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Part 1: The Economic Case and Budget Reallocation
Introduction
Healthcare is at an inflection point where artificial intelligence is being reframed not as an IT tool but as a workforce solution. This shift in mindset is profound: instead of treating AI like another software install (and squeezing it into the typical 2–3% IT budget of a provider1), forward-thinking healthcare leaders are treating AI as "digital labor" – a new category of workforce that can take on tasks traditionally performed by people. The implications are enormous. Labor accounts for roughly 60%2 of a hospital's costs, dwarfing the typical IT spend. By tapping into that labor budget and solving staffing pain points, AI solutions are unlocking faster adoption and commanding larger budgets than if they were pitched as standard IT products.
This three-part series offers a pragmatic and strategic look at why framing AI as a workforce solution is resonating in healthcare:
Part 1 examines the economic fundamentals—why the $0.60 vs $0.03 budget reallocation opportunity is so compelling, and how automation-first models achieve superior unit economics compared to traditional tech-enabled services.
Part 2 explores the investor landscape driving this transformation and provides a comprehensive founder playbook covering go-to-market strategy, regulatory navigation, and building defensible moats in the agentic AI space.
Part 3 looks ahead to long-term implications: who ultimately captures value as AI labor scales, how different stakeholders benefit, and how this shift could structurally bend healthcare's cost curve.
As one COO/CMO of multiple health systems we interviewed observed,
"If you draw a line and map every moment from when someone is referred to when they walk out, every touch point can be facilitated and automated by AI."
This insight captures the scope of the opportunity—and the unit economics that make automation-first models so attractive.
The goal is to provide healthcare founders (and the investors who back them) with an analytical roadmap for navigating the emerging era of AI in the healthcare workforce.
The Case for AI as a Workforce Solution
Why position AI as a "workforce solution" in the first place? The answer starts with healthcare's budget calculus. Hospitals and health systems spend the vast majority of their dollars on personnel – nurses, physicians, administrative staff, call center representatives, care coordinators, and so on. Labor expenses account for about 60% of the average hospital's budget. In contrast, IT typically represents only a small percentage (often around 2–3%) of expenditures. This means that solutions addressing labor costs have a significantly larger target to aim for. If an AI agent can credibly reduce staffing needs, augment staff productivity, or cover work that would otherwise require additional hires, it taps into the largest pool of spending in healthcare. For example, if AI can reduce the 2-4 hours nurses spend daily on documentation by 50%, a 500-bed hospital could effectively gain the equivalent of 125 additional nursing hours per day—worth $4.8 million annually at current nursing wages.
From a buyer's perspective, this framing changes who gets excited about the technology. Instead of being pigeonholed as another software tool that the CIO needs to justify, an AI workforce solution resonates with COOs, CFOs, and department heads who are under immense pressure from staffing shortages and wage inflation. These operational leaders control enormous budgets for labor and are actively seeking relief. They're often willing to pilot and deploy solutions quickly if they directly alleviate workforce bottlenecks (for example, reducing overtime, preventing burnout, or enabling smaller teams to handle a higher volume). In short, positioning AI as digital staff – virtual nurses, AI assistants, and automated analysts – opens the door to faster adoption because it addresses a significant pain point. It's not a "nice-to-have" tech gadget; it's a strategic workforce lever.
Crucially, treating AI as a workforce also tends to justify higher price tags and more robust ROI cases. A hospital might balk at a $500k software license coming out of the IT budget. Still, if that same $500k AI solution can replace the work of 3 full-time staff (worth $300k in salaries plus benefits, or ~$450k total cost) or dramatically improve throughput, it becomes cost-justified against the labor budget. As one analysis noted, administrative overhead in healthcare is enormous (nearly $1 trillion in the US)3, yet only ~10% of it is spent on technology today – leaving ample room to redirect manual work to automation. In summary, by aligning AI to the workforce problem, startups are finding far bigger wallet share and receptivity among healthcare organizations than traditional IT pitches ever could.
