You Can't Cut Your Way to Revenue Cycle Sustainability — Why AI Accountable Partnerships Are Replacing Billing Companies in 2026

At the HFMA Annual Conference 2026, a roundtable of health system CFOs and revenue cycle leaders reached an uncomfortable consensus: you cannot cut your way to sustainable revenue cycle performance. Not with vendor renegotiations. Not with layoffs. Not with offshore outsourcing. The math doesn't work anymore.

Adam Conley said it plainly: "It's impossible for us to cut our way to sustainability. There aren't enough people and contracts to cut. We have to grow our way to sustainability." Jeff Costello reinforced it from the hiring side: "In our market, we can't hire problems away. There's just too much need and not enough revenue cycle talent available."

Meanwhile, hospitals spend $19.7 billion per year on denial appeals, lose $48 billion in net revenue to final denials and uncompensated care, and send 20% of claim reimbursement to vendor partners for outsourced denial work. The billing company model doesn't solve the sustainability problem — it's part of it.

AI accountable partnerships are the structural replacement. Not another vendor. Not another percentage-fee contract with misaligned incentives. A fundamentally different model: fixed-price automation that makes claims clean the first time, operates in labor deserts without hiring, and aligns cost with value delivery instead of processing volume.

The HFMA Consensus: Cost-Cutting Has Hit the Wall

The HFMA Annual Conference 2026 executive roundtable — sponsored by Signature Performance — brought together CFOs and revenue cycle leaders from health systems of vastly different sizes and geographies. They agreed on one thing: the traditional playbook is exhausted.

"You can't manage solely on cost to collect without suboptimizing revenue." — Kevin Boren, East Region CFO, Essentia Health Duluth

Boren's point cuts to the core of the problem. When margin compression hits, the instinct is to cut: reduce staff, renegotiate vendor contracts, consolidate platforms. But cost-to-collect as the primary optimization metric sacrifices revenue. Cheaper denial management doesn't mean better denial management. Cheaper billing doesn't mean cleaner billing. It often means worse.

Matt Leshy of Signature Performance framed the broader imperative: "Health systems cannot cut their way to sustainability. They must protect and grow revenue while using technology responsibly, addressing workforce constraints and creating more accountable partnerships."

That 20% figure — from Dennis Jones at the roundtable — quantifies the cost of the current model. For every dollar recovered through outsourced denial management, twenty cents goes to the vendor. Multiply that across a health system processing thousands of denials per month, and the arithmetic explains why cost-cutting alone can't produce sustainability. The vendor model has a structural margin floor.

The Partnership Problem: Misaligned Incentives at Scale

Billing companies and offshore partners don't fail because they're incompetent. They fail because their operating model rewards the wrong things.

Leshy identified the failure mode explicitly: a partner's operating model that rewards short-term profitability over long-term outcomes. The downstream costs — "inconsistent service, governance friction, diminished institutional knowledge" — are absorbed by the hospital, not the vendor.

The incentive misalignment is structural:

• Percentage-fee contracts mean the vendor earns more when claim volume increases — including rework volume from claims that shouldn't have been denied in the first place
• Denial management outsourcing creates a vendor that benefits from the existence of denials, not from their prevention
• Per-claim pricing rewards processing speed over claim quality — faster throughput generates more revenue for the vendor regardless of first-pass acceptance rates
• Institutional knowledge drain means the hospital loses understanding of its own revenue cycle as more functions shift to vendor staff who turn over annually

Kenneth Hogue raised the revenue retention side: takebacks are rising alongside denial drops. Health systems are fighting to capture revenue and keep it. His principle — "trust but verify" — applies directly to vendor partnerships: audit the services received, verify the outcomes reported, maintain continuity of institutional knowledge.

But "trust but verify" at scale requires infrastructure the current model doesn't provide. When your billing company self-reports performance metrics, you're trusting the entity with misaligned incentives to grade its own homework.

