1 The Denial Numbers CFOs Cannot Ignore
Healthcare providers are losing ground. HFMA and Knowtion Health's June 2025 survey of more than 200 revenue cycle and finance leaders found that 82% believe payer denial behavior has gotten worse in the last two years. A separate HFMA and Solventum survey put the share of respondents spending more time on denial management at 69%. Kodiak Solutions reported an initial denial rate of 11.65% through November 2025, up from 11.41% in 2024. The AMA separately found 11% of claims were denied in 2023, up from 8% in 2021.
The cost of fighting these denials is also climbing fast. Premier Inc surveyed 280 member hospitals and found that claims adjudication cost providers more than $25.7 billion in 2023, a 23% jump over the prior year. Nearly $18 billion of that spending went toward disputing claims that should have been paid correctly the first time.
Meanwhile, payers are outpacing providers in AI sophistication. The 2025 CAQH Index found that more than half of health plans already use AI tools in administrative workflows, compared to only about a quarter of provider organizations. That imbalance is a major reason denial volumes keep rising even as provider teams work harder than ever.
2 Why CFOs Are Cautious Even Though They Are Interested
Interest in AI is high. HFMA and AKASA surveyed 519 CFOs and RCM leaders in April 2025 and found that 80% were exploring, piloting, or implementing generative AI, a jump of 38 percentage points in under two years. Yet a separate HFMA and FinThrive survey of 101 organizations found that while 63% already use some form of AI or automation in the revenue cycle, only about 15% report a clearly positive return on investment so far.
That gap between adoption and proven return is exactly why CFOs are approaching vendor selection carefully. The barriers they cite most often are consistent across multiple 2024 and 2025 surveys: limited IT infrastructure, lack of budget, integration challenges, difficulty proving ROI, and concerns about vendor reliability. A separate HFMA and Solventum survey of 272 executives found that 61% named the risk of a PHI breach as their top security concern when evaluating new AI tools.
3 What CFOs Actually Evaluate Before Signing a Contract
When you strip away the marketing language, the evaluation process CFOs use tends to focus on a small number of concrete questions.
Can the vendor prove hard financial savings, not just efficiency claims?
Finance leaders want to see the difference between cash that actually hits the balance sheet and softer productivity gains that are harder to verify. Headcount avoided and hours saved are secondary to recovered revenue and reduced write-offs.
Does the tool integrate cleanly with existing EHR and billing systems?
Integration complexity is consistently ranked among the top three barriers to adoption, and CFOs are wary of tools that require heavy custom IT work. A six-month integration timeline before seeing any value is a deal-breaker for most finance leaders.
What does the vendor's track record look like at organizations of similar size and payer mix?
References and comparable case studies matter far more than product demos. A result at a 50-bed critical access hospital does not translate to a 500-bed academic medical center with a complex commercial payer mix.
How strong is the security and compliance posture?
Certifications such as SOC 2 and HITRUST, along with clear breach response protocols and a signed BAA, are now treated as baseline requirements rather than differentiators. Without these, the conversation does not advance to the finance committee.
Does the tool include a genuine root cause analytics layer?
In the HFMA and Solventum survey, 60% of respondents said they need better insight into why denials are happening in the first place, not just faster ways to appeal them after the fact. A denial analytics layer that feeds back into coding quality and documentation is what separates platforms from point tools.
Is there a human in the loop for quality assurance?
Trust in AI accuracy remains one of the most commonly cited concerns, particularly for clinically complex decisions like prior authorization. CFOs want to see a clear handoff model where the AI does the drafting and analysis, and the analyst reviews and approves before anything goes out.
4 The Capabilities Getting the Most Attention Right Now
Across vendor evaluations and industry commentary, five categories of capability come up again and again in 2026.
Predictive Denial Prevention
Flags risk before a claim is ever submitted, catching coding and documentation errors upstream rather than in the appeals queue.
Automated Appeal Generation
Produces payer-specific language drawn directly from clinical documentation. Not a generic template, a finished letter calibrated to that payer and that denial reason code.
Root Cause Analytics
Connects denial patterns back to coding, documentation, or eligibility issues at the source, rather than simply reporting that claims were denied.
Prior Authorization Automation
CFOs remain cautious about scoping this too broadly given the clinical judgment involved, but targeted prior auth assessment is now a top-five capability request.
Agentic AI Orchestration
Increasingly, CFOs ask whether the platform coordinates handoffs between these functions rather than operating as a single isolated tool.
McKinsey's January 2026 report on the revenue cycle estimated that AI-enabled workflows could reduce cost to collect by 30 to 60%, framing this as a rare opportunity given that revenue cycle operations typically consume 3 to 4% of a health system's total revenue.
5 The Metrics That Actually Justify the Investment
CFOs rarely approve AI spending based on a single metric. Instead, they look at a cluster of numbers that together tell a believable financial story.
Industry benchmarks give a useful frame of reference. Top-performing organizations keep denial rates near 2 to 3%, days in accounts receivable under 35, and clean claim rates above 95%. Kodiak and Enjoin's analysis found that hospital net revenue leakage rose 25%, from $38.6 billion in 2024 to $48.4 billion in 2025, which gives a sense of just how much is at stake industry-wide.
