The State of AI in Healthcare RCM: Key Trends, Insights, and Strategies

Healthcare revenue cycle management stands at a transformative moment. Providers are capacity-constrained and face an increasingly complex financial landscape. Margins continue to erode, and administrative overhead threatens to burn out clinical staff and compound labor shortages.

To address these challenges, providers are investing in advanced tech and AI-powered solutions for RCM workflows--and payers are adopting AI tools of their own. This digital transformation is essential, but it requires the right tech investment strategy; without one, payers and providers risk an agentic AI war that drives up costs without improving patient outcomes.

With $740 billion in healthcare administration spending per year, the opportunity for improvement is massive--but only if providers deploy the right technology solutions to reduce costs and improve patient care. Here’s what the state of AI and tech investment tells us about providers’ strategic priorities going forward.

RCM Investment: The State of Healthcare Tech Spending

Healthcare organizations are investing in AI at unprecedented rates. Healthcare AI spending hit $1.4 billion in 2025, nearly tripling 2024's investment of approximately $500 million, according to Menlo Ventures.

In the breakdown of this spending surge, we see clear priorities and trends emerging:

Documentation and Back-Office Functions Remain the Foundation

Two categories dominated that $1.4 billion investment in healthcare AI: ambient clinical documentation ($600 million, growing 2.4x year-over-year) and coding and billing automation ($450 million). These two areas are recurring operational pain points, and tech can deliver measurable ROI.

• Ambient documentation reduces physician burnout by automating clinical note-taking. This represents the most mature AI application in healthcare: according to a report by KLAS Research and Bain & Company, "ambient documentation is furthest along, with roughly one-in-five providers at full rollout and another two-in-five in pilot.”

• Coding and billing automation recovers revenue and reduces operational costs by ensuring accurate code assignment, identifying missed charges, and improving clean claim rates.

Together, these foundational investments account for approximately 75% of healthcare AI spending--reflecting where organizations see the clearest path to ROI.

RCM is the Top Investment Priority for Providers

Nearly half of healthcare providers (49%) cite RCM as a top-three priority in a KLAS Research/Bain & Company report. The reason is straightforward: the use of AI in RCM directly impacts the bottom line through recovered revenue and reduced operational costs.

“RCM’s appeal lies in hard‑dollar ROI: Accurate documentation and coding, resulting in cleaner claims and fewer denials, lead to measurable gains on both the revenue and expense lines.”

- KLAS/Bain, Healthcare IT Investment: AI Moves from Pilot to Production

AI has long-term strategic value for many areas of healthcare operations. But RCM in particular is an area where AI can deliver immediate, quantifiable financial impact, making it attractive to providers facing shrinking margins.

Healthcare is Now Deploying AI Faster Than Any Other Industry

The healthcare industry has accelerated its AI adoption in the last few years. According to Menlo Ventures, "The $4.9 trillion industry, which represents one-fifth of the U.S. economy but accounts for only 12% of software spend, is now deploying AI at more than twice the rate (2.2x) of the broader economy."

The numbers underscore this rapid adoption: 22% of healthcare organizations have implemented domain-specific AI tools, representing a 7x increase over 2024 and 10x over 2023. Health systems lead with 27% adoption, followed by outpatient providers at 18% and payers at 14%.

Where are organizations deploying AI? Top applications share common characteristics: high-volume workflows, significant staff burden, and clear ROI potential.

KLAS/Bain & Company research highlights the top use cases:

• documentation support, including scribe and ambient listening (62%)
• clinical documentation improvement (43%)
• medical coding (30%)‍
• prior authorizations (27%)

Providers Accelerate Buying, While Payers Slow Down

Procurement cycles reveal very different approaches to AI adoption across healthcare sectors. Health systems have shortened average buying cycles from 8.0 months for traditional IT purchases to 6.6 months for AI solutions. Outpatient providers have been even faster, reducing timelines from 6.0 months to 4.7 months--a 22% drop. This is a fundamental shift from the industry’s past reputation for lengthy pilots and slow tech adoption.

But not all sectors are moving at this pace. Payers have lengthened their buying cycles from 9.4 months to 11.3 months (a 20% increase), while pharmaceutical and biotech companies remain steady at around 10 months. A survey by the National Association of Insurance Commissioners found 84% of insurers surveyed used AI, with 50% exploring or using AI to detect fraudulent claims, 68% for prior authorization approvals, 61% for disease management programs, and 45% for sales and marketing.

