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Generative AI in 2026: Concrete Impacts and Strategic Insights for Business Leaders
Generative AI is no longer an emerging concept—it's impacting the mechanics of work, investment decisions, and competitive positioning right now. The latest data shows nearly 900 million weekly ChatGPT users, and 80% of companies deploying AI agents capable of performing autonomous actions. Rather than approaching AI as a vague trend, smart organizations are examining how its adoption demands new operating systems, training regimens, and investment strategies to stay competitive.
This article organizes actionable insights from current generative AI deployment, with specific benchmarks, process changes, and measurable business outcomes designed for decision-makers evaluating where to focus this year.
Generative AI Adoption: Growth Outpacing All Prior Technologies
With generative AI reaching 100 million users in two months—a milestone that took eight years for the Internet—this field's business penetration is unmatched. Forty percent of working-age Americans already use generative AI just two years after the launch of ChatGPT, compared to the Internet’s seven-year adoption ramp-up.
For companies, this means AI capability is no longer a novelty. Over 92% of Fortune 500 firms leverage OpenAI tech; 80% now deploy AI agents capable of acting without direct human input. Enterprises not just talking about AI, but actively running entire operations through large language model (LLM)–driven platforms, increasingly see this as table stakes rather than differentiation. Organizations must now choose their “operating system” much as they once selected Windows, Mac, or Linux. Most leading LLM suites, including ChatGPT, Gemini, Microsoft Copilot, and Anthropic Claude, now offer dedicated enterprise environments supporting real-time team collaboration and data integration.
Operational Value: Beyond Chatbots to Full-Scale Automation
Generative AI systems have matured from basic chatbots into operational engines. The Anthropic Claude desktop application, as an example, lets users automate workflows including computer file access, code execution, and secure system logins—all through their AI assistant. This means tasks once reserved for IT teams or specialized staff can now be automated by employees without technical backgrounds.
The shift is from question-answering to autonomous multi-step execution, such as scraping research, analyzing documents, and preparing deliverables in minutes rather than hours. The transcript highlighted a once 100-hour information-synthesis workflow reduced to under 10 minutes with high factual accuracy, demonstrating concrete returns not just in speed, but in knowledge work quality.
Measured Business ROI: Studies Show Substantial, Quantifiable Gains
Skepticism around AI pilots has faded as longitudinal data arrives. The International Data Corporation reports a $3.70 return for every $1 invested in generative AI, with top performers exceeding $10 returns per dollar. Snowflake’s ESG survey found that 92% of enterprise early adopters already break even or profit from existing investments, and 98% of prior investors plan to expand AI budgets.
Anthropic’s study of 100,000 live conversations revealed 80% reductions in task completion time, and global PwC research showed 92% of daily AI users reporting measurable productivity gains—an indicator that headcount can be redirected or reduced, and process quality enhanced.
Economic Impact: Scaling AI, Not Experimenting
2026 signals a shift from experimentation to full-scale execution. Companies sticking with lengthy pilots or slow rollouts are quickly being surpassed. Forceful early adopters experience triple the improvement in operating profit impact compared to laggards—a trend supported by multiple large-scale independent studies. Half-measures no longer suffice; organizations must now move methodically but quickly, quantifying returns and integrating AI at the department and enterprise levels.
Software platforms used by tens of millions—including Salesforce, ClickUp, and HubSpot—now natively include AI agents for core business processes, automating customer management, analytics, and communication tasks in real time.
Talent and Workforce: Workforce Dynamics Are Shifting Fast
Entry-level job opportunities are declining sharply. In 2025, only 30% of graduates secured jobs in their field—a 44% drop in entry-level hiring compared to three years prior. Meanwhile, 62% of employers now require AI knowledge, but more than half of graduates report lacking practical AI exposure in their degrees. Companies are compensating by tripling down on automation and investing in robust AI operating environments.
Leaders must recognize the need to not simply “upskill” but to restructure roles and training around AI-native processes, eliminating legacy approaches that slow adaptation.
Strategic Recommendations for 2026
Choose and implement an AI Operating System: With LLMs functioning as organization-wide engines, determine which platform aligns with existing data, processes, and growth objectives.
Prioritize Measurement and Benchmarking: Replace speculative pilots with robust internal measurement frameworks to track ROI and competitive positioning.
Invest in Community and Training: Build knowledge-sharing and rapid onboarding processes. Success depends on employees’ ability to unlearn outdated habits and master new workflows with AI as a collaborator.