6 What This Means for Building Trust With CFOs
If there is one theme running through the last two years of survey data, it is this: CFOs are not resistant to AI. They are resistant to unproven AI. The organizations moving forward successfully tend to start with a narrow, measurable pilot rather than a sweeping rollout. They set clear baseline metrics before implementation, build performance guarantees into vendor contracts, and require a defined trial period before scaling further.
The AI vendors and internal champions who earn budget approval are the ones who speak the language of the balance sheet first and the language of technology second. Denials will keep rising as payers continue investing in their own AI systems, and that widening gap is exactly why this decision is no longer optional. The organizations that treat AI adoption as a disciplined financial decision, rather than a technology purchase, are the ones most likely to see real results.
Ready to Move From Research to a Real Pilot?
Start your 90-day pilot with DataRovers Denials 360 and start seeing results from day one.
See the DataRovers platform Talk to our team7 Frequently Asked Questions
Kodiak Solutions reported an initial denial rate of 11.65% through November 2025, up from 11.41% in 2024. The American Medical Association separately found that 11% of claims were denied in 2023, up from 8% in 2021. These numbers have not plateaued and are expected to continue rising as payers expand their own AI-based claim review systems.
Interest is high but proof is still limited. HFMA and AKASA found 80% of CFOs and RCM leaders are exploring or piloting AI, while a separate HFMA and FinThrive survey found only about 15% currently report a clearly positive return on investment. The gap is narrowing as more health systems move from pilot to full deployment, but CFOs are right to demand auditable performance data before committing.
Across multiple 2024 and 2025 surveys, the most common barriers are limited IT infrastructure, budget constraints, integration complexity, difficulty proving ROI, and concerns about PHI security. Of those, PHI breach risk was named the top security concern by 61% of executives surveyed by HFMA and Solventum in their review of 272 healthcare organizations.
The most common metrics are initial and final denial rate, days in accounts receivable, clean claim rate, net collection rate, appeal overturn rate, and cost to collect as a percentage of net revenue. Top-performing organizations benchmark denial rates near 2 to 3%, days in AR under 35, and clean claim rates above 95%. These are the numbers a vendor needs to move on a before-and-after basis to earn budget approval.
8 Top Questions People Ask About AI Denial Management
These are the questions CFOs, RCM directors, and revenue cycle analysts search most when evaluating AI denial management investments.
Start with three inputs: your current annual denial volume, your average cost to rework a denied claim (the AHA puts industry average at $43.84), and your current appeal overturn rate. Multiply denied claims by rework cost to get your current annual spend on denial recovery. Then model the scenario where your overturn rate improves from the industry average of 45 to 60% to the 75 to 85% range that AI-powered platforms typically deliver. The net reduction in rework cost plus the incremental recovery from a higher win rate is your conservative ROI estimate. Add days-in-AR reduction multiplied by your daily revenue as a second line. Any vendor that cannot help you build this model before you sign is not worth signing with.
Six questions that separate real platforms from marketing: Does the system generate a complete, payer-specific appeal letter on a single click, or does it assist staff in doing that manually? What is the measured appeal win rate across deployed clients, and can you provide a reference at a comparable health system? What is the go-live timeline from contract signature to first working denials, not first configuration call? How are PHI and claim data handled, and can you provide your SOC 2 Type II and HITRUST certification documentation today? Does the platform include a root cause analytics layer, and can you show me a denial trend report from a live client environment? What performance guarantees are written into the contract, and what happens if the agreed benchmarks are not met within the pilot period?
The ROI math works at smaller scale when the platform does not require a long implementation or large IT investment. A 10-provider physician group billing $8 to 10 million annually with a 10% denial rate and 45% appeal win rate is writing off roughly $400,000 to $500,000 in recoverable revenue per year. If an AI platform moves the appeal win rate to 75% and reduces rework cost by even 20%, the payback is typically well inside 12 months even at smaller claim volumes. The key question is whether the vendor offers a configuration lightweight enough to deploy without a dedicated IT integration project.
Traditional denial management software is a workflow tool. It organizes denials, tracks status, and routes claims to the right staff member. The human still does the analysis, drafts the appeal, and decides the strategy. A denial management AI agent, by contrast, does the analytical and drafting work itself. When an analyst opens a denied claim and clicks a button, the agent reads the denial reason code, retrieves the relevant clinical documentation, and generates a complete, payer-specific appeal letter ready for the analyst to review and approve. The distinction matters for ROI: software reduces disorganization, an agent reduces labor hours and eliminates the dependency on senior appeals writer expertise.
Implementation timelines vary significantly by platform architecture. Legacy RCM suites often quote 6 to 12 months for full deployment. Modern AI-native denial management platforms built on queue-based orchestration models typically go live in 8 to 12 weeks: weeks 1 to 4 cover EHR integration and payer connectivity setup, weeks 4 to 8 cover staff training and parallel testing, and weeks 8 to 12 cover full go-live with performance monitoring. ROI follows quickly: the platforms that start working existing denial backlogs from day one of go-live, rather than requiring an extended tuning period, are the ones reporting sub-90-day payback periods.