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.

Unlike previous tech waves that trickled down from large institutions, AI adoption is inverted. Individuals are the fastest adopters, followed by small businesses, with large corporations and governments lagging. This reverses the traditional power dynamic of technology access and creates new market opportunities.

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.

Unlike previous top-down technology waves (e.g., mainframes), AI is being adopted bottom-up. Individuals and small businesses are the first adopters, while large companies and governments lag due to bureaucracy. This gives a massive speed advantage to smaller, more agile players.

For its first three years, Read AI closed enterprise deals without salespeople. When IT departments inquired due to massive bottom-up adoption, the company provided self-service admin tools and automated volume discounts, often avoiding sales calls entirely.

While AI has vast potential, its most immediate and successful entry point is automating prior authorizations. This administrative bottleneck is considered an 'easy win' because it's non-patient-facing, has a clear ROI, and sits at the front of treatment, leading to natural and rapid adoption.

The history of AI tools shows that products launching with fewer restrictions to empower individual developers (e.g., Stable Diffusion) tend to capture mindshare and adoption faster than cautious, locked-down competitors (e.g., DALL-E). Early-stage velocity trumps enterprise-grade caution.

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.