Move from Experimentation to Execution: Organizations delaying full commitment risk falling irreparably behind. Adopt and scale AI tools now to close widening productivity and profitability gaps.
Conclusion
Generative AI’s compounded effect is measurable, rapidly expanding, and past the point of theoretical discussion. The data validates that the right investments and operational redesigns produce exponential returns. The pace of change means even recent expertise risks obsolescence—an AI-native, measurement-driven strategy is essential for business leaders planning for 2026 and beyond.
Topics Covered in This Episode:
- Generative AI Basics and 2026 Impact
- Explosive Growth of Large Language Models
- AI Adoption Rates in Enterprises
- AI Agents and Operating Systems Overview
- History and Evolution of Artificial Intelligence
- Transformer Architecture and Model Breakthroughs
- How Large Language Models Work
- Modern AI Capabilities: Multimodal Tools
- Quantifying ROI for Generative AI Investment
- Workforce Disruption and Future Job Trends
- Scaling AI: From Pilot to Enterprise-Wide
- Urgency for AI Upskilling and Competitive Advantage
Episode Transcript
Jordan Wilson [00:00:16]:
Have you ever felt overwhelmed by AI? Like, there's certain aspects of artificial intelligence that you barely understand to begin with, yet you're expected to use it, and it's changing every day? Understand where you're coming from. Chances are if you're listening to this, you probably have a full time job where you're expected to leverage AI, but you haven't had much formal training, if any, at all. And you definitely don't have extra hours in your day to learn. Oh, and, yeah, I mentioned that thing where AI is changing literally every single day. And when I say I understand the challenge, I mean, it, I mean, literally it's my only job to use, to build with and to teach AI every single day for ten to twelve hours. And that's all I've been doing for the past three years. And even I find it hard to keep up, but don't worry. That's where this new series comes into play.
Jordan Wilson [00:01:16]:
It's called the start here series and whether you're a beginner, extremely confused, or you're someone that uses AI every single day, yet you're looking to double down, this new series is for you. Because one of the most common questions I get asked all the time is, where do I start? Or someone saying, hey. I know a lot about large language models, but I wanna know more about the creative side. Where do I start? To tell the truth, I haven't had a good answer until now. That's why we're kicking off this start here series. So for those keeping up keeping up live, right, January is a time when I think most business leaders are setting new goals or trying to double down on good habits. So I know a lot of you are really trying to put in that extra effort here at the beginning at the beginning of the year to improve your understanding of AI. So that's why we're gonna be releasing probably two of these episodes a week for the next five or so weeks.
Jordan Wilson [00:02:17]:
So not necessarily abandoning our normal daily schedule if you're an avid listener to the program. Don't worry. We're just flexing a little bit here in the beginning of the year to try to help both beginners and advanced users alike get started and also get caught up. So we're gonna be going over the basics, like generative AI today. Like, what the heck is it? To simplifying more advanced techniques, like AI agents and explaining, explaining things like the model context protocol or what the health is a Ralph Wiggum loop. Right? So whether it's concepts that we're zooming out and going back in time or things that are happening literally today and helping you put them in perspective. The start here series is gonna be for you. So we're gonna be going over, you know, the from the correct way to launch a successful AI pilot to how to measure ROI to how to read and understand benchmarks and choose which AI system is best for you or your company.
Jordan Wilson [00:03:17]:
We're gonna be tackling it all here in the start here series. You ready? All right. Let's get into it. What's going on y'all. If you're new here, my name is Jordan Wilson. In everyday AI, it's for you, and we've been doing it for a very long time. It is an unedited, unscripted daily livestream, podcast, and free daily newsletter helping everyday business leaders like you and me not just keep up, but how we can leverage all the good stuff, make sense of the nonstop updates, and get ahead to grow our company and our career. So if that sounds like what you're trying to do, maybe this is episode number one for you.
Jordan Wilson [00:03:52]:
Maybe it's episode 700. It doesn't matter. We have something new and fresh for all of you. Yeah. A new URL to throw out there. Ready? So if you're interested, go to the go to starthereseries.com. That is starthereseries.com. So if you wanna keep up with this particular series, go do that.
Jordan Wilson [00:04:10]:
You're gonna get a, an invite link to sign up for our free community, and you will be inserted directly into the start here series, kind of onboarding flow. So as we add to this, there's gonna be more and more shows in there in that space, the dedicated space in our community. So if you wanna keep up with all the shows in this series, connect with other leaders. I mean, literally, we have industry leaders in our free community, right, that you can learn from, go connect with. Make sure you go to that, website. Alright. Now we have that out of the way. Just to let you know, these shows are gonna be a little quicker.
