Enterprise AI Is Wide Open - The Builders Who Lived The Problem Are Shipping First
Irina Zakharchenko, co-founder and COO of ApolloRise, helps leaders turn complex data into clear, confident growth decisions.
I spent nearly two decades on the enterprise technology buying side. I watched how companies source, evaluate and choose vendors. And I can tell you that the most persistent pattern in enterprise procurement is failing to find the best solution and instead finding the most visible one.
That pattern is about to be disrupted because the people building the sharpest vertical AI tools right now are small, domain-expert teams, many of them women-owned, who are shipping faster than procurement knows where to look.
The builders are already here.
The conversation about women in AI often starts with potential: what women could build if given the tools. But that framing misses what is already happening. Many are not at the starting line; they already shipped.
Women are deeply embedded in healthcare as clinicians, administrators and patients. They lead in legal support, education, caregiving and consumer operations. These are also the domains where traditional enterprise software left the most value on the table, where the gap between the tool and the actual workflow was widest. That gap created a backlog of unsolved problems sitting in plain sight.
AI collapsed the cost of turning domain expertise into a working product. A founder who knows where the process breaks can now build and ship without a full engineering team, a $2 million seed round or a technical co-founder she does not have access to yet. And that is exactly what is happening. As the CEO of a $12 billion AI healthcare startup told Fortune at Nvidia's GTC summit, some of the most valuable companies in the world will soon have fewer than 100 employees. His own company already does. Small teams are building serious companies and women-owned firms are among them.
The market is not waiting.
For anyone still hearing that AI failed the enterprise, the data says otherwise. According to Andreessen Horowitz's enterprise AI analysis, 29% of the Fortune 500 and 19% of the Global 2000 are now paying customers of at least one AI startup. No technology category in modern memory has penetrated the largest enterprises this fast.
And most of the opportunity is still unclaimed. Legal fragments into in-house counsel, law firms, patent specialists and plaintiff attorneys, each with entirely different workflows. Healthcare splits across specialties, facility types and payer relationships. Education, caregiving and women's health remain largely untouched. These are thousands of vertical slices waiting for someone who understands the problem well enough to build for it.
Meanwhile, major tech companies have started writing AI adoption into employee performance reviews. According to Business Insider, leadership at the largest firms is telling executives that transformation is non-negotiable. Everything those organizations cannot build fast enough internally is revenue sitting on the table for someone building outside.
Enterprises have not yet noticed the sourcing gap.
Here is the part of this story that no market report covers.
Large enterprises are spending months building internally what small, specialist firms already built years ago. They default to their own base instead of looking externally, and often end up with slower solutions that are less informed by the actual workflow. Meanwhile, the teams that solved the problem first—many of them women-owned—are one sourcing decision away from being found.
This is a discovery gap. Enterprise procurement still draws from the same pools, the same directories, the same referral networks it always has. The enterprises that update how they source will find better AI tools, faster. The ones that do not will keep paying more to build less.
And even when a specialist firm does get in the room, the evaluation itself can work against them. I recently pitched a client whose feedback was that our team was "too technical," that we needed to be more polished and persuasive instead. Think about what that means for AI procurement.
The buyer chose storytelling over substance. But AI tools that are selected for the pitch instead of the architecture do not scale. They hit friction the moment a real team tries to operate them without guardrails. The enterprise ends up paying twice—once for the product that sounded right, and again to fix what it cannot do.
The advantage goes to whoever looks first.
The barrier for small AI firms is no longer technical, capital or team size. It is visibility and that is a solvable problem on both sides.
For small AI firms, the practical playbook starts with discoverability. Register in federal and state procurement databases. Pursue certifications such as WOSB, SBA and HUBZone that put you on buyer shortlists, as large enterprises are increasingly required to review.
Build case studies around measurable outcomes, not feature lists, so a procurement officer can justify the sourcing decision internally. And when you do get in the room, lead with the architecture. Show how the product operates after the demo is over, how it scales, how it handles edge cases and how a team adopts it without hand-holding. In a market flooded with polished pitches, technical depth is a differentiator, not a liability.
For enterprise buyers, the move is equally straightforward: Expand where you look. The best vertical AI tools are increasingly being built by small, specialist teams outside your existing vendor network. A sourcing pipeline that only surfaces the usual names will miss the products built by the people closest to the workflow you are trying to fix.
Nearly a third of the Fortune 500 is already buying AI. The builders who lived the workflow are shipping real products right now. The enterprises that broaden how they source will find them. And the founders who keep building will likely be ready when they do.
By Irina Zakharchenko, co-founder and COO of ApolloRise, helps leaders turn complex data into clear, confident growth decisions. Read Irina Zakharchenko's full executive profile here.
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