return on investment. All right. And I actually have a great— if I'm being honest, I've done this podcast 650+ times. I'd say probably one of my top 5 podcasts I'm going to tell you at the end that really breaks down this ROI myth and the steps that your organization needs to take. So make, make sure to stick around to the end and I'm going to tell you which episode to listen to. But the Wharton study found that boring productivity wins. That creates ROI on AI and moonshots fail. So it said that boring tasks like data analysis and summarization scored the highest. Essentially, they created this index, um, you know, looking at different skill types or different types of work, uh, to simplify it, right? And then they gave it a score and the boring tasks obviously had extremely high productivity gains or time savings, which obviously leads to ROI. But these specialized killer apps showed huge value. So this is numbers above 100. So, you know, such as legal contract review and HR recruitment. But then there were certain AI tasks that didn't do too well and didn't have great ROI index scores, such as 58% for deploying AI agents. So yeah, if you're shooting for the moon and if you're not, right, and you're trying to deploy, you know, 50 AI agents, yet no one at your organization has been trained on LLM 101. Yeah, that's going to fail. Moonshots are going to fail, especially when ongoing training and development is plaguing the everyday enterprise. So here's kind of the, the gist of the Wharton findings. Well, money follows proof, not hype, because they, they showed that 88% of leaders expect budget increases in the next 12 months. That to me comes as no surprise. Actually, I would have expected low 90s on that. That's what a lot of other studies show. But It's working, right? ROI is happening when you look at spending and when you look at, unfortunately, hiring is going down across the board. Not in every single use case in this study. I can't, right? This study is actually super in-depth. It is really good. Like I said, you need to go read it for yourself. It's very visual, so it's longer, but it's visual. It's quick. It's a quick read, but they did see that critically 11% of organizations are now cutting legacy IT and HR programs to fund AI, right? So cutting from IT and HR, especially now looking at today's business landscape, right, when a lot of companies are going through a high rate of turnover, right? Even when you talk about remote and hybrid work and how that increases the need for solid IT departments, they're cutting IT and HR and investing in AI. But 30% of tech budgets are for internal R&D, essentially just trying to build proprietary moats that rivals can't copy. But it's not going to work, and here's why. Because the data also shows that there is a enormous training crisis, which, oh, weird, I've been screaming about this from day one, quite literally screaming about this for 3 years ago, that organizations would gladly, right back in early 2023, were spending millions of dollars to try and fine-tune, you know, GPT-3.5, but they wouldn't even tell anyone how to use it, right? And everyone thinks it's Google, right? So you have literally talked to so many companies back in, you know, early 2023 spending 6, 7, multiple 7 figures trying to fine-tune these earlier models, you know, for tens of thousands of employees to use. Yet they didn't provide any training. It's assumed everyone knows how to use these things, right? And now when you, stack on the agentic scaffolding and all of the tool use that these models have. And now the fact that these models are agentic by nature, right? And they're models that can think and plan ahead. They, they reason, right? There's a training crisis because investment in training actually dropped by 8 points, which is not a good sign, right? And leader confidence in that exact training also collapsed by 14 points. Essentially, AI developments are happening too quickly and companies are too eager to spend money on the latest technology and spend time, you know, trying to implement it, but not actually training. So the more technology advances, right? When you think of the first version of ChatGPT or you think of, you know, GPT-3.5, right? Think of or, you know, even before that, or, you know, early 2022, 2023 models. It was a simpler time, right? And in theory, even though the technology was extremely groundbreaking in the first couple of quarters, it was technically way easier for people to use. Because like I said, your enterprise data for the most part wasn't connected. Obviously hallucinations were way higher and way more prevalent. So maybe it was a little more dangerous to use, but it was easier to use. Now, right, when everything's agentic by default, tool use by default, your company's data in by default, now it's harder. And, you know, now you have all these different modes and models, right? But 49% in the Wharton study cite recruiting advanced AI talent as their single biggest challenge, yet they aren't even training internally. Right? Talk about a paradox. You know, almost half of everyone says, yeah, we can't recruit anyone good at AI. Oh yeah, but we're not training anyone, right? We're not trying to learn, right? Call us. That's what we do, right? Get you, get you. Like I always tell people, right? And I'm not, I'm not trying to say this in a like, oh, you need to hire, you know, hire us to train your company. It's what we do, but your team needs a bunch of me's. That's what they need. I like, first thing when people hire, like hire me, I say, uh, hey, you need to find your people who are like me every single day. You need a team of people, especially enterprise organizations, those with, you know, thousands of employees. You need a team of people. All they do is they keep up with the latest AI technology, whatever you're playing with, right? Whatever you're using in production, you need to have people, uh, constantly, uh, receiving, collecting feedback. From your frontline users. You need to have people testing fallback models. What happens if the next model comes out, right? And let's just say you're using ChatGPT Enterprise, right? And GPT-6 comes out and it stinks and all your workflows are broken, right? You need to be having fallback kind of AI operating systems in place. You need to be training people around