Budget Reallocation: Tapping a $0.60 vs a $0.03 Opportunity
The budget reallocation opportunity can be illustrated with a simple comparison: $0.60 vs $0.03. For every dollar a provider organization spends, about 60 cents go to labor and perhaps 2–3 cents to IT. In practical terms, that means a hospital might spend hundreds of millions of dollars annually on salaries (for nurses, clinicians, billing staff, schedulers, etc.) but only a few million on new software. This disparity has historically limited the impact of health IT. No matter how great a tool is, if it's allocated a tiny portion of an IT budget, it faces a very constrained economic ceiling.
Agentic AI flips this script. By demonstrating the ability to take on work, not just provide tooling, these solutions are compelling CFOs to reallocate funds from labor budgets into technology. For instance, a 500-bed health system typically spends $8 million to $15 million annually on call center operations. An AI "digital agent" that could automate a portion of those calls or make each human agent significantly more efficient, might justify an investment of several million dollars – far above what the call center would typically get in IT tools – because it's coming out of that labor line item. We're effectively seeing tech budgets commandeer part of the labor budget in the name of efficiency. It helps that these investments can often be framed in terms of direct ROI: "This AI will reduce overtime by X%," "allow us to handle Y more patients without new hires," etc.
Moreover, the urgency of labor shortages strengthens the case. As the pandemic recedes, nearly every health organization is grappling with workforce gaps and burnout. The nursing shortage, for example, is projected to reach 275,000+ by 20304, and healthcare systems face mounting financial pressure from turnover costs that average $52,350 per departing nurse. Administrative staff turnover is high, and hiring qualified people (for roles like medical billing or care coordination) is difficult and costly. In this environment, solutions that reduce reliance on scarce human workers are not just cost savers – they are business continuity and growth enablers. This often means that AI projects receive accelerated approval because the alternative (not having enough staff to handle demand) is not acceptable.
Finally, by focusing on workforce outcomes, AI providers can often sidestep the protracted procurement cycles typically associated with pure IT systems. Instead of a lengthy RFP competing against established EHR or ERP vendors, an AI workforce solution might be trialed in a specific department as a pilot to fill staffing holes, proving itself in months. Successful pilots then lead to enterprise rollouts, which are funded by productivity gains. The net effect is faster adoption curves than we've historically seen for new health IT. We're seeing startups achieve enterprise customer adoption within months rather than years by positioning themselves as workforce solutions rather than traditional IT tools.
This shift is already visible in IT spending patterns. As one health system CIO we interviewed noted,
"We see a shift happening. IT expenses have increased by a couple of percentage points each year, primarily due to the cost of labor and inflation. Five years ago, only 25% of IT was used to automate processes. Today, more than half is being allocated to automation and analytics platforms, and this will keep increasing."
Automation-First vs. Tech-Enabled: The Unit Economics
As AI workforce solutions prove their value in healthcare, founders face a critical strategic consideration: how much human service to include alongside AI automation. Many early healthcare startups adopted a tech-enabled services approach, combining software with teams of physicians, nurses, or coordinators to deliver comprehensive services (think telehealth companies or navigation services). This often improves outcomes but can suffer from service-like margins and scalability issues. In contrast, the new crop of agentic AI startups is usually automation-first, aiming for software-level margins by minimizing human involvement in the delivery process.
The difference becomes starkly apparent in unit economics. A pure software or automation solution can potentially achieve gross margins in the 70–90% range (after accounting for the costs of cloud computing and maintenance) – comparable to SaaS businesses. A tech-enabled service that employs clinicians or operators will have significantly lower gross margins, often in the 30–50% range when small, possibly improving to around 60% at best with scale and efficiency. For example, an analysis by Bessemer Venture Partners5 compared a $100 million run-rate healthcare SaaS business (~70% gross margin) to a larger $225 million run-rate tech-enabled health services business (~45% gross margin). The service business had higher revenue, but after adjusting for margins, the enterprise value was similar to or lower than the smaller pure software business. The takeaway: automation-heavy models can drive significantly better margin structures, which in turn support higher valuation multiples and more sustainable scaling (since each new customer adds profit rather than proportional cost of service delivery).
