Artificial intelligence is being rapidly adopted in healthcare.
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For years, healthcare was written off as a digital laggard—a sector mired in regulation, resistant to change, and a full generation behind every major technology wave. Now, according to a new report from Menlo Ventures, the script has flipped. Healthcare has emerged as the new leader in enterprise artificial intelligence (AI) adoption, deploying AI at more than twice the rate of the broader economy. The findings challenge long-held assumptions about the slow pace of change in healthcare and point to a deeper story: the cost crisis in which US healthcare finds itself is driving a hard look at AI to fuel efficiency and optimization.
How the Report Was Conducted: A Deep Dive Into Healthcare’s AI Reality
Menlo Ventures partnered with Morning Consult, the opinion research firm, to conduct one of the most comprehensive studies to date on AI adoption in U.S. healthcare. Between August and September 2025, the firm surveyed more than 700 executives across the healthcare ecosystem—including:
• 410+ technology decision-makers at hospitals, health systems, and outpatient providers
• 120+ senior insurance and benefits leaders at national and regional payers
• 170+ executives from pharmaceutical and biotechnology companies
Respondents represented organizations of varying size, geography, and scope from community hospitals to national systems, from Medicare Advantage plans to biotech innovators. The survey was supplemented by more than 20 in-depth interviews with industry leaders and Menlo’s own portfolio insights across AI and digital health investments.
This blend of quantitative and qualitative inputs gives the report unusual breadth. It doesn’t just measure attitudes. It maps how capital, strategy, and innovation are converging in real time across every major healthcare vertical.
From Skepticism to Scale: The Numbers Tell a Story
The data is striking:
• 22% of healthcare organizations have already implemented domain-specific AI tools, a 7× increase over 2024 and 10× over 2023.
• Health systems lead with 27% adoption, followed by outpatient providers (18%) and payers (14%).
• By comparison, just 9% of companies in the broader economy have adopted AI, and most of those rely on general-purpose tools rather than healthcare-specific applications.
In a $4.9-trillion industry that makes up one-fifth of the U.S. economy but only 12% of national software spending, this represents an extraordinary acceleration.
Big Bets from Big Names: The AI Imperative
If 2023 was the year of curiosity, 2025 is the year of conviction. Major systems are moving fast:
• Kaiser Permanente deployed Abridge’s ambient documentation platform across 40 hospitals and 600+ medical offices—the largest generative AI rollout in healthcare history.
• Advocate Health reviewed more than 225 AI tools and implemented 40 live use cases spanning documentation, imaging, and call-center automation.
• Mayo Clinic is investing more than $1 billion in AI over the next several years, across 200+ projects that extend from administrative automation to direct clinical care.
• SimonMed, one of the nation’s largest radiology groups, is now piloting 50+ AI systems across intake, documentation, and revenue cycle management.
These examples illustrate an industry crossing the rubicon—from testing to scaling.
Healthcare’s New Technology Ethic
Unlike the EHR era, which was driven by regulation and central mandates, this AI wave is decentralized and experimental. The Menlo report finds that high-performing organizations evaluate new tools through three criteria:
1. Maturity of technology: Preference for production-ready, reliable systems that scale fast.
2. Level of risk to patient care: Low-risk administrative use cases move first; patient-facing tools follow with greater scrutiny.
3. Short-term value delivery: Quick wins that generate credibility and build adoption momentum.
Notably, cost is not the top concern. Organizations are willing to pay a premium for trustworthy solutions, knowing the risks of failure in healthcare (operational disruption, clinical error, or reputational harm) are existential.
Procurement Acceleration for Some
The study found that procurement timelines for providers have shrunk:
• Hospitals: from 8.0 months to 6.6 months (−18%)
• Outpatient providers: from 6.0 to 4.7 months (−22%)
In contrast, payer procurement cycles have lengthened to 11.3 months, reflecting risk aversion and regulatory caution. The divergence is striking: providers are now the fastest movers in healthcare technology, a reversal of historic norms.
Where the Money Is Flowing
AI spending in healthcare nearly tripled year-over-year, reaching $1.4 billion in 2025. Three areas dominate:
• Ambient clinical documentation ($600M)
• Coding and billing automation ($450M)
• Patient engagement and prior authorization (10–20× growth YoY)
Crucially, 85% of that spending is flowing to startups, not legacy incumbents. That’s because AI-native firms like Abridge, Ambience, and OpenEvidence are building from the ground up, while traditional vendors like Epic, Oracle Health, and Athenahealth are still layering AI onto legacy platforms.
Methodology Matters
What sets Menlo’s report apart is its granularity. The sample captures a cross-section of the healthcare economy—urban and rural, nonprofit and for-profit, large and small—and accounts for differences in mission, margin pressure, and regulatory complexity. By pairing survey data with investor insights and executive interviews, the report not only quantifies AI adoption but contextualizes it—explaining why certain organizations move faster, where value is accruing, and how incentives differ by sector. In short, it’s not a hype piece—it’s a roadmap.
Industry Pressures and the Acceleration Imperative
Healthcare’s rapid embrace of AI isn’t happening in a vacuum—it’s unfolding under historic pressure. Over the past five years, the industry has faced simultaneous economic, labor, and cultural shocks that have redefined what it means to survive as a healthcare organization. Margins have narrowed. Workforce morale has eroded. Patients, newly empowered as digital consumers, expect seamlessness that legacy systems were never built to deliver.
1. The Economic Squeeze
Hospitals are operating on razor-thin margins, often under 1%. Staffing costs have soared, supply chains remain unstable, and reimbursement rates have failed to keep pace with inflation. In this environment, every CEO and CFO is under pressure to find new forms of leverage—technology that multiplies output without multiplying headcount. AI has emerged as the first credible way to achieve that.
2. The Labor Crisis
Healthcare’s labor challenge is structural, not cyclical. According to the Bureau of Labor Statistics, the U.S. will face a shortage of more than 200,000 nurses and 100,000 physicians by the end of the decade. Many clinicians who stayed through the pandemic are burned out or retiring early. AI tools—especially those that automate administrative work and documentation—are being adopted not just for efficiency, but as a moral imperative to keep clinicians practicing medicine instead of managing bureaucracy.
3. The Expectations Shift
Patients now judge healthcare against the same consumer experiences they get from Amazon or Apple. They expect 24/7 access, personalized recommendations, instant responses, and seamless navigation. The old service model—siloed departments, phone trees, weeks-long waits—simply can’t compete. AI is the only way to meet these expectations at scale and deliver care that feels both personal and immediate.
Why This Time Feels Different
In previous waves of innovation, healthcare’s response was compliance-driven—adopting technology because regulators or payers demanded it. This time, the motivation is existential. Healthcare leaders aren’t adopting AI because they have to; they’re adopting it because they can’t afford not to. AI has become the new determinant of competitiveness, the next dividing line between organizations that thrive and those that fade. It’s no longer a question of if or when. It’s a question of how fast—and whether the human mission of healthcare can evolve as quickly as the technology now demands.
The Takeaway: Efficiency Is Just the Beginning
Healthcare’s AI revolution isn’t theoretical—it’s here. Buying cycles are compressing. Dollars are flowing. Outcomes are emerging. But the deeper opportunity is human, not technical. If we get this right, AI can help restore what medicine has lost: time, empathy, and presence. Behind every statistic in the report lies a promise—that technology, for once, might serve the people who serve others.