the clock because if you're not, One of your competitors is, and they're gonna be the ones that are definitely gonna leapfrog you. And the Wharton study actually says when, more on that in a second, because there's actually another problem, which is maybe a little scarier, uh, when we talk about, uh, human potential. Well, that in a second. Uh, this is more on the, uh, the hierarchy, uh, kind of the perception gap that paralyzes action. So the study found a massive disconnect. Said that 56% of VPs are highly optimistic about adoption, but only 28% of managers, half the percentage, the people that are actually using this on the front lines, share that optimism. So there is friction, and friction kills momentum. And again, literally had a whole episode dedicated on this back in 2023, y'all. Here we are almost in 2026. Hate to sound like a broken record. Uh, it's— this is going to continue. That gap, this perception gap, is going to continue to widen because so few organizations are taking AI implementation as a people management issue. Everyone's looking at it as a technical implementation. It is a people management, um, implementation. The reason why there's the gap, right? Because your VPs, your C-suites, especially for public organizations, they see AI capabilities when they get their hands on it and experience it themselves. Uh, they think shareholder, they think boardroom, they think profit. That's why they're optimistic. Mid-managers don't. They think, this is my job. This is job security. This is my role. This will make me redundant, right? Top-down AI implementation is going to fail every time. And the Wharton study shows this, that there is just friction and that friction is killing momentum. And maybe what's ultimately more troublesome is this skills paradox because the study found that 89% agree that AI enhances skills, right? It's going to make you better at whatever you do. So whether you're in data, finance, creative writing, advertising, uh, a STEM-related field, research, it doesn't matter. AI, if you use it correctly, if you understand the technology, it's going to make you better. But 43% of people simultaneously fear skill atrophy, right? So this kind of creates this vanishing ladder as AI automates the very skills baseline, sometimes junior level tasks needed for training and needed for individuals to practice their craft. And that brings up a new and tricky core challenge. Gaining productivity today by augmenting your skills with AI or augmenting your workflow with AI without sacrificing the human capability needed for tomorrow. I've talked about this at length. Sometimes the more you use AI, the dumber you might feel, right? And especially if you are really good at AI. That's why I sometimes have to take a step back. Right? That's why sometimes I literally open up a blank Word document and I type. Right? Yeah, because I'm in, you know, using different large language models. I use them all, all day. You know, 8 to— most days, eh, 6 to 10 hours. Right? Sometimes I need to step away and use my brain without AI. I need to write down some strategies. I need to do some creative writing, some brainstorming, some problem solving on paper. If you're not finding that balance, or your team or your organization, if you become overreliant, you're going to run into what the Wharton study found is this skilled paradox. And what this all has led to is two different types of companies. Emerging. People are looking at it. That rhetorical question I started the show with, is AI a failure or is AI a success? Well, you have your winners. They have clear metrics when it comes to ROI. They focus on back office automation, the boring stuff, and they have strong executive alignment. They're training their teams. They're not going top-down implementation. The losers, they're chasing shiny AI objects every day. Something new, new model, new goal, new priorities. They chase sexy, sexy moonshot projects, right? Trying different AI agents every single day, and all they're trying to do is chasing efficiency, chasing Productivity at all costs, chasing shareholder value, chasing stock price. And that is going to create that cultural resistance. And the divide isn't about technology, it's about culture, execution, and people. And Wharton, the study said this, said 2026 is when the winners will permanently separate themselves. So in the case where the MIT study measured 6-month, uh, movement on a profit and loss statement, the 3-year study from Wharton showed a productivity transformation. And the data is clear: 74% figured out what works while everyone else is debating what's real, what's fake. So those companies that have had clear data and they've been able to measure different tasks, different projects, different workflows, pre-AI and with augmented AI, they've already got it figured out. So the question that you have to ask is how far ahead are that 74% going to get? And are you truly part of that 74%? Has your company actually found positive ROI on generative AI? And is it enough? And are you radically transforming the way that your organization does day-to-day work? Because let me tell you this, let me drop one of my little buzzwords and phrases that I hate, and I've rewritten it, right? If your organization is talking about upskilling with AI and reskilling with AI, or if you talk about You know, um, someone using AI won't take your job, right? Or AI won't take your job, someone using AI will take your job, right? It's all wrong. All that is wrong. Upskilling with AI, reskilling with AI, you're gonna fail. That assumes you are going through similar steps and just sprinkling in AI when it seems convenient or when something can do it faster. That's not becoming an AI native or an AI-first organization. What's my word, y'all? If you've listened to the show, you know you have to unlearn. You don't upskill or reskill. You unlearn and rebuild as an AI native first organization. But it starts with ROI. It starts with reverse engineering your day-to-day tasks, your day-to-day workflows, your same SOPs that you've been running, your blueprint you've been running for 5, 10, 15, 20 years that's been successful. You gotta be ready to rip it up because if you are not truly part of that 74% that has actually found return on generative AI, ROI, Wharton says, and I agree, you're gonna get