Enterprise AI Adoption Stalls at Evals and Orchestration
arjunrajlab · x · 2026-07-17
This post summarizes three major bottlenecks in enterprise AI adoption: Evals, system orchestration, and talent.
- Evals: Enterprises often struggle to articulate their true use cases and translate goals into offline/online evaluations. Evals must cover business objectives while helping select models based on the quality-cost-latency tradeoff.
- Harness: The real challenge isn't building a chatbot, but creating a system independent of the model itself to handle routing, multi-agent orchestration, context management, tool calling, and memory.
- Talent: Talent capable of building this infrastructure is extremely scarce. The author argues this is the critical bottleneck preventing enterprises from moving beyond the "basic chatbot" phase.
The original quote further notes that most enterprises remain stuck in scenarios characterized by "80% single-turn, semi-deterministic, with heavy guardrails or human intervention," far from adopting complex custom models at scale. Once they enter multi-agent and context-retention scenarios, model portability drops, forcing enterprises to continuously update their evals and system architectures.
Related event: Evals, Orchestration, and Talent: The Three Bottlenecks in Enterprise AI(2 posts)→
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