Presented by CertiniaThe initial euphoria around Generative and Agentic AI has shifted to a pragmatic, often frustrated, reality. CIOs and technical leaders are asking why their pilot programs, even those designed to automate the simplest of workflows, aren’t delivering the magic promised in demos.When AI fails to answer a basic question or complete an action correctly, the instinct is to blame the model. We assume the LLM isn’t “smart” enough. But that blame is misplaced. AI doesn’t struggle because it lacks intelligence. It struggles because it lacks context.In the modern enterprise, context is trapped in a maze of disconnected point solutions, brittle APIs, and latency-ridden integrations — a “Franken-stack” of disparate technologies. And for services-centric organizations in particular, where the real truth of the business lives in the handoffs between sales, delivery, success, and finance, this fragmentation is existential. If your architecture walls off these functions, your AI roadmap is destined for failure.Context can’t travel through an APIFor the last decade, the standard IT strategy was “best-of-breed.” You bought the best CRM for sales, a separate tool for managing projects, a standalone CSP for success, and an ERP for finance; stitched them together with APIs and middleware (if you were lucky), and declared victory.For human workers, this was annoying but manageable. A human knows that the project status in the project management tool might be 72 hours behind the invoice data in the ERP. Humans possess the intuition to bridge the gap between systems.But AI doesn’t have intuition. It has queries. When you ask an AI agent to “staff this new project we won for margin and u …