Jordan Wilson [00:04:47]:
Alright. This one's, you know, might be thirty minutes, but most of these start here shows are gonna be about twenty minutes. I wanna keep them very fast, very factual, which you know is gonna be a little hard for me. Alright. So, without further ado, let's get into it. Let's talk about generative AI, the basics, how it works, and why it matters in 2026 more than ever. So let's start with the pace in the reality. Well, nothing has spread this fast ever.
Jordan Wilson [00:05:18]:
Nearly 900,000,000 people. The last confirmed was 800,000,000, but I know the stat it's nearly 900,000,000 people are using chat GBT weekly. I mean, that's more than the entire population of Europe. And chat GBT, let's talk about an explosion. It reached a 100,000,000 users in its first two months. To get to that same number. The internet took eight years. So if you want to talk about a technology that you can't ignore, think about how commonplace the internet is in our day to day lives.
Jordan Wilson [00:05:54]:
You can't do too much without it. Right? Especially if you're a knowledge worker, you can't do too much without it. And just two years after launch, 40% of working age Americans are using generative AI and the internet took seven years to reach that same level. So the generative AI technology, which is what large language models are kind of under the umbrella on. It is the most explosive growth of any technology ever. And companies are using it, whether you have realized this or not. Right? This conversation has changed a whole lot over the past few years. But nowadays, using AI is table stakes.
Jordan Wilson [00:06:43]:
Like, you have to. You don't have a choice. Right? Maybe three years ago, it was a competitive advantage. You know, it was kind of novel, you know, to be using generative AI and large language models back in, you know, '21, or in 2022 when ChatChiuVT came out. It's not anything special today. You have to be using it today. Right now, about 80% of companies are even deploying AI agents that take actions, not just answer questions. So, not only are 92% of Fortune 500 companies, you know, as an example using OpenAI's technology, that's just one company.
Jordan Wilson [00:07:22]:
Right? But 80% of companies are even deploying AI agents. All right. So yes, the space moves quickly and yes, we are going to zoom out, but I first just wanted to set the table, so to speak and let you know where we are. Just about right. And depending on where you're you're tuning in from. Right? Yeah. We have listeners from all over the world, so thank you for that. But here in The US, that's usually the the lens I'm talking through.
Jordan Wilson [00:07:47]:
I'm from Chicago. Hey. Good to meet you. Right? A lot of you are starting here with this episode. Everyone's using AI here in The US. Every single company. Right? You don't see a company anymore. I haven't met a company in a very long time that's not using whether officially or unofficially.
Jordan Wilson [00:08:05]:
Right? Yeah. There's the whole shadow AI, you know, and, and some of those things, but every company is using AI. So it's changed though. And I think as strange as it is, I think a lot of people still have a very 2022 view of AI. Right? Let's just use ChatChippity because that is the most widely, used AI tool in the world. Right? Obviously, Microsoft Copilot, very popular in the enterprise. Google Gemini, Anthropic Claude. Right? Those are what I, refer to as the big four.
Jordan Wilson [00:08:42]:
So if you ever hear me reference the big four, I'm not talking about consulting. I'm talking about those, four companies. But, now they're not just these friendly chatbots anymore. These AI systems are operating systems in and of themselves. I've been saying this for years. Companies need to make a decision. You need to move all your operating your day to day operations into a large language model sooner rather than later. Right? We'll probably explore that more in a later, start here series, the exact process of, choosing and setting up an AI operating system.
Jordan Wilson [00:09:17]:
Sound official term. It's something I think I've made it up a couple of years ago, and I've been running with it ever since, the AIOS. Right? But in the same way, companies, you know, in the nineties or eighties or early two thousands depends. Right? They gave all their employees computers at some point, and they had to make a a choice. Right? Are we a a Windows organization? Are we a a Mac organization? Are we a Linux organization? You have to make the same choice. What is your company going to be? But now in a few clicks, your entire organization's data can be accessed instantly in these AI operating systems. Right? ChatGPT, they have a Teams and an enterprise plan. Gemini has a business and an enterprise plan.
Jordan Wilson [00:09:56]:
Obviously, Microsoft three sixty five, Copilot has an enterprise plan. Claude has an enterprise plan. These are for Teams now, and they bring your data in instantly. And Teams can collaborate, right, seamlessly. So with no knowledge even right now. Right, here's here's some stuff. So some new stuff. Right? Yes.
