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Part 3: Long-Term Value Distribution and Healthcare's Economic Future
This is the final part of our three-part series on AI workforce solutions in healthcare. In Parts 1 and 2, we explored the economic fundamentals and provided strategic guidance for founders and investors. Here, we examine who ultimately captures value from this transformation and how it might reshape the entire healthcare system.
Long-Term Implications: Who Wins and the Impact on Healthcare Costs
As agentic AI becomes more pervasive, it's worth contemplating the bigger picture: Who ultimately captures the value created by AI as a workforce, and how might this shift affect the cost of the care system as a whole?
In the near to mid-term, we can expect a distribution of value among multiple stakeholders:
Startups and their Investors
In providing these AI workforce solutions, successful startups will generate significant enterprise value (as we're already seeing with unicorn valuations). High margins and subscription-like revenue from these solutions can make for very profitable businesses. Investors are betting that these companies can capture a slice of the vast labor costs they are saving – essentially monetizing a portion of the efficiency gain. For example, if an AI saves a hospital $1 million a year in labor, the startup might capture $300,000 of that as revenue, leaving $700,000 in savings for the customer. At scale, that translates to robust profits. Over time, competition may drive prices down. Still, the best-positioned AI companies could become essential platforms that command durable fees, much like Electronic Health Records (EHRs) have become must-haves with hefty costs.
Providers (Hospitals/Clinics)
Healthcare providers can benefit from improved margins by reducing labor expenses or at least curbing their growth. In a tight financial environment (many hospitals have been operating at slim margins or losses since 2020), the ability to structurally reduce costs is extremely attractive. We could see hospitals able to reinvest their savings into other areas, such as quality improvement or new services. In theory, providers could also pass savings along in the form of lower prices for services; however, historically, healthcare providers have not often lowered prices. Instead, the savings may slow down annual cost increases or help hospitals stay solvent. Providers also benefit via workforce stability: AI taking on undesirable tasks might reduce burnout and turnover among staff, which has indirect financial benefits (retention of nurses, etc., avoiding expensive temp staffing).
Payers (Insurers and Employers)
Payers are key indirect beneficiaries. Many agentic AI solutions help ensure patients receive appropriate, timely care (thus preventing costly complications) and streamline administrative tasks (such as claims, authorizations, and customer service). If these measures lead to fewer unnecessary ER visits, improved medication adherence, or quicker discharge processes, payers will save on medical claim costs. Some payers are directly investing or partnering in this space. Over time, payers may incorporate expectations of AI efficiencies into their reimbursement models – for instance, if prior authorization becomes inexpensive and instantaneous, an insurer might require it more universally, knowing it's not a burden with AI. Payers also win from member satisfaction if tools like AI concierges make the healthcare experience smoother. An interesting dynamic will emerge with contracting: as providers save money with AI, payers may push for lower reimbursement increases, essentially attempting to claim some of the savings. This is analogous to how insurers push back on hospitals if, for instance, the length of stay decreases due to improved care coordination – they might argue that costs should also decrease. So, there may be a tug-of-war in capturing the value, but overall system efficiency does benefit payers.
Patients
The end consumers of healthcare – patients – could be significant winners if this AI-driven efficiency translates into more accessible and affordable care. In the long run, bending the cost curve down is crucial for patients (lower premiums, lower out-of-pocket costs) and society at large. Agentic AI has the potential to structurally deflate the cost of care by removing some of the labor cost inflation that has plagued healthcare for decades. If a task that used to require a $30/hour employee for 1 hour can be done by AI in 5 minutes of compute time, that's a huge productivity gain. Historically, productivity gains in other industries (like manufacturing) have led to lower consumer prices or at least enabled higher volume at the same cost. Healthcare hasn't seen such productivity leaps in a long time – it's one reason costs continue to climb. AI workforce solutions might finally change that. Patients will also experience greater convenience and access: digital agents can provide 24/7 service, shorter wait times, and more personalized interactions at scale. A patient might get immediate answers from an AI agent at midnight, whereas previously, they'd wait hours or days for a human callback. In clinical settings, AI can extend care reach (e.g., more frequent check-ins for chronic patients via automated texts or calls, which keeps them healthier). So, the value to patients is better service and potentially slower growth in bills.
