Contrary to expectations, professions that are typically slow to adopt new technology (medicine, law) are showing massive enthusiasm for AI. This is because it directly addresses their core need to reason with and manage large volumes of unstructured data, improving their daily work.

The most significant opportunity for AI in healthcare lies not in optimizing existing software, but in automating 'net new' areas that once required human judgment. Functions like patient engagement, scheduling, and symptom triage are seeing explosive growth as AI steps into roles previously held only by staff.

The friction of navigating insurance and pharmacies is so high that chronic disease patients often give up, skipping tests or medications and directly worsening their health. AI can automate these tedious tasks, removing the barriers that lead to non-compliance and poor health outcomes.

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.

The successful early adoption of AI in healthcare was brilliant because it first targeted the administrative burdens that clinicians hate, such as documentation (scribes) and billing. By winning the hearts and minds of powerful incumbents with immediate quality-of-life improvements, the industry built momentum for more complex clinical applications.

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 adoption in healthcare has accelerated by sidestepping slow enterprise sales cycles. Companies like Open Evidence offer free, consumer-like apps directly to doctors (prosumers). This bottom-up approach creates widespread use, forcing organizations to adopt the technology once a critical mass of their staff is already using it.

Unlike the top-down, regulated rollout of EHRs, the rapid uptake of AI in healthcare is an organic, bottom-up movement. It's driven by frontline workers like pharmacists who face critical staffing shortages and need tools to manage overwhelming workloads, pulling technology in out of necessity.

The 'Overton window' of trust in AI for health is shifting much faster for consumers than for doctors. Patients are rapidly adopting tools like ChatGPT, often introducing the technology to their physicians. This dynamic creates a bottom-up adoption pressure and means the initial challenge is not convincing health systems, but managing the interactions between AI-empowered patients and not-yet-AI-empowered clinicians.