Jordan Wilson [00:10:14]:
I know this is gonna age, right, if you're listening to this in June or July, but, you know, Anthropic just came out with a tool for their desktop program called, Claude Cowork. So with no knowledge at all, no tech know how, you can use a desktop program. Let me say this again. You can use a desktop program, using their Opus 4.5 model, one of the most powerful models in the world. It can control your computer. It can access your file system. It can browse the Internet in your browser that's logged into everything. Are you still running in circles trying to figure out how to actually grow your business with AI? Maybe your company has been tinkering with large language models for a year or more, but can't really get traction to find ROI on Jenna AI.
Jordan Wilson [00:11:05]:
Hey, this is Jordan Wilson, host of this very podcast. Companies like Adobe, Microsoft, and Nvidia have partnered with us because they trust our expertise in educating the masses around generative AI to get ahead. And some of the most innovative companies in the country hire us to help with their AI strategy and to train hundreds of their employees on how to use Gen AI. So whether you're looking for chat g p t training for thousands or just need help building your front end AI strategy, you can partner with us too, just like some of the biggest companies in the world do. Go to youreverydayai.com/partner to get in contact with our team, or you can just click on the partner section of our website. We'll help you stop running in those AI circles and help get your team ahead and build a straight path to ROI on GenAI. A large language model can do that. It's not just a friendly chatbot.
Jordan Wilson [00:12:00]:
That is the work us humans do. It can access my computer. It can access, the terminal. It can code. It can access all my files on my local machine. It can go out and log into my email, log into any system, and access any data that I tell it to. That's where we are now. Today's generative AI is much more than friendly chatbots.
Jordan Wilson [00:12:22]:
But let's zoom out to understand, because I think for whatever reason, people think of AI as a very new technology, which I think is one of the reasons why there's a high distress. The other reason why is most people don't know how to use it. And you see someone share some, you know, someone puts a screenshot of something from Chad GPT on on Facebook or LinkedIn and someone usually that has no clue what they're doing. And it's, an example of a large language model getting something very simple, very wrong. FYI, anytime you see that I've been doing this for a long time. 99 times out of a 100, it is human error. That human has no clue what they're doing. Right? But AI is not new.
Jordan Wilson [00:13:00]:
AI has been around in some way, shape, or form for decades, up to seventy years. So the term artificial intelligence was actually coined in the fifties, and we've been kind of chasing this, you know, dream ever since. So there were early systems, the expert systems in the seventies and the eighties, right, used in a lot of different sectors. I think banking, mortgages were some of the bigger ones, obviously, in health care. But even early systems in the seventies, were shown to be able to diagnose infections better than most doctors. Right? And we obviously, with the advent of large language models, we have a lot more studies that show that. So what is changed? It's not the ambition. Right? It's finally there's the technology has evolved a lot.
Jordan Wilson [00:13:46]:
You know, there are a couple breakthroughs in the twenty tens, but, obviously, the big one that most people are familiar with is kind of the advent and the, proliferation of the transformer, that has led to chat GBT. So the biggest thing that's changed as well, the technology and the compute needed. Right? In the same way, how to, you know, access the Internet, you know, in the early days, you had to have a a computer bigger than a car, right, and connect it up to all these cables. And now your cell phone obviously can. So the same thing is true with AI. Right? These systems that used to be very slow and very big and very expensive. Right? It's not like that anymore. The technology has changed.
Jordan Wilson [00:14:28]:
It's gotten exponentially, faster, more affordable, and more powerful. But probably the biggest breakthrough that led us to where we are today is in 2017. So, that's when Google researchers published the very popular research paper, attention is all you need. And it quietly changed the trajectory, technically not just of the artificial intelligence technology, but I would say of the world. Right? The other thing I didn't really touch on artificial intelligence right now, and maybe we'll tackle this in another start here series. It has become, I'd say, probably more important than oil. It's probably become more important than, country's military. Right? Whether you realize it or not.
Jordan Wilson [00:15:17]:
Right? Maybe if you come back and listen to the show in 2027 or 2028, you'll be like, oh, okay. This weird guy was right. It is more important. Right? That's why you're seeing, a lot of geopolitical tensions heightened, right now around the technology itself. Right? Essentially, you know, there's this thing called AI. There's this thing called AGI or artificial general intelligence, and then there's this thing called ASI, artificial superintelligence. Right? That's when you start to get into the Terminator thing. But, we'll tackle this in a later start here series show.
