The Real AI Opportunity for Smaller Businesses Isn't Building AI

Over the past two months, Mark Cuban has been remarkably consistent in how he talks about artificial intelligence. Across multiple appearances and interviews, his advice to smaller businesses has not been to build AI, hire model experts, or chase frontier technology. It has been far more pragmatic: the opportunity is in using AI to solve specific operational issues, not in creating the technology itself.

Cuban regularly returns to the same grounding statistic. There are roughly 33 million businesses in the United States, and most of them will never have AI budgets, machine learning teams, or technical specialists. Yet these businesses know they need AI to stay competitive. The gap is not awareness; it's execution. They understand the pressure but lack the expertise to deploy AI in ways that actually improve how the business runs.

This is where popular AI narrative breaks down. Much of the market conversation still assumes that value comes from building intelligence itself. Cuban's point is that this thinking misses where the leverage really is. AI is becoming cheaper, more accessible, and increasingly customizable. The constraint is no longer access to intelligence, but whether that intelligence can be applied meaningfully inside real businesses with limited resources and no tolerance for experimentation that doesn't pay off quickly.

Application, however, depends on something far more basic than models or tools. It depends on data.

AI systems do not struggle because they lack sophistication. They struggle because they are asked to reason over incomplete, outdated, or context-poor information. Smaller companies may have CRMs, finance systems, and operational software, but those systems often reflect fragments of reality rather than a coherent picture of customers, markets, and change. When AI is deployed on top of that foundation, insights remain generic, recommendations feel unreliable, and trust erodes quickly.

This is why so many AI efforts stall before they scale. Industry research and executive commentary increasingly point to data context, not tooling, as the gating factor. Without the right data signals and external context, even well-designed AI initiatives fail to influence decisions at the leadership level.

Data enrichment addresses this gap quietly but decisively. It adds the business, market, and customer context that internal systems alone cannot provide. In doing so, it gives AI a clearer understanding of what the business actually does, who it serves, and where risk or opportunity is emerging. This is what allows intelligence to adapt to the business, rather than forcing the business to adapt to the technology.

For boards and executive teams, this reframes the AI conversation entirely. The strategic choice is not whether to "invest in AI", but whether to build a data foundation that allows AI to be useful at all.

The AI era will not reward ambition alone. It will reward readiness. Organizations that treat data as strategic infrastructure, clean, enriched, and decision ready, will quietly accumulate advantage while others chase tools that never quite deliver. This is not a technology cycle to "wait out". It is a structural shift in how intelligence enters the business. The question for leadership is simple: when AI starts making better recommendations, will it be because it understands your business, or because your competitors made sure it understood theirs?

About the Author

Janet Bumstead is a RevOps strategist, founder of Enroot Strategies, and Partner at EnrichIT!, where she helps companies make better revenue decisions at scale. She also serves as an Advisor with Next Generation Governance Group, is an educator, and an active board member. Her work sits at the intersection of revenue operations, context-driven data enrichment, and executive decision-making.

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