Use Case Specialization: Discovery, Competition, Objections

Use Case 1: Selling Prior Authorization AI

Who buys

• Payer-side: VP Utilization Management, Chief Medical Officer, Chief Operating Officer, sometimes Chief Information Officer if it's a platform play.
• Provider-side: VP Revenue Cycle, Director of Patient Access, sometimes specialty practice leaders (oncology, cardiology, ortho — the high-PA specialties).

Why now (the urgency story)

• CMS-0057-F deadlines: faster turnaround required for affected payers by January 2026; FHIR-based PA API by January 2027.
• State AI laws (CA SB 1120 and similar) increasing oversight of UM decisions made by algorithms.
• Provider abrasion concerns and high-profile denials reporting putting reputational pressure on plans.
• Margin pressure in MA driving administrative cost reduction urgency.

Discovery question bank — Prior Auth (15)

1. How many prior authorization requests does your team handle per month, and what's the trend over the last 12 months?
2. What's your current median and 95th-percentile turnaround time, broken out by urgent vs non-urgent?
3. Where do you stand against the CMS-0057-F timeline — what's already in motion, what isn't yet?
4. How are requests coming in today — fax, portal, X12 278, Da Vinci PAS? What's the mix?
5. Which criteria sets do your reviewers apply — InterQual, MCG, internal coverage policies? Per service line or universal?
6. What's your auto-approval rate, and where do you think the next 5–10 points of automation could come from?
7. How much of reviewer time is spent on document handling vs. actual clinical judgment?
8. How are you measuring and managing equity in PA outcomes across protected demographics?
9. What's your medical director's review queue look like, and where are they overloaded?
10. How do you handle requests where the clinical evidence is incomplete — what's the back-and-forth pattern with providers?
11. Which service categories have the highest reviewer effort per case (oncology, behavioral health, advanced imaging, surgery)?
12. How do you handle PA denials operationally — what percentage convert to appeals, and what's the overturn rate?
13. What does your provider abrasion data show — are there specific providers or specialties driving disproportionate complaints?
14. For gold-carding: have you implemented or considered it, and what would it take to qualify it operationally?
15. If you imagine the function 3 years out, what does it look like — same headcount with more volume, or restructured around a different model?

Competitive positioning

Common objections — Prior Auth

Use Case 2: Selling Payment Integrity AI

Who buys

• VP / SVP Payment Integrity
• VP / SVP Clinical Operations (for clinical PI specifically)
• CFO at smaller payers
• SIU Director for FWA-focused offerings

Why now

• Medical cost trend continuing high through 2024–2026; every basis point matters.
• Incumbent dissatisfaction: most large payers run multi-vendor PI stacks and are open to displacement on specific categories.
• FWA enforcement: DOJ and OIG visibility on improper payments is high.
• AI maturity making clinical validation (read-the-chart) accessible at scale.

Discovery question bank — Payment Integrity (15)

1. What's your current PI savings as a percent of paid claims, and how has it trended?
2. How is your PI program structured — pre-pay, post-pay, FWA — and which has the most expansion room?
3. Who are your current vendors by category, and what's working / not working with each?
4. What's your provider abrasion budget — how aggressive can you go before contract relationships push back?
5. Where do you see false positives most damaging your provider relationships?
6. For clinical validation specifically: what percent of inpatient claims get reviewed today, and what's the constraint — coder capacity, chart access, vendor cost per review?
7. How are you handling DRG validation today? What's the average dollar lift per validated DRG shift?
8. For COB: what's your accuracy rate on identifying true primary, and what's the financial exposure when you get it wrong?
9. Where are your SIU referrals coming from today — algorithm, tip line, payer cooperation?
10. What's your appeal overturn rate on PI findings, and where are providers winning that they shouldn't?
11. How do you measure auditor productivity — claims/reviewer/day, dollars recovered/reviewer/year?
12. What proportion of your PI activity is on Medicare Advantage vs Commercial vs Medicaid?
13. What rules / policies have you built internally that you'd consider proprietary IP?
14. How are you handling itemized bill review at the high-dollar end — internally, outsourced, automated?
15. If we could add net-new findings (categories your current vendors don't catch) at controlled false-positive rates, what would the procurement path look like?

Competitive positioning

Common objections — Payment Integrity

Use Case 3: Selling Appeals AI

Who buys

• Provider-side: Director of Denials / Appeals, VP Revenue Cycle
• Payer-side: Appeals & Grievances director (yes, payers run appeals operations too — for Levels 1 and 2)
• Health system CFO if denials are at crisis levels

Why now

• Denial rates have been climbing for 3+ years across most major systems.
• Manual appeal letter drafting is expensive ($20–$120 per letter depending on complexity); ROI is fast on automation.
• NSA IDR has created a new appeals-adjacent volume that's growing.
• LLMs have made plausible-sounding letter drafts achievable — the differentiation is citation accuracy and clinical validity, which is exactly what the Builders track Step 3 prepares your product to deliver.

