The Gap Between Promise and Practice
Healthcare organizations have spent the last two years talking about AI. They’ve hosted panels, launched pilot programs, and issued press releases about the future of intelligent automation. Yet most of those initiatives never make it past the boardroom slideshow.
The problem isn’t the technology. It’s execution.
Steve Valdiserri, founder of Avanti Strategy Group and a healthcare strategy consultant with more than a decade of experience in healthcare operations and technology, has watched this pattern repeat across value-based care programs, health tech platforms, and provider organizations. His work centers on helping healthcare companies solve real problems and realize measurable value, and he sees a clear divide: organizations that treat AI as a headline versus those that treat it as a tool.
“I can only win when my mind is stronger than my emotions,” Valdiserri often says, quoting Jim Rohn. The same logic applies to AI strategy. Hype creates noise. Discipline creates results.
Where AI Actually Works in Healthcare Today
The most effective AI implementations in healthcare aren’t the ones making headlines. They’re the ones quietly improving operations in revenue cycle management, attribution analysis, and care coordination.
Valdiserri points to three areas where AI is already delivering value:
Revenue cycle management operations. Automation can handle claims processing, denial management, and payment reconciliation faster and more accurately than manual review. For organizations struggling with claim backlogs or coding errors, AI-driven RCM tools can reduce administrative burden and accelerate cash flow.
Data-driven decision-making. Healthcare generates massive amounts of data, but most organizations lack the infrastructure to turn that data into actionable insights. AI and analytics platforms help provider organizations identify patterns in patient populations, predict risk, and allocate resources more effectively.
Operational workflow optimization. From scheduling to prior authorization, AI can streamline repetitive tasks that consume staff time without adding clinical value. The result is more capacity for high-touch patient care and fewer administrative bottlenecks.
These applications work because they solve specific, measurable problems. They don’t require a complete organizational overhaul. They fit into existing workflows and produce clear outcomes.
The Execution Problem
Most healthcare AI projects fail not because the technology doesn’t work, but because organizations skip the hard work of implementation.
Valdiserri’s approach, shaped by nearly a decade at VillageMD and his Harvard Medical School certification in AI in Healthcare, starts with a simple question: what problem are you actually trying to solve?
Too many organizations start with the solution and work backward. They buy an AI platform because competitors are doing it, then scramble to find a use case. That’s innovation theater, not strategy.
Real execution requires:
Clear problem definition. If you can’t describe the operational or financial pain point in one sentence, you’re not ready to implement AI.
Stakeholder alignment. Technology teams, clinical staff, and finance leaders need to agree on goals and success metrics before a single dollar is spent.
Commitment to the process. AI implementation isn’t a one-time project. It requires ongoing refinement, training, and integration. Organizations that succeed are the ones willing to do the unglamorous work of iteration and adjustment.
“Discipline and organization are foundational,” Valdiserri says. “Healthcare is a very disorganized industry. Always changing. Not one way to do things. Personally staying organized to juggle all the tasks is paramount to succeed.”
What Organizations Should Do Now
Healthcare leaders don’t need to wait for the perfect AI solution. They need to start with what’s already available and build from there.
Start small and specific. Pick one high-impact, low-complexity use case. Test it. Measure it. Learn from it. Then scale.
Leverage consumer AI for productivity. Before investing in enterprise platforms, explore how consumer AI tools can boost personal and team productivity. Valdiserri advocates for using accessible AI to streamline internal operations, from meeting summaries to data analysis. These low-cost, high-impact wins build organizational confidence.
Invest in education. AI literacy isn’t optional. Leadership teams need to understand what AI can and can’t do, how to evaluate vendors, and how to manage risk. Credentials like the Harvard Medical School AI in Healthcare program provide frameworks for responsible implementation.
Measure what matters. AI projects should be held to the same financial and operational standards as any other initiative. If it’s not improving patient outcomes, reducing costs, or increasing revenue, it’s not working.
The Real Question Isn’t Whether AI Works
It’s whether your organization is ready to execute.
The healthcare industry is at a turning point. AI has moved from experimental to essential. But the gap between innovation and execution remains wide. Organizations that close that gap, that commit to doing the hard work of implementation, will be the ones that realize real value.
Valdiserri’s advice is straightforward: “It’s on you to get you where you want to be. No one else is going to do it for me.”
The same is true for healthcare organizations. The tools are here. The question is whether you’re willing to put in the work.