General LLM capabilities keep expanding, but between an impressive demo and production-grade deployment lies a clear gap. From our observation, enterprises commonly face three hurdles when adopting large models.
Hurdle 1: The Model Doesn't Know Your Business
General training corpora can't cover an enterprise's private knowledge — product manuals, process documents, historical tickets. RAG (retrieval-augmented generation) addresses this by injecting enterprise knowledge into generation, but its engineering is far from trivial: chunking strategy, index quality, retrieval ranking and citation tracing all shape the final result.
Hurdle 2: Capabilities Float Outside Business Processes
If AI is just a chat window, value stays at individual productivity. Only when embedded into approval, dispatching and quality-inspection processes does it produce organizational output — which requires deep integration with existing systems, the very meaning of industry application system integration.
Hurdle 3: Output Quality Can't Be Measured
Without evaluation there is no optimization. Enterprises need business-aligned evaluation sets and quantitative metrics that turn "feels good" into "score improved." The evaluation system should be built during POC, not retrofitted after launch.
The common thread: these are engineering problems, not model problems — precisely the value proposition of AI foundation software: delivering model capabilities reliably to the business frontline through engineering.