A major share of AI value can come from faster decisions, better resource allocation and fewer missed opportunities.
Read article: The decision dividend: AI value beyond labor savingsMcKinsey’s 2026 survey shows that wider AI use alone does not ensure financial impact; workflow redesign and cost discipline matter.
Read article: The gap between individual AI productivity and enterprise returnsNatural-language access to enterprise data becomes dependable only when semantics, authorization, observability and cost controls surround the model.
Read article: Controls required for enterprise conversational analyticsRunning agents inside the governed data boundary can reduce duplication, egress cost, latency and observability gaps.
Read article: Move AI agents to governed data, not data to agentsAs systems become multi-model and multi-agent, cost, access, tracing and runtime policy must cover the full interaction chain.
Read article: Unified governance for models, tools and AI agentsA practical look at the Open Knowledge Format and why portable context, metadata and documentation matter for more dependable AI agents.
Read article: Making organizational knowledge usable by AI agentsA useful data agent starts with explicit sources, business context and testable outputs—not with the chat interface.
Read article: From natural-language questions to an evaluable data agentDeloitte’s enterprise report distinguishes surface-level AI use from process redesign and deeper business-model transformation.
Read article: Moving from AI experiments to business redesign