This divergence seems to represent different approaches to AI adoption: Payers and biopharma remain AI-curious, still in piloting in experimenting mode, while providers are moving forward with AI as a strategic necessity. These trends in AI spend also represent different pressures facing providers and payers. Providers face margin erosion, staffing shortages, and administrative burden that AI can directly address. Payers face higher medical loss ratios, utilization rates, and risk-adjustment scrutiny while they brace for enrollment pressures.

Startups Capture the Vast Majority of AI Spend

The vast majority (85%) of all generative AI spending in healthcare flows to tech startups rather than incumbents. This trend is disrupting traditional healthcare IT dynamics, where established vendors like Epic, Cerner, and AthenaHealth have historically dominated.

“Startups have some advantages: They can move faster and design products natively around AI capabilities, unburdened by legacy technical debt and the bureaucracy of larger organizations.”

- Menlo Ventures, 2025: The State of AI in Healthcare

Healthcare AI has produced eight unicorns and several others valued at $500M-$1B--more than any other vertical AI segment. The success of startups in this area represents not only the huge market opportunity in this area, but also investors’ confidence that AI-native solutions will capture significant market share from traditional healthcare IT vendors. As the market evolves and financial pressures mount, the value of AI-based solutions built from the ground up stands apart from AI features added onto existing tech platforms.

Strategies for Providers: Maximizing ROI While Improving Patient Experience

AI is a massive opportunity for healthcare RCM--but AI cannot be implemented equally or universally across the revenue cycle.

Providers need a strategic approach to AI in RCM (i.e. use AI for the right things) that provides the best possible patient experience (i.e. be clear, transparent, and flexible) for the lowest possible cost (i.e. be efficient and accurate).

"A sharp, thoughtful focus will separate leaders from the rest of the pack. Organizations that succeed in capturing value from technology investments tend to prioritize a small number of high-impact domains, pairing the technology with fundamental workflow and operating model changes."

- KLAS/Bain, Healthcare IT Investment: AI Moves from Pilot to Production

1. Focus on Upstream Denial Prevention

Appealing rejected claims and working through payer disputes is time-consuming and costly. Leading organizations are shifting their focus upstream to prevent denials before they occur.

Investing time into the upstream workflow delivers compounding benefits: better documentation improves coding accuracy, reduces compliance risk, decreases the administrative burden on clinicians, and prevents the downstream denial work that consumes A/R team capacity.

This proactive approach improves the patient financial experience while reducing bad debt and collection costs. Patients appreciate knowing their financial responsibility in advance, and providers avoid the challenges of collecting unexpected balances after services are delivered.

Recommendations:

• Invest in ambient documentation CDI tech: Denial prevention starts at the point of care. Ambient documentation tools ensure complete, accurate clinical records are created in real-time. Clinical documentation improvement (CDI) technologies review documentation for completeness and compliance before billing occurs, catching issues that would otherwise trigger denials.
• Use AI-powered eligibility verification and benefit checks: Real-time eligibility verification during appointment scheduling prevents costly surprises later on. AI-powered tools can verify coverage, identify authorization requirements, communicate patient financial responsibility upfront, and flag potential payment issues before services are rendered.

2. Convert Services to Software

Healthcare IT spending has not kept up with the growth of the overall industry. A tiny portion of overall spending goes toward SaaS and other tech: of the $740 billion spent on healthcare admin in the U.S, only $63 billion goes to healthcare IT.

“Many of AI’s first winners have found success selling into existing IT budgets with solutions that augment existing IT systems with intelligent modules. But the larger and more transformative opportunity lies in automating manual workflows that  were never part of IT budgets, effectively converting services dollars into software dollars for the first time.”

- Menlo Ventures, 2025: The State of AI in Healthcare

AI presents an opportunity to fundamentally reshape this equation--automating manual workflows that were not previously part of IT budgets. For example, prior authorization, patient engagement, and front-office RCM operations are traditionally people-intensive workflows funded through services budgets, but they are great opportunities for IT transformation.

• Prior authorization is one of the largest opportunities to streamline healthcare admin workflows. Physicians and their staff spend 13 hours per week on average completing prior authorization requests; tools that automate prior authorization requests and appeals cut work that previously took hours down to minutes.

• Patient engagement and healthcare front door: Patient engagement and care navigation represent another massive services category ripe for automation. By automating routine patient billing inquiries, appointment scheduling, and care navigation, AI tools significantly reduce the burden on front-desk staff while improving the patient experience through instant, 24/7 availability.

3. Create end-to-end, tech-enhanced workflows

A collection of point solutions--each one addressing an isolated use case or workflow step--introduces technical debt, complexity, complex integration needs, and unnecessary vendor lock-in. For better results, healthcare providers need a complete end-to-end RCM workflow with AI as an integrated component.