Jordan Wilson [00:15:48]:
But, essentially, the first, you know, country or company to develop AGI, artificial general intelligence, that is big. Right? Anyways, it started with this paper, and that has kind of changed the course of the world and the business world, especially. But that paper is, essentially introduced the t in GPT. Right? Generative preformed transformers. But the t in that paper was introducing the transformer architecture, which is the engine behind almost every single AI model today. And OpenAI was kind of the first to run with it. Right? You know, a lot can be said with the current race between OpenAI and Google. They're definitely the front runners in the AI race.
Jordan Wilson [00:16:34]:
You know, Claude, Anthropic's Claude is is right there as well. But OpenAI was the first to take this, research and really productize it. So they built GPT one in 2018, GPT two in 2019, GPT three in 2020, which is actually when they started releasing it to the public, with a lot of earlier software programs. That's when, you know, myself and my team, we started using it daily in 2020, two years before ChatGPT came out. But most of the world kind of figured out about this big breakthrough that no one saw coming that originated in the 2017 Google attention is all unique paper. It really came to life in November 2022. That was the chat g b t moment. That is the line in the sand that I think has changed, the course of not just the business world, but also the information world.
Jordan Wilson [00:17:27]:
So how the heck do large language models work? All right. So you're like artificial intelligence has been around for a long time. Right? And then there's this important thing called the transformer. Well, and then the transformer, led to large language models. So here's kinda what they are and how they work. Right. So they predict, essentially, these large language models have been trained on the history of the Internet. Right.
Jordan Wilson [00:17:53]:
There's a lot of lawsuits that are gonna come from that. Right? Because people are like, well, what about copyrighted information? Yeah. It's gonna play out in the course eventually. But these large language models essentially scrape not just everything on the Internet that we use, the open web, but they scrape everything on the closed web, offline datasets. Right? Essentially, the entirety of human history and human knowledge has been scraped by these large language models. Then you have very smart humans, at these big companies that then train the models, and they go through a process. I'm not gonna get too dorky. It's called reinforcement learning with human feedback, but they train these models.
Jordan Wilson [00:18:32]:
Right? So they say, hey. When, you know, someone asks a question about, you know, topic a, here's what a good output would be. Here's answer a is good, answer b is bad. Right? And so they go through this reinforcement learning process to train the models, and these models are trained to be a helpful assistant essentially with this, their training data. Alright? And this is how the earlier, models worked. Today's models much more sophisticated. Right now, if you ever hear, a term like scaffolding or, agentic, right, today's large language models are night and day difference, than the large language models that set off the chat g b t, craze. Right? Those were kind of just I call them old school transformers.
Jordan Wilson [00:19:18]:
Right? Today's models, they're technically still transformers. Right? But I call them, reasoners. Right? A lot of people call them reasoners or logic based models. So they're much different now. There's a process that, they can kind of mimic human logic. So, yes, they are still technically, next token prediction machines, but there's essentially a, step by step problem solving on top of that prediction engine that mimics human logic. Right? And you can see it think, step by step and go through and, you know, use these different tools it has at its disposal. It might run code.
Jordan Wilson [00:19:54]:
It might go you know, before it responds to you, these models that think now. Right? They used to just spit out very quickly and answer. Right? The earlier models, the GPT two, the GPT three were really bad. Right? The GPT three five that launched with, you know, CHAD CBT a little bit better. It was at least coherent and usually accurate. Right? But today's models, extremely impressive. Right? They tackle problems like experienced human. Today's models score better on offline IQ tests than 99% of humans.
Jordan Wilson [00:20:25]:
They are literally in the top 1%. Right? They are at genius level scores, taking offline, IQ test that they have not been trained on. Alright? So that's where, kind of large language models are today, how they kind of work. Right? So scrape data, from data sources. Humans train them, to be helpful assistance for users. A user ask a model, a query. The model might just pull that out from its training data, or it might decide, hey. I need to use some tools at my disposal.
Jordan Wilson [00:20:58]:
I should probably go fetch something on the web. It seems like the user is asking about something very current. Maybe I should use a data analysis. Maybe I should run some code under the hood to get to this, to get to this answer. So, yes, this, kind of this scaffolding or the tools that models use, help it hallucinate less, and just provide much more robust and impressive outputs. So, the scale is hard to comprehend. Right? We're talking about billions and trillions of parameters. So a a parameter essentially a learned pattern that these models have gone through in their training data.
