Healthcare organizations are investing in AI faster than ever. AI spending reached $1.4 billion in 2025, which is almost triple what it was a year earlier.
The excitement is real. Executives greenlight pilots. IT teams plug in new tools. Data scientists fine-tune models. Then, quietly, most of these efforts fade out.
MIT’s NANDA initiative found that about 95% of AI projects across industries fail to show measurable returns. Healthcare isn’t far behind. Fewer than one in three pilots ever make it to production, held back by compliance hurdles, fragmented data, high integration costs, and limited internal expertise. The result is more money going in and fewer results coming out.
Many of these failures share a common thread. Teams jump ahead to advanced AI tools without having the necessary infrastructure in place to support them. Again and again, the same three gaps stand in the way.
Most healthcare organizations still rely on decades-old systems built for billing or recordkeeping, not data science. Claims live in one system, EHRs in another, and admin data somewhere else entirely. AI tools that perform well in testing hit a wall when they can’t pull information from the right places. Replacing legacy infrastructure isn’t realistic, and trying to plug in AI solutions rarely works.
A pilot built in an isolated environment, disconnected from an organization’s infrastructure, is easy to spin up (especially with vibe coding!). It’s really hard to put that solution into integrated infrastructure that allows an organization to build on it.
HIPAA laws predate large language models, creating uncertainty about how to apply existing rules to new technology. Questions around “minimum necessary” standards, auditability, data retention, and access controls become particularly complex with AI systems.
HIPAA protections are real and necessary, but they also slow things down. Teams get stuck on questions about who can access what data and how audit trails will work. Many AI vendors can’t give solid answers, so compliance officers say no.
There is increasing pressure to provide quantifiable proof that AI models are safe, effective, and trustworthy. Even when an AI system performs well, it can’t succeed without trust. Clinicians want to understand how an algorithm reaches its recommendations and when they should override it. If those details aren’t clear, the tool may get ignored or sidelined.
Without robust governance frameworks, AI becomes a black box that team members avoid or work around. Several recent lawsuits underscore the regulatory, legal, and reputational risks of overstating model performance in regulated domains like healthcare (i.e., State of Texas vs. Pieces Technologies, a suit that resulted from a healthcare AI company overpromising a low LLM hallucination rate). Vendors can promise transparency, but trust only develops through consistent performance, clear oversight mechanisms, and proven reliability in actual healthcare workflows.
When AI projects stall, the impact extends beyond the price tag of the pilot. Compounding experiments result in new tech debt. Duplicate vendor spend and redundant infrastructure drive up total cost of ownership while delivering minimal return on investment.
After seeing multiple AI pilots fizzle out, stakeholders stop believing the next one will be different. Pilots start to feel like side projects that pull focus from core company priorities.
In the end, organizations find themselves spending heavily on AI while seeing almost none of its benefits.
The healthcare systems that make AI work start with a different question: Not “Which tool should we buy?” but “What are our core business challenges that AI can impact, and what do we need to have in place to make it work here?”
Becoming an AI-enabled organization isn’t about chasing the next product demo. It’s about having the right scaffolding, including solid data architecture, clear governance, and realistic ROI goals.
Compliance and auditability have to be built in from the start. When HIPAA controls, audit logs, and data permissions are part of the core infrastructure, AI deployments move faster and face fewer roadblocks.
Teams that also know where their data lives, how it moves, and what rules apply can integrate structured data into AI-enabled workflows.
Keywell has been working to address the challenges of enterprise AI deployment over the last eighteen months, and, after working through many roadblocks, our solution is now built on several premises:
This approach turns AI from a risky experiment into enterprise-ready capabilities that healthcare teams can rely on.
There are endless AI companies and solutions clamoring for leaders’ attention, but ending the cycle of failed launches requires thoughtfully tackling the invisible forces at play, like integrated architecture, built-in governance, and compliance systems that support real-world use in regulated environments.
Contact us to learn more.