lapped. If you are just doing, uh, table dressing AI, right? And you're like, oh yeah, we gave everyone licenses and yeah, you know, we're writing emails way faster. Here's some fake productivity claims. You're going to get crushed. So here's our take on the study. Need to be investing in people over technology, right? The winners of the next decade won't be the ones with the best tech or using the best models. It's going to be a moot point, right? What model, what tech? It's all going to be good. It's all going to be fractional of a percentage better or worse. What will be completely different is the amount of human transformation that you pour into your company. Those are gonna be the ones who are gonna solve the people problem the fastest. So it's a race to manage change, build new skills, and redesign career paths, and redesign your team's literal roles for an AI-first era. So here's kind of, uh, my takeaway from the 5-step playbook of reading this Wharton study in looking at those companies that found ROI versus those that didn't, or those that found higher in different sectors, et cetera. Like I said, uh, the study is very granular. I'm trying to keep it high level, but here it is. 5 things. Number 1, you need to mandate formal ROI metrics. Get rid of vague goals, right? The study shows that 72% of winners formally measure Gen AI's impact with structured business-linked metrics. 2, need to prioritize those boring wins. Forget about the moonshots. Focus on productivity. Um, focus on measuring those boring tasks. Number 3, solve the people problem first. Don't buy the tech. Don't just give everyone, oh, here's ChatGPT Enterprise license, go. You need to train. You need to fix the training cost. The— you need to fix the training crisis because the longer it goes unaddressed, The wider that gap is going to be, and you're actually just hurting yourself. You might be better off if you didn't even try to implement AI in the first place than if you're trying to throw it out throughout your entire organization, mandate its use, and not actually train and teach people. 4, you need to bridge the perception gap. You have to align the optimistic C-suite, um, VPs with those people who are actually doing the work. You have to have those courageous conversations about, hey, what happens when AI works? And then 5, last but not least, you have to fix the vanishing ladder. Human minds and human potential can actually be fragile. And I think, all right, people always think that I'm just like pro-AI everything. I'm not. Sometimes you gotta turn the AI off. right? And you need to practice and refine and sharpen those skills that humans are still going to need. No one, no one knows what human skills are still going to be important in 5 years, right? When presumably, you know, we're all just orchestrating agents, but I assume we're still going to have to be able to use our brain. We're still going to have to have a certain level of taste in our domain. We're gonna have to still be able to use our brains, right? And we need to fix that problem. We can't just, um, kick everything and automate everything with AI, right? And just be a passive human in the loop. I hate human in the loop. We need to have expert-driven loops. Those are proactive. Those are ones where you're using your brain. Human in the loop implies that a lazy human is gonna check an AI's work. You have to fix that. All right. And hey, if you're feeling maybe inspired or you want to learn a little more, make sure go check out episode 628 because yeah, we go over what's the best LLM for your team, but the real gem, and I'll probably just create a dedicated episode on this sometime soon, the 7 steps to evaluate and create ROI for AI. So. Uh, not trying to brag, but I'm pretty sure our list, we covered a lot of stuff that Wharton found in their 3-year study, uh, and I think some things that are actually better and a little more refined. So make sure you go check that out. But that is a wrap. So let me just say this. If you're still scratching your head and wondering, is AI a failure or is AI a success? It's a rhetorical question. The question's been answered. The question has been answered by any large-scale enterprise study with solid methodology, right, that is put together in a way that any large-scale study should be. You can't deny the impact both today, tomorrow, and next quarter, next year that AI will have. So ignore the pundits, Ignore the, uh, the marketing and advertising that's dressed up as studies to dissuade you, uh, from following what you know to be true. Generative AI, if you invest in the people, invest in learning, it is a transformational technology that we've never seen ever, right? There's a reason why the CEOs of all the big companies say it's as big as fire or electricity. It is absolutely transformational. So we laid out how to get an ROI. So no more scratching your head. It's time to roll up your sleeves and get to work. And how do you do that? Well, you keep tuning in. So thank you for tuning in today. Uh, if you haven't already, if this was helpful, actually, please repost this on LinkedIn. So if you're listening on the podcast, thank you as always. Please, if you're still listening at this point, do me a big favor. Click that follow button, subscribe to the show on Apple Podcasts or on Spotify. If you're listening to this live on LinkedIn, click that repost button, share this with your network. I want to keep this thing always free, unbiased, and to help everyone, but I can only do that if you do those things. If you subscribe to the show, if you share this with others, repost this on the social, on the socials, and then you gotta go to our website, youreverydayai.com. We're gonna be sharing the Wharton study obviously in today's newsletter, as well as throwing a ton of more information that we couldn't get to that I I think it's going to be really helpful. So thank you for tuning in. Hope to see you back tomorrow and every day for more Everyday AI. Thanks, y'all.

And that's a wrap for today's edition of Everyday AI. Thanks for joining us. If you enjoyed this episode, please subscribe and leave us a rating. It helps keep us going. For a little more AI magic, visit youreverydayai.com and sign up to our daily newsletter so you don't get left behind. Go break some barriers and we'll see you next time.