AI as a key solution to workforce shortages and scalability
Healthcare systems are at a breaking point. The combination of rising patient demands and workforce shortages is challenging every executive in this space. A recent Bain survey confirms this, showing that staffing concerns are now one of the top issues on every healthcare leader’s 10-year horizon. The reality is clear: there’s not enough human capacity to handle the shift to value-based care using traditional methods.
This is where artificial intelligence earns its place at the table. We’re not talking about abstract potential, we’re talking about practical, immediate value. AI can take over repetitive processes, surface key insights from unstructured data, and reduce the load on overstretched teams. That means your physicians and staff can focus where it actually matters, patient outcomes. AI gives you a way to scale not by hiring thousands of people, but by multiplying what your best people can accomplish.
This isn’t about replacing human expertise. It’s about enabling that expertise to operate at a much higher level. You can drive faster decision-making, reduce administrative drag, and deliver more personalized care. All with less strain on the workforce you already have. For a C-suite leader, this should trigger immediate action. If your system is struggling under the current load, scaling human labor isn’t the only solution. Expanding system intelligence might be the smarter one.
Now, this isn’t magic or marketing, it’s just smart capacity planning. Get AI working where speed, accuracy, and consistency matter most. Start with high-impact functions and build your operational advantage from there. The organizations that lead here will shape the next phase of healthcare performance. The ones that wait will be left reacting.
Rapid expansion of AI adoption over the next three years
The pace at which health systems are planning to scale AI is accelerating. Leaders aren’t waiting around to see if this works. They understand it’s already working, and fast adoption is the new standard. Executives across the industry are signaling a major shift: over the next three years, AI will move from small pilots and isolated functions to broad-scale, organization-wide deployment.
This shift is not only driven by pressure to improve outcomes, it’s also about operational survival. AI is moving into core processes like clinical decision support, care management, and administrative workflows because that’s where immediate gains can be felt. It’s no longer enough to plan for incremental improvement. The businesses that win in healthcare over the next three years will be the ones that apply AI with intent, focus, and speed.
Executives need to ensure that adoption doesn’t stall in complexity. The tools exist, the talent exists, and the use cases are well-defined. What’s required now is disciplined action: choosing where to deploy, aligning your teams, and measuring results. AI won’t fix broken processes on its own, but it will massively boost efficient systems that are ready to scale.
Momentum matters. The organizations moving now are gaining the experience, data, and talent edge that later adopters won’t be able to replicate overnight. Use these next three years wisely. Build the AI capabilities that directly support your business model and your care strategy. This is a short window, and the energy you put behind adoption now will define your competitive position long after the tools become standard.
Holistic requirements for effective AI implementation
Deploying AI at scale in healthcare isn’t just a technology decision, it’s an organizational one. Success doesn’t come from just installing the next platform or integrating another algorithm. It comes from aligning your use cases with your business priorities, securing the right data infrastructure, and building cross-functional buy-in from clinical and operational teams. If those aren’t in place, scaling AI quickly becomes expensive and ineffective.
Start by getting specific about what you’re solving. Identify the areas where AI can fundamentally change outcomes, whether in reducing administrative overload, improving population health, or enhancing clinical decision-making. Then, verify you have the operational and clinical data in good enough shape to power those systems. If your data’s unreliable, your outputs will be too. And wherever AI is used, governance must follow. You’re not scaling tech; you’re scaling decision-making. That requires controls, oversight, and system-level accountability.
Equally important is how you bring people with you. Clinicians and staff are already managing high workloads. If AI feels like another demand, you’ll get resistance. If it directly eases their burden and improves patient care, you’ll get momentum. Engagement isn’t an afterthought, it’s a requirement. Without clinical buy-in, AI strategies get blocked at the frontline level, no matter how promising the tech.
Finally, someone must own this transformation. You need a leader on the executive team, not a project manager, with clear authority to drive these changes across departments. Change at this scale doesn’t happen off the side of someone’s desk. Appoint an internal sponsor who understands clinical, technical, and strategic dimensions of AI and has the mandate to execute. That’s how adoption becomes transformation.
Strategic decision-making on building vs. Outsourcing AI capabilities
As AI moves from test cases to scaled deployment, every health system will face the same question: what do we build ourselves, and what do we buy? This decision carries long-term implications, impacting cost, flexibility, speed to implementation, and ownership of proprietary value. There isn’t one answer that fits every organization. But what’s clear is this, doing both is unavoidable. The differentiation comes from knowing when to do each.
Build when the capability aligns directly with your core strategy, patient model, or competitive edge. That could mean clinical algorithms trained on your own patient population, or AI tools that directly support care pathways that set you apart. These are strategic assets, you’ll want control over how they evolve. On the other hand, outsource anything that’s commoditized or not your strong suit, especially when precision, regulatory readiness, or ongoing maintenance are non-trivial. Administrative automation is a good example, let someone else take that lift if it lets your teams focus on care innovation.
What executives must avoid is indecision. Stalling on a build-vs-buy question means operational delay and wasted opportunity. Instead, define your core capabilities clearly. Then assess your in-house capacity honestly, technical leadership, data science maturity, infrastructure strength. Match those against vendor offerings under a simple test: can the outside solution meet your needs faster, with less risk, and enough adaptability long term? If yes, outsource it and move on.
Organizations that scale AI well aren’t obsessed with ownership, they’re obsessed with results. Whether you develop it or license it, what matters is how it supports care delivery, improves outcomes, and reduces cost. Every AI decision should ladder up to that. Keep focused, cut complexity, and move quickly.
Key takeaways for decision-makers
- AI solves workforce pressure at scale: Leaders should invest in AI-enabled systems to relieve labor shortages while expanding capacity, especially as demands on value-based care models increase.
- Adoption velocity is accelerating: Health systems are rapidly scaling AI across functions, so executives must act now to avoid losing operational and competitive ground over the next three years.
- Execution matters more than tools: Success with AI requires strong data infrastructure, clear use cases, engaged frontline staff, and a leadership mandate, not just new software.
- Build-vs-buy choices shape outcomes: Executives should develop AI capabilities internally only for strategic differentiation and outsource the rest to stay agile, reduce risk, and focus on what matters most.
