The Startup Reckoning: Why Healthcare AI Now Demands Industry-Built Founders

By Stephanie Jones, Founder & CEO, It Takes a Village Software and Services (iTAV) 

As the startup landscape faces significant scrutiny, particularly for new entrants to the market, the tension between credibility and innovation is palpable. In today’s climate, particularly in regulated industries like healthcare, credibility now matters as much as innovation. Investors and buyers are increasingly demanding not just AI capability, but domain expertise, governance readiness, and operational fluency. For-profit startups seeking longevity must demonstrate they are built with purpose, not simply built for growth.

As I’ve grown It Takes a Village Software & Services (iTAV), a health tech software company that brings a people-first, AI-powered workforce intelligence platform to help government sponsored payers and vendors optimize their operations and enhance administrative efficiency, I’ve learned a few key lessons that other founders in the IT and startup space can leverage as they launch, expand, and seek funding. 

Build from Industry Experience, Not Just Technical Capability

One of the biggest gaps in today’s AI landscape is that many solutions are built with strong technical foundations but limited industry context. In healthcare, particularly within government-sponsored plans, that disconnect can create tools that look promising on paper but struggle in real-world operations.

Over the past year, as we’ve demoed early versions of our solution to more than 30 health plan leaders and industry experts, a clear theme has emerged: organizations are not just open to AI innovation, they are actively looking for solutions grounded in operational realities. Many leaders want tools designed specifically for the challenges their teams face every day, rather than broad, industry-agnostic platforms.

My perspective on this comes from years spent as an operator and former COO, where I helped scale operations for more than 50 government-sponsored payer clients. Through those experiences and through my own buying decisions, I saw firsthand how often technology implementations miss the mark because they fail to account for the nuances of the healthcare system. When those gaps appear, organizations are forced to rely on manual workarounds, which undermines the value AI is supposed to deliver.

That experience has shaped a deliberate approach: take the time to build thoughtfully and work closely with pilot customers during the MVP phase to ensure the technology reflects the realities of the sector.

During one prototype demo, a regional plan market president expressed surprise and enthusiasm when she noticed that one of the homepage metrics addressed calls per member. She mentioned that, among all the solutions she had evaluated, she had never seen that metric highlighted.

To us, it seemed like a fundamental operational measure for health plan call centers. But the reaction highlighted something important: even large analytics providers can overlook core operational indicators when their products are designed broadly rather than for the specific needs of government-sponsored payers. When the people building AI solutions lack a deep understanding of the sector, the training data, insights, and ultimately the effectiveness of the AI can suffer.

Design for Regulated Environments from Day One

Healthcare innovation also comes with a distinct set of barriers. Requirements like SOC 2 compliance, strict data protections, and oversight from AI governance committees mean new technologies face intense scrutiny before they are ever deployed.

For startups entering this space, designing with those realities in mind from the very beginning is critical. Stakeholders increasingly want transparency into how AI systems operate, how insights are generated, and how decisions are ultimately made.

One way to address this is through a human-in-the-loop model that pairs advanced analytics with human oversight. This type of framework helps ensure that AI augments decision-making rather than replacing it entirely, while also building the trust organizations need when adopting new technology.

Validate With the Market Before Scaling

Another consistent lesson has been the importance of engaging with the market early and often. Too many technology solutions are built in isolation and only later introduced to the industries they aim to serve.

Continuous feedback from healthcare leaders, particularly around how AI is being used and where it raises concerns, has been essential in shaping how solutions should evolve. Listening first helps ensure that the tools being developed align with operational realities rather than theoretical use cases.

Compete on Purpose, Not Just Valuation

Beyond the technology itself, motivation matters. Government-sponsored healthcare plans cover roughly 125 million Americans through programs like Medicare, Medicaid, and ACA marketplace plans. The stability of these programs depends heavily on the operational health of the organizations that administer them.

Many employees working within these systems face significant stress and burnout as demand continues to outpace staffing capacity. Improving operational efficiency—even modestly—can have a meaningful impact.

If operational improvements can raise margins by even a quarter of a percentage point, it may help keep essential health plans viable, reduce market exits, and ultimately prevent disruption for the members who rely on them.

Embed Experience Across the Leadership Team

Finally, building effective healthcare technology requires more than a strong founder, it requires deep operational knowledge across the leadership team.

At iTAV, our leadership team brings together roughly a century of combined experience in healthcare operations. That shared experience helps ensure the technology being developed reflects how organizations actually function and how teams make decisions in high-pressure environments.

Equally important is recognizing that AI works best when it supports people, not replaces them. When designed thoughtfully, it can help operators move faster, make better-informed decisions, and improve both service delivery and employee experience.

As a former COO, I’ve always tried to lead with empathy—remembering that behind every system or workflow are teams of people doing their best to support patients and members. The goal isn’t simply to introduce new technology, but to create tools that make those jobs more manageable and the systems they support more sustainable.

Innovation in healthcare will continue to accelerate, but lasting impact will come from solutions built with both technological rigor and a deep understanding of the people and systems they aim to serve.

Stephanie Jones is the Founder and CEO of iTAV, where she works to simplify complex healthcare systems and improve access for patients, caregivers, and the operational teams that support them. A healthcare and technology executive with more than 30 years of experience, Jones previously held senior leadership roles at organizations including Cigna, Coventry Health Care (now Aetna), UnitedHealth Group, and Convey Health Solutions. Throughout her career, she has led large-scale operational and technology initiatives and helped scale high-growth healthcare businesses. Jones is also an author and thought leader on healthcare transformation, leadership, and innovation.