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More than $40 billion has gone into enterprise AI, and 56% of CEOs say they have nothing to show for it – the gap has surprisingly little to do with the models
Google now processes 3.2 quadrillion tokens a month, up from 480 trillion a year ago. While vendors compete on per-token prices, the organisations pulling ahead on AI are the ones tracking a different metric entirely: tokens per business outcome. This article explains why per-token price is the wrong number to optimise, introduces tokens per resolved outcome as the unit that makes agentic AI economics legible, and sets out the three architectural patterns – task decomposition, context discipline, and evaluation-driven model selection – that create durable cost and performance advantages in agentic AI programmes.
Unchecked AI acceleration creates significant financial and operational liabilities. Explore how to bridge the Delivery Integrity Gap by shifting from activity-based metrics to Outcome Certainty and Risk-Owned Execution.
A comprehensive guide on bridging the GenAI readiness gap. Covers data engineering for LLMs, token-cost optimisation, prompt governance, and ‘Build vs Buy’ strategies to ensure ROI and scalability in enterprise AI deployments.