Jordan Wilson [00:21:37]:
So more parameters just means that there's more patterns the models can, recognize and use. I like to think of it's it's a connection in its big neural network brain. So GPT three had a 175,000,000,000 parameters. GPT four reportedly had over, about almost 2,000,000,000,000 parameters. And a lot of today's newer models, we don't know how many, but we've seen reports that they're anywhere from two to 4,000,000,000,000 parameters. Alright? So it's just in in insane amounts of of data. And then there's also something called the context window, and even that has exploded. And that's essentially how much a large language model can remember before it starts to forget.
Jordan Wilson [00:22:17]:
Right? And we do in our in our free, you know, prime prompt polish core, the course, that you can access in our community, by the way. So you can just go to starthereseries.com, sign up for our community, and you will also get instant access to that free, prompt engineering, context engineering course that's been taken by more than 15,000 people. Anyways, a tote, context window is extremely important, and that's another thing that has scaled alongside with models because the earlier versions, they would forget things almost right away. Right? Today's, you can work with them sometimes depending on how you have it set up. You can work with them for hours or days until they start to forget things. Right? There's obviously, some, auto compaction. You you know, these models are starting to work, kind of automatically compacting this information, So it keeps a longer, kind of memory going. So generative AI isn't just text anymore.
Jordan Wilson [00:23:15]:
Right? So large language models started as mainly text based models, but now they're multimodal by default. There's a lot of different, kind of techniques, but everything kind of now falls on the under this generative AI umbrella, right, where traditional, artificial intelligence was more deterministic. Right? It was based on more, decision trees. Right? If else or if this then logic. Right? So, traditional AI. Right? The expert systems in the in in the eighties and used through the nineties, etcetera. A lot of it was more deterministic. Right? It was very rule based.
Jordan Wilson [00:23:51]:
That's not large language models. Right? You know, people say hallucinations, or creativity is a feature, not a bug. Right? That's what this next token prediction. And what that means is, right, like, if think. If a little kid goes to touch a stove, what do you say? You say, oh, be careful. That's hot. Oh, that's hot. Don't touch that.
Jordan Wilson [00:24:14]:
Be careful. Right? That's what you would naturally say. Right? Humans, I think, very much are like large language models. So when people say, oh, large language models, they can't they can't reason. Well, they they kind of can because it's based on the reasoning of millions of humans in theory. Right? But the same thing can be said for different type of models. On the image side, you have very popular, you know, AI image generators as well. You know? Three, four years ago, they were very bad.
Jordan Wilson [00:24:44]:
They didn't even look like you couldn't even tell what something was. You know? So some of the earlier versions, like, you know, DALL E two or some of the earlier versions of Midjourney, today's AI models, You can't tell the difference. Right? I've mentioned this before. Used to do a lot of photography. I've taken more than a million photos. I've owned like, I don't know, eight different DSLRs, you know, professional cameras. I can't tell the difference. And if, and if anyone tells you today, I think maybe three months ago, you could tell the difference today.
Jordan Wilson [00:25:16]:
No one can. Right? So, yes, there's different types of AI. It's not just text. There's, you know, text to music that's really good. You know, Suno v five. There's text to video. Great companies out there, Runway, their Gen four five, Google v o three one, Sora two, a lot of Chinese models. Right? So it's not just text.
Jordan Wilson [00:25:38]:
It is multimodal. These models are multimodal by default. So, you know, text, images, music, soundscapes, sound effects, writing code, it is all over the place. And it works. Right? Yeah. Maybe you read a bad headline recently that said, you know, 95% of AI pilots failed. They don't. That was, marketing.
Jordan Wilson [00:26:03]:
The ROI is true. If you look at real studies, right, that talk to thousands or tens of thousands, of business leaders, almost every single reputable study in the world shows that the ROI, return on investment of AI, is exponential. So as an example, the International Data Corporation found that companies get $3.70 back for every $1 invested in generative AI, and top performers are seeing a 10 plus, dollar return per dollar invested. Snowflake in, ESG enterprise strategy group survey, more than of almost 2,000 business leaders show that 92% of early adopters say their AI investments are already paying for themselves. So, yeah, already positive, ROI. And then, similarly, other studies show that up to 98% of of the same companies, are that have previously invested in AI are looking to increase their investment. 98% are increasing their investment. Right? So it's no longer experimentation like it was in 2023.
Jordan Wilson [00:27:09]:
Now it's all about scale. Now it's all about, oh, wait. We can crush our competitors with AI. Unfortunately, it's about reducing headcount. We'll get to that in a little bit. But the returns are there and they are undeniable. And the economics get stakes as well are massive. Right? Like I said, never trust small scale studies or studies that have, an agenda.
