"Executives increasingly believe AI will reshape their businesses, but many large organizations are still stuck at proofs of concept. McKinsey's 2025 State of AI report shows widespread experimentation, but real business value is being seen by only a small set of "high performers." 23% of respondents report their organizations are scaling an agentic AI system somewhere in their enterprises, but the use of agents is not yet widespread."
"Many engineering teams still rely on architecture optimized for transactional apps, not for AI systems that mix structured and unstructured data and live event streams. This legacy architecture has three main characteristics that slow AI adoption: rigid schemas and silos, outdated logic, and AI implemented as a bolt-on. First, current ERP and CRM systems are built around rigid schemas and operate in silos."
AI initiatives often stall not due to model quality but because data architectures are poorly suited for agentic systems. Many enterprises use transactional-focused architectures that separate structured tables, search indexes, and vector stores into silos with rigid schemas and incompatible APIs. That fragmentation forces heavy translation, synchronization, and an AI layer to glue disparate sources before semantic understanding can occur. Outdated business logic and bolt-on AI implementations further impede scaling. People, process, and especially inadequate data plumbing are major obstacles to realizing AI-driven business value, limiting deployment to a small set of high-performing organizations.
Read at InfoWorld
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