Healthcare executives are increasingly focused on this equation of scalability. As one CFO we spoke with put it,
"From a budget perspective, if I can replace many workflows with automation and remove 10 workers, that saves me much money. Everyone needs to answer - how quickly can I scale it for less cost."
However, a balance must be struck. In healthcare, pure automation can be risky if the AI isn't ready to handle all edge cases. Many successful models begin with a hybrid approach – AI handles the heavy lifting, and humans address exceptions – and gradually increase automation as the AI learns. Founders should think of human involvement as a "training wheel" or fallback that ensures service quality in the early stages (and helps navigate any regulatory requirements for human oversight) but with a clear roadmap to removing or reducing that human cost over time. The margin expansion story is a powerful one for investors: it means initial gross margins of 30-50% can improve to 80% or more as AI takes over more of the work. This stands in contrast to traditional tech-enabled services, where humans remain a core cost indefinitely.
There's also the question of pricing and capturing value. If your AI workforce solution delivers the equivalent of a $100k/year employee's work, how do you price it? Many startups are experimenting with usage-based or outcome-based pricing (for instance, charging per successful task completed or a percentage of the value of collections improved, etc.). The good news is that, unlike selling generic software, selling "labor" allows pricing against existing salary or outsourcing benchmarks, which are usually relatively high in healthcare. This can justify premium pricing while still offering the buyer clear cost savings. For example, if a health system typically spends $50 on a manual prior authorization request (in staff time), an AI that does it for $10 still leaves plenty of savings on the table and can pocket a healthy margin. The high-cost baseline of healthcare labor creates ample room for a win-win value division between customers and AI vendors.
In summary, the agentic AI trend is enabling startups to pursue service-like impact with software-like margins. The most scalable companies will be those that figure out how to maximize automation, ensure quality and safety with minimal human touch, and price based on value delivered rather than traditional software licensing. This combination yields a beautiful unit economic profile that is drawing significant investor interest – as evidenced by some of the large funding rounds we'll discuss in Part 2.
In Part 2 of this series, we'll examine the funding surge behind AI workforce solutions and provide a comprehensive founder playbook for go-to-market strategy, regulatory navigation, and building defensible moats.
Sources
- Definitive Healthcare. "Average Hospital IT Expenses: Healthcare Technology Insights." HospitalView Product, 2025. Based on Medicare Cost Report data from 5,000+ U.S. hospitals. https://www.definitivehc.com/resources/healthcare-insights/average-it-expenses-us-hospitals
- American Hospital Association. "Costs of Caring: A Report on Hospital Financial Pressures." 2025 Annual Report, April 2025. https://www.aha.org/costsofcaring
- Sahni, Nikhil R., Prakriti Mishra, Brandon Carrus, and David M. Cutler. "Administrative Simplification: How to Save a Quarter-Trillion Dollars in US Healthcare." McKinsey & Company, October 20, 2021. https://www.mckinsey.com/industries/healthcare/our-insights/administrative-simplification-how-to-save-a-quarter-trillion-dollars-in-us-healthcare
- National Academies of Sciences, Engineering, and Medicine. "The Future of Nursing 2020-2030: Charting a Path to Achieve Health Equity." Chapter 3: The Nursing Workforce. NCBI Bookshelf, 2021. https://www.ncbi.nlm.nih.gov/books/NBK493175/
- Kraus, Steve and Sofia Guerra. "Benchmarks for Growing Health Tech Businesses." Bessemer Venture Partners Atlas, October 2023. https://www.bvp.com/atlas/benchmarks-for-growing-health-tech-businesses
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