Market Data
Supporting data for market size and dynamics.
TAM / SAM / SOM
Total Addressable Market (TAM)
Healthcare back-office is massive:
- Utilization management alone is a multi-billion dollar function across payers
- Revenue cycle management, care management, clinical operations add more layers
- Total healthcare admin spending: ~$1 trillion annually in US
Needs: Specific TAM number with source
Serviceable Addressable Market (SAM)
Health plans + UM/UR functions we can realistically reach:
- Regional health systems (3-20 hospitals)
- Specialty payers and TPAs
- UM/UR outsourcing companies
- Large physician groups with value-based contracts
Size sweet spot: $50M - $500M organizations
Needs: Specific SAM number with source
Serviceable Obtainable Market (SOM)
Near-term capture potential based on current pipeline and sales capacity.
Needs: Calculation based on pipeline × conversion × deal size
Key Statistics
| Stat | Source | Why It Matters |
|---|---|---|
| 95% of AI projects fail to create business value | MIT 2024 | Implementation is the bottleneck, not models |
| 74% of companies struggle to scale AI | Industry survey | Talent gap is real |
| Only 1% have reached full AI maturity | Industry survey | Massive greenfield opportunity |
| 800% growth in FDE hiring demand | Job market data | FDE model is proven and growing |
| 90%+ automation potential for PA | CMS projections | Regulatory tailwinds |
Industry Trends (Tailwinds)
AI in Healthcare
- LLMs crossed capability threshold for judgment-intensive tasks
- Healthcare organizations have budget and pain, lack talent
- AI models commoditizing — implementation expertise is scarce
Regulatory
- CMS mandating electronic prior authorization
- Interoperability requirements pushing digitization
- Compliance complexity creates switching costs
Healthcare Operations
- Nurse burnout crisis → need to reduce admin burden
- Prior auth backlogs → quantifiable pain point
- Payer-provider friction → room for better tools
FDE Model Validation
- Palantir proved the model at scale
- OpenAI, Ramp, Commure adopting similar approaches
- Enterprise AI needs hands-on implementation, not just software
Competitive Landscape
Incumbents
- Epic, Cerner — don't understand AI, move slow
- Legacy UM software — point solutions, not AI-native
AI Startups
- Most are wrappers — no healthcare depth
- Don't understand incentives, relationships, real problems
- High failure rate in healthcare specifically
Our Position
- Bridge the gap that kills both categories
- Healthcare PE + engineering + non-traditional thinking
- FDE model creates stickiness incumbents can't match
Sources
- FDE Manifesto v1:
research/fde-manifesto/v1-draft.md - Healthcare AI Intel:
research/market-intel/healthcare-ai.md - Palantir Analysis:
research/fde-manifesto/palantir-analysis.md
Additional sources to add with specific citations