Incumbent software like Epic often just digitized outdated, paper-based processes, inheriting their inefficiencies and data silos. AI-native companies can ignore this technical and process debt, designing workflows from a clean slate to fundamentally disrupt giants whose products are built on obsolete logic.

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.

Organizations behind on traditional digitalization have a unique advantage. Instead of a costly catch-up, they can leapfrog this intermediate step and reimagine core processes—like org charts, career paths, and recruiting—to be AI-native from the start, avoiding the burden of legacy digital systems.

Contrary to conventional wisdom, large medical practices are predicted to outpace major hospital systems in AI adoption. Practices' more modern, cloud-based infrastructure allows them to deploy AI tools more quickly than hospitals, which are often hindered by legacy technology, complex governance, and slower ROI realization on new tech.

Urgency is forcing a major shift in hospital procurement. CIOs are no longer willing to wait years for incumbents like Epic to develop AI tools. They are actively partnering with startups to deploy commercially ready solutions now, prioritizing speed and immediate operational impact over vendor loyalty.

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.

Established SaaS companies struggle to implement AI because their teams are burdened with supporting existing customers, fixing feature gaps, and fighting legacy competitors. AI-native startups have a massive advantage as they don't have this baggage and can focus entirely on the new paradigm.

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.

Industries historically slow to adopt software are now rapidly embracing AI. Unlike rigid workflow tools, AI excels at parsing dense text and augmenting the nuanced, unstructured work common in these fields. This allows new AI vendors to gain traction without needing to rip-and-replace legacy systems of record like EHRs.