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The Broader Healthcare Transformation
The role of humans in healthcare could shift to higher-touch, higher-complexity activities. As one health system executive explained, "We're less focused on replacing humans but more on repurposing them to take on more complex tasks. As we automate the more mundane tasks, free up resources from outsourcing, we are probably looking at some of that savings to flow in the form of better salary ranges."
In the long term, if agentic AI becomes ubiquitous, we might witness a re-balancing of the workforce. The healthcare industry could gradually shift away from its labor-intensive model for routine tasks while preserving human expertise for complex care, which could 'tame the beast' of healthcare inflation. Some estimates suggest that broader AI adoption could save 5–10% of total healthcare spending annually (approximately $200–360 billion in the US)1 if applied effectively – savings that ultimately free up resources to either lower costs or reallocate to other needs.
Who loses in this scenario? Potentially, labor-intensive service providers or outsourcing businesses may find their models disrupted. Staffing agencies, BPO firms, or any middleman relying on inefficiency could be squeezed. Some roles in healthcare will evolve or even phase out over time. For example, medical scribes might not be needed if AI fully automates documentation, or call center headcount could be reduced as AI agents handle an increasing number of calls. This can create short-term friction in the labor market, although given the chronic shortages, it may be more of a re-deployment of people to where they're truly needed (e.g., more nurses at the bedside, fewer doing paperwork). Policymakers and institutions will need to manage this transition to ensure we upskill and transition workers accordingly.
Overall, the shift of AI from a tool to a workforce has the potential to drive a rare productivity revolution in healthcare, which could bend the cost curve and improve care delivery if guided correctly. The winners will be those who proactively embrace and shape this trend (startups building the solutions, providers, and payers deploying them intelligently, and patients who get more responsive care). Those who resist may find themselves on the wrong side of a more efficient paradigm.
Conclusion
What we are witnessing is the beginning of a new era in healthcare, one where the definition of "workforce" expands to include digital employees working alongside humans. Agentic AI is proving its worth not in futuristic visions but in day-to-day operations – scheduling appointments, answering calls, filling forms, coordinating care, and more. By smartly framing these solutions as workforce enhancers (not just IT), founders are accelerating adoption and scaling into budgets that were previously untouchable for tech.
As one VP of Revenue Cycle at a leading system put it,
"We don't care whether a dollar is spent on a human or AI. What matters is that our cost to collect stays under 3%."
That sentiment captures the emerging consensus: AI will win where it can prove to be cheaper, faster, and more scalable—without compromising outcomes.
The most successful ventures will be those that remain analytical in approach (proving ROI every step of the way), pragmatic about integration and change management, and strategic in navigating the complex healthcare ecosystem of stakeholders and regulations.
For healthcare founders and innovators, the charge is clear: focus on real problems, measure real results, and integrate AI in ways that make healthcare work better for everyone involved. The opportunity is not just to build another software tool but to fundamentally reshape how work gets done in healthcare – making it more efficient, more human-centered (ironically by offloading the drudgery to machines), and ultimately more sustainable. As this shift gains momentum, it could help us finally break out of the cycle of rising costs and workforce burnout that has long plagued the industry. In a sector often criticized for its resistance to change, the rise of AI as a digital workforce may well be the disruptive force that, managed wisely, leads to a structurally better healthcare system. And that is a future worth striving for.
Sources
- Sahni, Nikhil, George Stein, Rodney Zemmel, and David M. Cutler. "The Potential Impact of Artificial Intelligence on Healthcare Spending." National Bureau of Economic Research Working Paper, October 2023. Reported in Healthcare Dive, January 26, 2023. https://www.healthcaredive.com/news/artificial-intelligence-healthcare-savings-harvard-mckinsey-report/641163/
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