Dr. Wachter argues AI's rapid healthcare uptake stems from a collision of new technology with a system universally seen as failing. While consumers weren't clamoring for a better Google, everyone in healthcare—patients and providers alike—recognized the deep, unmet needs, making them receptive to a transformative solution.

Companies believe AI isn't delivering because technology moves too fast, so they invest in training and agile frameworks. The real, invisible problems are structural: ambiguous decision rights, siloed data ownership, and misaligned employee incentives. Solving for 'speed' when the foundation is broken guarantees failure.

Insurers use AI to auto-deny claims and require tedious phone calls for appeals. Lunabill provides hospitals with an AI voice bot to automate these calls. This creates an arms race where one company's AI will inevitably negotiate with another's, foreshadowing a future where many adversarial B2B processes become fully automated AI-to-AI interactions.

Before implementing AI automation, you must validate and refine a process manually. Applying AI to a flawed system doesn't fix it; it just makes the system fail more efficiently and at a larger scale, wasting significant time and resources.

An "AI arms race" is underway where stakeholders apply AI to broken, adversarial processes. The true transformation comes from reinventing these workflows entirely, such as moving to real-time payment adjudication where trust is pre-established, thus eliminating the core conflict that AI is currently used to fight over.

Chronic disease patients face a cascade of interconnected problems: pre-authorizations, pharmacy stockouts, and incomprehensible insurance rules. AI's potential lies in acting as an intelligent agent to navigate this complex, fragmented system on behalf of the patient, reducing waste and improving outcomes.

AI is not a silver bullet for inefficient systems. Companies with poor data hygiene and significant technical debt find that implementing AI makes their bad systems worse, simply scaling the noise and dysfunction rather than solving underlying problems.

Many AI projects become expensive experiments because companies treat AI as a trendy add-on to existing systems rather than fundamentally re-evaluating the underlying business processes and organizational readiness. This leads to issues like hallucinations and incomplete tasks, turning potential assets into costly failures.

The 'bot-on-bot' conflict between provider billing AI and payer denial AI is unsustainable. An AI system that deeply understands the clinical encounter creates a verifiable source of truth. This could make the ROI on both revenue cycle and payment integrity teams negative, forcing collaboration.