Within the span of about two weeks, three very different voices converged on the same message.
Kevin O'Leary posted a video on X saying that if he were 25 today, he'd focus on two things: AI implementation for small businesses and data center infrastructure. His framing was specific. Companies with five to 500 employees are desperate to adopt AI, they have the money and the urgency, but they don't have anyone who can actually do it. He drew a clear line between this work and traditional consulting, calling it "implementation and execution."
Mark Cuban, in a clip from TBPN that went viral on X, made a nearly identical case. He compared the current moment to walking into companies in the mid-1980s that had never seen a personal computer. There are 33 million companies in the US. Most of them don't have AI budgets. They don't have AI experts on staff. But they know they need it. Cuban's take: "Regular software is over. Everything will be made just for how your business actually works. Who's going to set that up for them?"
Then there's a post from Luke Pierce that pulled the thread even further. He pointed out that both Anthropic and OpenAI are now building PE-backed consulting arms to deploy AI inside companies. The two organizations building the most powerful AI on the planet looked at the market and decided that businesses can't figure out how to use it on their own. That's not a minor observation. When the model builders themselves start doing implementation work, it tells you exactly where the gap is.
All three are pointing at the same thing.
The Gap Is Real, and It's Not About the Technology
Every company we talk to has access to the same AI tools. ChatGPT, Claude, Copilot, Gemini. The models are available. The pricing is reasonable. The capabilities are extraordinary. And yet the vast majority of organizations are still running on spreadsheets, disconnected tools, and manual processes. Not because they don't care about AI. Because nobody has shown them how to actually wire it into the way they work.
This is the gap that Gadoci Consulting was built to fill.
We started the firm about eight months ago, but the work itself goes back further than that. Before Gadoci, I spent years in the weeds of AI adoption at the enterprise level, watching the same pattern play out over and over. A company buys tools. They run a pilot. The pilot looks promising. And then nothing happens. The pilot doesn't scale. The workflow doesn't change. The ROI never materializes.
The problem was never the technology. It was always the space between the technology and the actual work.
What We See on the Ground
When we engage with a new client, the first thing we do is understand how the business actually runs. Not the org chart version. The real version. Where does the data live? What's manual that shouldn't be? What breaks when volume goes up? Where do people lose time to tasks that AI could handle in seconds?
This is what O'Leary was getting at when he distinguished implementation from consulting. Traditional consulting gives you a strategy deck and a roadmap. Implementation means getting inside the workflows, building the automations, training the people, and making it stick.
We call this AI Operations. It's not MLOps. It's not data science. It's not IT. It's the discipline of making sure AI actually delivers business value through workflow integration. That distinction matters, because it's why so many organizations have invested in AI and have nothing to show for it.
Our framework levels AI solutions into three tiers. L1 is individual productivity: getting people confident and effective with the AI tools they already have access to. L2 is workflow automation: building team-level and department-level systems that automate real processes. L3 is custom applications: bespoke AI-powered tools built for specific business needs. Most companies are stuck at L1, doing scattered individual experimentation with no strategy connecting it to business outcomes.
The Market Is Confirming the Thesis
Eight months into building this firm, we're not struggling to find demand. We're managing it. Companies are reaching out because they bought the tools, ran the workshops, set up the licenses, and realized they still don't know how to make any of it operational. That's the story Cuban is telling, that O'Leary is telling, and that the AI labs themselves are confirming by investing in consulting arms of their own.
There's a statistic that keeps showing up in industry research: roughly 95% of all generative AI pilots fail to produce measurable revenue returns. That number isn't about bad technology. It's about the absence of what we do. Nobody mapped the process. Nobody built the bridge between the model and the workflow. Nobody trained the people who have to use it every day.
When Cuban says "there'll be more jobs than people for a long, long time" in this space, he's right. When O'Leary says companies are "willing to pay to solve that pain point," he's describing the conversations we have every week.
Where This Goes
The next three to five years are going to compress dramatically. The tools are getting better at a pace that most people haven't internalized yet. As I wrote in Execution Is Solved. Now What?, the execution layer for AI-powered work has collapsed to near zero for anyone willing to learn how to direct it. The constraint now is human: the ability to think clearly about what to build, communicate precisely what you need, and adapt continuously as the tools evolve.
Companies that figure this out early will compound their advantage. Companies that wait will find themselves further behind than they expect, because the pace of change isn't linear.
We're building Gadoci Consulting for exactly this moment. Not because Cuban and O'Leary told us to. Because we've been living inside this problem for years, and every signal in the market says it's only accelerating.
If your organization is sitting on AI tools it hasn't figured out how to use, that's the gap. We exist to close it.