Discovery question bank — Appeals (15)

1. What's your annual appeal volume by level (Level 1 / Level 2 / external review or ALJ)?
2. What's your overturn rate at each level, and how has it trended?
3. What's your average cost per appeal — fully loaded, including chart pull, drafting, submission, follow-up?
4. How do you prioritize which denials get appealed vs. written off — by dollar, by likelihood, by payer?
5. What's your "no-touch" write-off rate — denials that never get appealed because of bandwidth?
6. What's the breakdown of denial reasons (CARC codes) driving your appeal volume?
7. How are your appeals teams structured — by payer, by denial type, by service line?
8. How do you handle medical necessity appeals specifically — who drafts, who signs, what evidence is pulled?
9. What policy and criteria libraries do your appellants use — and how current are they?
10. How do you handle citations in appeal letters today, and how often do you find errors after submission?
11. For NSA IDR specifically: are you using it, how often, and what's your success rate?
12. What's the productivity benchmark — appeals per FTE per month — and how do you measure quality?
13. What technology is in place today — manual templates, RPA, prior automation efforts?
14. What's the relationship with your largest 3 payers — are there pattern denials that warrant systemic conversations vs. case-by-case appeals?
15. If you could 2x appeal capacity at constant headcount, what would you focus the new capacity on?

Competitive positioning

The competitive set here is fragmented: legacy RCM vendors with bolt-on appeals modules, RPA-based players, newer AI entrants, and in-house build. Strong angles:

• Citation accuracy — most LLM-based competitors hallucinate codes and policy sections; your verification layer (per Builders Step 3) is a real differentiator if you can demonstrate it.
• Per-letter cost — be ready with concrete cost-per-appeal math.
• Clinical depth — generic LLM appeals tools fail on complex medical necessity letters. If yours can handle these with Medical Director oversight, the value step is large.
• Workflow integration — appeals tools that live separate from the denials work queue create friction; integrated appeals + queue management wins.

Common objections — Appeals

Use Case 4: Selling Provider Denial Prevention AI

Who buys

• CFO — ultimately. RCM and denials hit the bottom line directly.
• VP / SVP Revenue Cycle
• Director of Patient Access (for front-end prevention)
• Director of Denials (for downstream management)
• CMIO when EHR integration is significant

Why now

• Provider margins compressed through 2024–2025; many systems running negative operating margins.
• Initial denial rates trending up industry-wide, 7–12% common, 11–15% at struggling systems.
• Labor costs in RCM rising faster than collections; cost-to-collect inflation.
• EHR-integrated AI now viable at production scale.

The four CFO KPIs

Every conversation should anchor on these four:

1. Initial Denial Rate — % of claims denied on first submission. Best-in-class < 4%.
2. Final Denial Write-Off Rate — % of denials that become bad debt. Lower is better.
3. Days in Accounts Receivable (especially >90 days) — how long is cash tied up.
4. Cost-to-Collect — admin cost per dollar collected.

Discovery question bank — Provider Denials (15)

1. What's your current initial denial rate, and how has it moved over the last 24 months?
2. What's your final write-off rate on denied claims?
3. What's your AR days breakdown, particularly >90 days?
4. What's your cost-to-collect, and how does that compare to your benchmark?
5. What's the CARC distribution of your denials — top 10 by frequency and by dollar?
6. For each of the top 5 CARCs — where in your workflow does the prevention opportunity live?
7. How is your patient access function structured today — staffing, training, eligibility verification tooling?
8. How are auth requirements checked at scheduling vs. at registration vs. pre-service?
9. How does the front-end coordinate with coders and billing on documentation gaps?
10. What pre-bill scrubbing is in place today — your EHR's native, a clearinghouse, a third-party scrubber?
11. For denials that do happen, what's the workqueue prioritization logic — value, age, payer?
12. How do you handle systemic vs. one-off denials — what's the root-cause analysis cadence?
13. Which payer relationships have the most denial friction, and is that a contract problem or a workflow problem?
14. What automation efforts have been attempted in the past — what worked, what didn't, why?
15. If you could reduce initial denial rate by 2 percentage points and AR days by 5, what would the financial impact be — and what would have to be true for you to believe we could deliver?

Competitive positioning

This space has classic incumbents and a wave of AI entrants:

• Established RCM platforms (Epic Resolute, Oracle Health revenue cycle, Waystar, R1, Conifer) — broad, integrated, but slow on AI.
• Specialized denial-management vendors (Recondo, Vyne, MD Clarity, etc.) — narrower offerings.
• AI-native entrants (you, and your direct competitors) — agility, depth, but proving durability.
• Outsourcing (Conifer, R1, Optum360) — alternative path; some systems prefer full-service over tooling.

The CFO business case template

Common objections — Provider Denials

Cross-use-case reference: the top 10 CARC codes every client engagement professional should know