Jordan Wilson [00:27:32]:
But, when you talk about does AI work? I mean, look at Anthropics, November study. 100,000 real conversations. They found that AI reduces task completion time by 80%. There was a McKinsey digital study a couple years ago. Same thing, said between 75 to 80% time savings on standard knowledge work tasks. PWC study of 50,000 workers globally said 92% of daily AI users report productivity gains. Right? 92% are reporting productivity gains. And I wanna talk to the other 8% and teach them a couple of things, but these stats are undeniable.
Jordan Wilson [00:28:14]:
These large language models do the tasks that used to right? I remember tasks I used to do fifteen years ago. A lot of researching. Right? Looking having 20 tabs open, you know, reading these PDFs, grabbing information out, personalizing it, synthesizing it, putting it in spreadsheets, maybe then eventually turning those spreadsheets into a PowerPoint, something like that. Those are projects that would take forty, fifty, sixty, a hundred hours. I literally timed this the other day. Takes like ten minutes now. One model, one prompt, if you know what you're doing, can do that entire process in ten minutes. And it's about 99.7% factual, if you know what you're doing.
Jordan Wilson [00:28:58]:
Right. Today's models are completely unrecognizable from the Chad GBT of 2022. And you might be thinking, wait, if all these AI models are that good, what's happening to jobs. Right? Let me just tell you this. If you're brand new to the show, I'm a realist. Right. I'm not someone drinking this AI Kool Aid and being like, oh, you know, none of us are going to have to work and we're all going to live, you know, this utopian AI dream. No, I don't think so.
Jordan Wilson [00:29:28]:
I think it it might get a little bad. Right? And we're already starting to see that. Talk to recent grads. How many recent grads do you know? It's hard to get a job because companies just aren't hiring anymore, especially the companies that have figured out AI. Right? So, some stats here. So only thirty percent of graduates in 2025 secured a job in their field, and that's down from forty one percent the year before. That is a huge year over year drop. And entry level hiring is down 44% from its peak three years ago.
Jordan Wilson [00:29:59]:
So entry level hiring going down nearly 50% is catastrophic. And just the number of, graduates not being able to secure a job in their field, that's, worrisome as well. Right now, sixty two percent of employers say that candidates should have AI knowledge, but 55% of graduates say their degree programs didn't prepare them. So, yeah, everyone wants AI experience, but, unfortunately, a lot of colleges and universities, you know, from 2022 to 2024 or 2025 just banned AI. So it has created, especially in The US, kind of a crisis. Right? Companies can't find the experience that they need. So instead, they're just doubling, tripling down their AI investments as these models get more and more smart or as these, models get smarter and they're like, wait. Maybe we don't need all these people.
Jordan Wilson [00:30:54]:
Also, 51% of recent grads are second guessing their career choice due to AI up from 33% the year prior. That is a huge jump. Right? Anyone of you study statistics and you see something like that, this is from a study. I covered this in an earlier episode. It had always been around that, like, 25 to 30% mark, and then it just skyrocketed to more than 50%. And I do assume probably in three years, that number is gonna be more than three fourths. I would assume that the overwhelming majority, probably three and four college grads are gonna be like, what did I just go to school for? Right. Unless you're in something related to AI.
Jordan Wilson [00:31:37]:
It's like, what did I go to school for? Yeah. All right. And companies are betting big on it. So global AI spending on AI has already is expected to hit $2,000,000,000,000 this year, and that's a 37% jump from 2025. And Gartner predicts that 40% of enterprise apps will include AI agents by year end. So, essentially, everything h, sorry. Companies and enterprises are spending more and more money on AI in the common software that most companies use. Right? I'll just throw some basic examples.
Jordan Wilson [00:32:15]:
Right? Salesforce is a very, you know, popular CRM, the most popular CRM in the world. And then, you know, click up, let's use that as an example, smaller company, but they're a, you know, pretty big CRM company. You know, you can say the same thing for, you know, HubSpot, right? Those companies basic software that tens of millions of businesses use everything's an agent now, Right? Everything is being agentified before our very eyes. The very task that humans would do inside of this software, it's all just becoming an agent now. So as we wrap up here, what does all this mean? Alright. So now you know a little bit the history of generative AI. Right? AI is not new, but the transformer technology that led to large language models is relatively new. And the adoption of large language models has been unlike anything we've ever seen, and it's not even close.
