Enterprise AI fails in execution — orchestrate models into reliable automated workflows

Enterprises — roughly 200,000 large organizations targeted by a $120B market (calculated as $600K ACV each) — are repeatedly seeing AI pilots fail in execution because models alone don't deliver reliable, auditable, cost-controlled workflows: integrations break, retry logic is ad hoc, observability is limited, and business SLAs are missed. The problem is not model quality anymore but orchestration and execution reliability across multiple comparable open and hosted LLMs, vector stores, and downstream systems. You could build an orchestration platform that composes models, connectors, and business logic into reusable, versioned workflow primitives with built-in observability, SLOs, cost controls and policy enforcement, plus automated model routing and rollback. Differentiation comes from productizing reliability (e.g., deterministic retries, transactional patterns, and performance SLAs) and shipping a library of domain-specific workflow templates that reduce integration effort from months to weeks for enterprise buyers. This market is attractive now because models are commoditizing, APIs and RAG patterns are standardizing, and buyers are shifting to outcome-based purchasing — the provided market and revenue scores (95 and 88 out of 100) reflect that. Competition is medium: technically feasible but execution- and sales-intensive; it’s worth pursuing if your team can combine deep systems engineering with enterprise GTM and accept a multi-quarter sales cycle and significant integration work to prove reliability and ROI.