Why Domain-Specific AI Is Reshaping Enterprise Strategy

InnovationWhy Domain-Specific AI Is Reshaping Enterprise StrategyBySam Mugel,Forbes Councils Member.for Forbes Technology CouncilCOUNCIL POSTExpertise from Forbes Councils members, operated under license. Opinions expressed are those of the author. | Membership (fee-based)May 27, 2026, 06:00am EDTSam Mugel, Ph.D., is the CTO of Multiverse Computing, a leader in developing efficient value-driven AI & quantum solutions for businesses. gettyThe promise of large language models in enterprise settings has proven difficult to deliver on. According to the RAND Corporation’s analysis, more than 80% of AI initiatives never reach meaningful production deployment. While this research and most post-mortems point to integration as the cause, that diagnosis understates the problem. Integration failures are usually symptoms of a more fundamental issue where the model was never designed to understand the environment it was deployed in.​The Limits Of Adaptation​General-purpose models are trained on the public internet, encyclopedias, books and code repositories. This gives them remarkable breadth. However, as Roberta Cozza, vice president analyst at Gartner's Technology and Service Providers​, explained in a recent interview: “A generic AI that doesn't speak to the specific challenges, processes and content that an enterprise has is not really helping." In other words, general-purpose models have no exposure to the proprietary knowledge that defines how most businesses actually operate: internal APIs, legacy system logic, industry-specific terminology developed over decades, compliance edge cases and the tacit decision rules embedded in operational workflows that have never been documented anywhere a public model could find them.​​The most common response is fine-tuning or retrieval-augmented generation (RAG). Both approaches layer domain-specific information on top of a general model, improving surface performance without changing how the model reasons. When push...