The Labor Desert: Where Hiring Is Not an Option

Cost-cutting assumes you had too many resources to begin with. In revenue cycle labor deserts, the opposite is true: there were never enough people, and the pipeline isn't producing more.

"In our market, we can't hire problems away. There's just too much need and not enough revenue cycle talent available." — Jeff Costello, HFMA Annual Conference 2026

Desmond Jackson described serving a rural area as operating in a "labor desert" with no revenue cycle talent pipeline. This isn't a temporary staffing shortage that resolves with higher wages. It's a structural absence: qualified RCM professionals don't exist in these markets, won't relocate to them, and can't be trained fast enough to address the gap.

The HFMA September 2026 data reinforces why this matters:

• 15% of claims initially denied across the industry
• $19.7 billion per year spent on denial appeals
• Only half of appeals overturned — meaning $9.85 billion in appeal investment produces zero recovery
• $48 billion in net revenue loss from final denials and uncompensated care across 2,300+ hospitals
• 68% of RCM leaders identify inaccurate or incomplete intake data as a primary denial driver (Experian 2025)

A health system in a labor desert facing these denial rates has three options: overpay for scarce local talent, outsource to an offshore vendor with misaligned incentives, or deploy AI agents that operate without geographic constraints. The first option isn't sustainable. The second isn't solving the problem. The third is the structural answer.

Offshore Is the AI Target

Dennis Jones didn't mince words about which jobs AI displaces first:

"AI-driven layoffs are going to affect offshoring companies more. I've been to Mumbai. At 7 p.m., you see thousands of people pouring into buildings to do repetitive, high-volume, sometimes low-balance work. That's the AI target."

This is the honest assessment that the industry avoids stating directly. Offshore billing operations employ large staffs performing repetitive tasks: data entry, claim follow-up, denial categorization, payment posting, eligibility verification. These are exactly the workflows where AI agents achieve the highest displacement ratios — not because the workers aren't skilled, but because the work itself is rule-based, high-volume, and pattern-driven.

The comparison is direct:

Jones's observation about making "claims process clean the first time" is the key differentiator. Offshore billing companies make their money processing claims — including reprocessing claims that were denied because they weren't clean. AI agents that produce clean claims on first submission eliminate the rework that generates vendor fees. The incentive alignment is structural: AI's cost doesn't increase when claim quality improves. The billing company's revenue decreases.

The Takeback Trap: Fighting to Keep What You Captured

Hogue's warning about takebacks rising alongside denial drops reveals a second front in the sustainability war that most revenue cycle conversations miss.

A denial is a refused payment. A takeback is a payment retracted after it was received. Both reduce net revenue, but takebacks are insidious because they arrive after the revenue has been booked, often months later — disrupting cash flow projections, complicating financial reporting, and requiring staff to relitigate claims they thought were resolved.

The OIG's 2026 federal review found that 97% of appealed Medicare Advantage skilled nursing facility prior authorization denials from one major contractor were overturned. When payers deny at 97% error rates and then retract payments on claims that do get through, the revenue cycle becomes a two-front war: fighting for payment and fighting to keep it.

AI addresses both fronts simultaneously:

• Upstream denial prevention catches the conditions that cause denials before claim submission — eligibility gaps, authorization mismatches, documentation insufficiency, coding errors
• Takeback defense monitors post-payment activity for retraction patterns, validates that recoupment requests are contractually justified, and generates automatic responses when they aren't
• Payer rule monitoring detects when payer behavior shifts — new PA requirements, modifier audit patterns, retroactive policy changes — and adjusts workflows before claims are affected
• Audit-ready documentation maintains the evidence chain for every claim decision, so when a payer challenges a payment, the clinical and administrative justification is already assembled

A billing company can manage denial appeals after the fact. It cannot monitor payer behavior in real time across every contract, every code, every modifier, and every authorization status. That's a data problem, and AI solves data problems.