Jordan Wilson [00:33:18]:
So why does it matter this year more than ever? Well, I've been the crazy guy yelling at you all for many years to not wait any longer to train your employees, to invest in the future of work. Right. Just using AI is not going to do anything. It's not going to do anything for your department. It's not going to do anything for your career. It's not going to do anything for your company. Right? I think a lot of, you know, a lot of enterprises thought like, okay. Well, yeah, we'll just buy some Copilot seats.
Jordan Wilson [00:33:55]:
We'll buy some, you know, Chatt GPT enterprise seats, and, you know, that'll make the board happy. And, you know, maybe our people are a little more productive and we'll be able to quietly reduce headcount and everything will be good. It's not like that anymore. Companies that adopted AI early are now three times more likely to see operating profit impact, up to 5% than those companies that are still in the experimentation phase. Right? You can't be experimenting anymore, both as individuals, as departments, as organizations. You can't treat this as, you know, we're gonna pilot this AI thing. No. You have to hit the ground running.
Jordan Wilson [00:34:39]:
You have to hit the ground learning. You have to hit the ground experimenting. You have to hit the ground measuring. You have to hit the ground scoping. You have to hit the ground running your own internal benchmarks, but you have to hit the ground running as fast as you can. Right? You have to go slow, but you have to go fast. Right? You have to be methodical. You have to measure.
Jordan Wilson [00:35:01]:
You have to, know. You have to be able to quantify, but you have to do it as fast as possible. Because the gap between AI fluent workers and AI fluent companies and everyone else is widening every single month, every single week, every single day. So by companies sitting there and saying, yeah, we're going to do a year long pilot. You know, we're going to make sure we get this AI thing, right. We're going to do a slow rollout. It's not going to work right. Even fortune 500 companies that have that mentality.
Jordan Wilson [00:35:40]:
They're going to get eaten up. You're already starting to see stories of it. Right? Companies that thought they were too big for AI. Right? You saw a lot of companies, FYI, doing about face. Right? Some of the consulting companies, you know, now investing billions of dollars. You know, some of the banks said, oh, no. We're large language models, never touching that. Let's laugh at that.
Jordan Wilson [00:36:02]:
No. Now they're investing billions of dollars. You can't not play the game. You don't have a choice. The future of work is generative AI is large language models. So the window is closing y'all and that is why generative AI matters in 2026 more than ever. This is the year to make your move. This is the year to level up.
Jordan Wilson [00:36:31]:
And guess what? It starts here with the start here series. Alright. Thank you for tuning into the first, volume of the Start Here series. Like I said, future ones are gonna be much, much faster, about twenty to twenty five minutes. And make sure if you're still listening, check the show notes of this episode. So, if you're listening on January 15, right, that's when, you you know, this show is debuting. Nothing's gonna be there. But in the future, we're gonna go and update the show notes.
Jordan Wilson [00:37:01]:
So if you're listening on the podcast as we release new episodes, we will make sure to link them there. But more importantly, just go to starthereseries.com. There, you can, sign up for our community for free. You can go follow. It's gonna send you straight, to the space that we have set up that's gonna have just the start here series in there. Nothing else so you can focus. So, whether you're hearing this in mid January, mid February, late twenty twenty seven, it doesn't matter. It's gonna be there.
Jordan Wilson [00:37:33]:
You can get caught up, and instantly level up. Alright. So here's the final take. AI isn't new, but generative AI's compounding impact is. In today's large language models move faster than anyone can track, and they can even outperform humans in blind tests at producing economically viable work and valuable work. So don't think about AI upskilling or AI reskilling. If you do that, you're gonna fail. That is the wrong way to approach AI.
Jordan Wilson [00:38:02]:
You have to unlearn. You have to unlearn good habits, and you have to build a solid foundation from scratch, AI first, AI native. You don't get to sprinkle AI on the top. It's not gonna work. Alright. Thank you for tuning in. Like I said, please go to starthereseries.com. If this was helpful, tell someone about it.
Jordan Wilson [00:38:23]:
Please subscribe to the podcast. Share this. If you're listening, on social media, on LinkedIn, tag someone who needs to hear this. We all need to start somewhere. Alright? Don't let the rapid pace of AI confuse you. Don't let it slow yourself or your company down. That is my job. I work for you.
Jordan Wilson [00:38:41]:
You don't have to spend hours every single day. I do it. I cut it to you straight. Alright. So thank you for tuning in. Hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all.