Vertical AI vs General-Purpose LLMs
Evidence-based comparison for enterprise decision-making. When to use domain-specific Vertical AI platforms versus general-purpose large language models.
Last reviewed: 2026-01-06
Bottom Line
Vertical AI platforms excel at structured, high-stakes decisions requiring domain expertise, policy compliance, and auditability (e.g., insurance claims, prior authorizations, logistics exceptions). General LLMs excel at unstructured tasks like content generation, summarization, and conversational interfaces. For enterprise operations, Vertical AI provides 85-95% accuracy vs. 60-75% for general LLMs on domain-specific tasks.
When to Use Each Approach
Use Vertical AI When:
- Making high-stakes decisions (claims approvals, prior auth, exception handling)
- Regulatory compliance and audit trails are required
- Domain accuracy >90% is critical for operations
- Decisions must be explainable and defensible
- Integration with enterprise systems (EHR, TMS, Policy Admin) is needed
- Cost and latency must be optimized for high-volume decisions
- Human-in-the-loop review workflows are required
- Subject matter expert knowledge must be preserved
Use General LLMs When:
- Generating content (emails, summaries, reports)
- Conversational interfaces and chatbots
- General knowledge questions and research
- Multi-language translation and localization
- Creative tasks (brainstorming, ideation)
- Low-stakes recommendations
- Rapid prototyping and experimentation
- Tasks where 70-80% accuracy is acceptable
Real-World Scenario Comparisons
Insurance Claims Adjudication
Vertical AI analyzes policy language, claim facts, and adjuster expertise to determine coverage with 92% accuracy. Provides explainable rationale citing specific policy sections. Decision time: 2 minutes.
General LLM may misinterpret policy exclusions, miss jurisdiction-specific rules, and hallucinate policy provisions. Accuracy: 65%. No audit trail. Decision time: 5-10 minutes.
Prior Authorization Decision
Vertical AI matches clinical indicators to payer-specific medical necessity criteria, identifies documentation gaps, generates approval-ready packages. 88% first-pass approval rate.
General LLM lacks knowledge of payer-specific criteria, may recommend outdated guidelines, cannot integrate with EHR for patient history. Not suitable for production use.
Customer Service Email Response
Limited capability. Vertical AI can provide structured responses for specific workflows but not natural, empathetic conversation.
General LLM excels at generating natural, empathetic email responses. Can adapt tone, handle edge cases, and provide personalized replies. Superior for customer communication.
Logistics Exception Resolution
Vertical AI detects exception type, evaluates impact, routes to appropriate workflow, and escalates per SLA. 60% reduction in resolution time.
General LLM can describe exception handling processes but cannot integrate with TMS, execute workflows, or make binding routing decisions. Informational only.