The Accountability Architecture: What Replaces the Billing Company Model

The shift isn't from one vendor to another vendor. It's from a vendor model to an accountability model. The difference is structural.

Fixed Pricing That Aligns With Outcomes

Percentage-fee contracts mean the vendor earns more when the hospital processes more claims — including rework from avoidable denials. Fixed-price AI automation inverts this: the cost is predictable regardless of volume, and the system's economic incentive is to reduce rework (since rework consumes the same fixed capacity without generating additional revenue).

Transparent Real-Time Metrics

The "trust but verify" principle Hogue emphasized requires infrastructure that billing companies don't provide. AI systems generate auditable logs for every action: every eligibility check, every claim submission, every denial response, every payer rule change detected. Performance isn't reported in quarterly business reviews — it's visible in real time.

Institutional Knowledge Retention

When a billing company relationship ends — or when its staff turns over at 30-40% annually — the hospital loses institutional knowledge. Payer-specific rules, contract nuances, denial pattern intelligence, and workflow optimizations walk out the door. AI systems retain and compound institutional knowledge. Every denial pattern detected, every payer rule change identified, every workflow optimization discovered becomes permanent infrastructure.

Clean Claims First, Not Denial Management After

Jones's aspiration — making claims process clean the first time — is the architectural difference. Billing companies are optimized for the second pass: filing appeals, resubmitting corrected claims, following up on unpaid balances. AI systems are optimized for the first pass: verifying eligibility before scheduling, confirming authorization before service, validating coding before submission, and checking payer rules before the claim is generated.

The economics are straightforward. A clean claim costs a fraction of a denied-and-appealed claim. The HFMA September 2026 analysis confirms that hospitals spend $19.7 billion on appeals with a 50% overturn rate — meaning half that investment is pure waste. Prevention at the front end eliminates both the denial and the appeal cost.

The H.R.1 Variable: Self-Pay and Charity Care Are About to Expand

Conley raised a concern that most revenue cycle conversations are ignoring: the H.R.1 (One Big Beautiful Bill Act) impact on self-pay and charity care populations. If enacted, the legislation is expected to reshape Medicaid coverage in ways that push more patients into uninsured or underinsured status.

For health systems already operating in labor deserts with exhausted cost-cutting options, an expanding self-pay population creates a third crisis layer. The billing company model is particularly ill-suited for self-pay management: percentage-fee contracts on low-balance patient accounts generate minimal vendor revenue, which means minimal vendor attention.

AI agents handle patient financial clearance, coverage discovery, Medicaid screening, charity care determination, and payment plan optimization at the same level of attention regardless of account balance. The system doesn't deprioritize low-balance accounts because its compensation isn't tied to collection amounts.

Building the Sustainable Revenue Cycle

The HFMA roundtable consensus points to a clear architecture for revenue cycle sustainability in 2026 and beyond:

1. Deploy AI for upstream denial prevention — make claims clean at the point of origin, not through downstream rework
2. Replace percentage-fee vendor relationships with fixed-price AI automation that aligns cost with outcomes
3. Address labor deserts with technology, not relocation packages — AI agents operate where qualified staff can't be hired at any price
4. Retain institutional knowledge in systems, not in vendor staff — every payer rule, denial pattern, and workflow optimization compounds over time
5. Build real-time transparency into every metric — "trust but verify" requires infrastructure, not quarterly reports
6. Protect captured revenue with automated takeback defense — the second front that billing companies don't address

Boren's mention of ambient listening for documentation quality and HCC coding optimization points to where the value-based care layer connects: AI that improves clinical documentation integrity doesn't just capture revenue today — it positions the health system for value-based contracts where accurate risk adjustment determines capitation rates.

Conley summarized the imperative: "AI is going to lead to the replacement of people who don't know how to use it." The health systems that survive won't be the ones that cut the deepest. They'll be the ones that deployed AI to grow revenue, prevent denials, and build accountability into every transaction — while their competitors were still negotiating percentage points with billing companies.