Recommendations:

Choose solutions with true EHR-agnostic bidirectional integration:

AI tools that seamlessly exchange data with existing systems deliver exponentially more value than standalone applications. Look for solutions with real-time data synchronization across EHR, practice management, and billing systems, automated updates that flow bidirectionally without manual intervention, and flexible integration capabilities that support your specific technology stack.

Evaluate AI solutions according to four critical criteria:

• Does it reduce the admin burden on clinical staff? Solutions should free up physician and nursing time, not create new documentation or workflow requirements.
• Does it improve the patient financial experience? Technology should make billing clearer, payment easier, and financial navigation less stressful for patients.
• Does it include appropriate governance and human oversight? Particularly for clinical decision support and claims adjudication, human oversight remains essential. Look for solutions with clear audit trails, configurable approval workflows, and transparent decision-making logic.
• Can you demonstrate ROI in under 6 months? Solutions that pay for themselves quickly justify broader investment and build organizational confidence in AI deployment.

4. Deploy AI for the Right Tasks and Workflows

Not all AI applications deliver equal value, and not all workflows benefit equally from automation. Strategic AI deployment requires understanding where AI truly adds value versus where it creates unnecessary complexity or risk.

Focus on high-volume, repetitive administrative tasks first: AI excels at automating routine, rules-based workflows that consume significant staff time but don't require complex clinical judgment. Eligibility verification, benefit checks, payment posting, and basic patient inquiries represent ideal starting points for automation.

• Use AI to augment human expertise, not replace clinical judgment: The most successful AI implementations enhance rather than replace human capabilities. AI can surface relevant information, flag potential issues, and automate documentation--freeing clinicians and staff to focus on complex decision-making and patient interaction.
• Prioritize workflows with clear, measurable outcomes: Solutions that demonstrate concrete ROI within 6 months build organizational confidence and justify further investment.
• Start with high-impact use cases: Begin AI deployment in areas where success builds momentum for broader transformation. Look for applications where success can be quantified through metrics like reduced days in A/R, improved clean claim rates, decreased staff time on specific tasks, or increased patient payment collection rates. Patient billing support, prior authorization automation, and medical coding assistance represent high-impact starting points that demonstrate clear ROI while building organizational competency with AI tools.

5. Emphasize Responsible Use and Governance

As AI capabilities expand, governance becomes increasingly critical. Healthcare organizations must balance innovation with appropriate oversight, particularly given the sensitive nature of patient data and financial decisions.

• Establish clear governance frameworks: Successful AI governance starts with clear policies that define where AI can be used, what decisions require human oversight, how AI systems are monitored and evaluated, and what constitutes acceptable versus unacceptable AI application. Create cross-functional governance committees that include clinical, operational, financial, legal, and IT leadership to ensure AI deployment aligns with organizational values and regulatory requirements.
• Document AI use and decision-making authority: Maintain comprehensive documentation of AI system implementation, including model training data and validation approaches, decision logic and weighting factors, performance metrics and accuracy benchmarks, and audit trails for AI-influenced decisions. This documentation serves multiple purposes: regulatory compliance, quality assurance, continuous improvement, and organizational transparency.
• Maintain human oversight for consequential decisions: While AI can automate routine tasks, certain decisions require human judgment. Maintain human review for claim denials and adverse determinations, high-dollar authorization decisions, unusual or edge-case scenarios, and decisions involving potential fraud or abuse. The goal isn't to slow down operations with unnecessary manual review--it's to ensure appropriate oversight for decisions with significant financial or clinical consequences.
• Test for bias and accuracy regularly: AI systems can perpetuate or amplify biases present in training data. Implement regular testing for demographic disparities in outcomes, accuracy across different patient populations, consistency with established clinical guidelines, and alignment with organizational values and priorities. When testing reveals issues, make iterative improvements to models, adjust decision thresholds or weightings, enhance training data to address gaps, and implement additional oversight for affected scenarios.

Preparing for Healthcare RCM's AI-Powered Future

Healthcare stands at a pivotal moment in its AI transformation. Organizations are investing real dollars behind the promise of improved efficiency, better patient experiences, and sustainable financial operations.

Collectly is an AI-powered patient billing and RCM platform that works with any and all EHR/PM systems. Accelerate cash flow, streamline patient billing, and improve RCM workflows with Billie, the AI agent built for billing and RCM. Collectly users report 95% patient satisfaction with the billing process, and providers attain a 2-3x increase in timely payments.

Talk to Billie today to see what Collectly can do for your practice.