Kevin O’Leary, Mark Cuban, and voices tied to Anthropic and OpenAI are all pointing to the same market gap: companies want Artificial Intelligence, but most still need help deploying it inside day-to-day operations. O’Leary framed the opportunity around small businesses that are eager to adopt the technology but lack people who can implement it. Cuban described a similar environment, comparing it to the early personal computer era, and said there are 33 million companies in the US. Most of them don’t have Artificial Intelligence budgets. They don’t have Artificial Intelligence experts on staff. But they know they need it.
The central problem is presented as operational, not technical. Businesses already have access to tools such as ChatGPT, Claude, Copilot, and Gemini, and the models are described as affordable and highly capable. Even so, many organizations still rely on spreadsheets, disconnected tools, and manual processes because they do not know how to integrate Artificial Intelligence into how work actually gets done. The emphasis is on implementation and execution rather than traditional consulting deliverables such as strategy decks and roadmaps.
That implementation work is defined as understanding how a business really runs, where data lives, which tasks are manual, what breaks under volume, and where time is lost on work that automation could absorb. The approach is described as “Artificial Intelligence Operations,” a discipline focused on producing business value through workflow integration rather than through MLOps, data science, or information technology alone. The piece argues that this missing layer explains why many pilots show promise but fail to scale, change workflows, or produce return on investment.
A three-tier framework is used to organize adoption. L1 is individual productivity: getting people confident and effective with the Artificial Intelligence 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 Artificial Intelligence-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.
Demand for this kind of work is described as strong. The firm says it started about eight months ago and is already managing inbound demand from companies that bought tools, ran workshops, and issued licenses but still cannot make Artificial Intelligence operational. One industry figure is cited repeatedly: roughly 95% of all generative Artificial Intelligence pilots fail to produce measurable revenue returns. The next three to five years are expected to bring rapid acceleration, with the key constraint shifting from technology to human judgment, clear problem definition, and the ability to adapt as the tools evolve.
