🚨 STOP THROWING MONEY AT AI AGENTS WITHOUT READING THE FINE PRINT. 🚨 A new McK
By Alistairhofert · October 27, 2026 · Curated by George's Blog
🚨 STOP THROWING MONEY AT AI AGENTS WITHOUT READING THE FINE PRINT. 🚨
A new McKinsey & Company QuantumBlack, AI by McKinsey report reveals a massive blind spot in enterprise AI adoption: the real cost of agentic workflows isn't just in the tokens—it’s hiding in plain sight.
⚡ While tech leaders rush to deploy AI agents, this article highlights that "tokenomics" (AI economics) are highly complex and often unexpectedly expensive. Even as individual token prices plummet, enterprise AI spending is spiking. Why? Because most leaders are misjudging the fully loaded costs of these systems.
Here are the 5 biggest takeaways from the report that every frontline leader needs to know:
🤖 The Hidden Cost is Human: While high-compute tasks (like coding) burn tokens, in banking workflows, token costs are only 20-25% of variable run costs. Human oversight accounts for a massive 70-75% of the bill.
📉 Scale & Reuse are Everything: Agent economics favor high volumes and reusable capabilities. Building agents once and deploying them across multiple workflows is the only way to amortize fixed infrastructure and orchestration costs.
🎯 "Completed Work ROI" is the Only Metric That Matters: A single agent might look cheap, but that tells you nothing. Leaders must track the "fully loaded" cost of completing a workflow (humans + agents + deterministic systems) relative to the value generated.
🔄 The Economics are Highly Dynamic: A workflow that is uneconomical today may be a goldmine in six months. Leaders must budget for "evolution"—including continuous tweaking, compliance, and model routing—not just the initial build.
⚙️ The Rise of AgentOps: Just as FinOps revolutionized cloud spending, organizations now need an AgentOps capability. AI unit economics must become a standard part of Quarterly Business Reviews (QBRs).
Business leaders who will win the "enormous prize" of agentic AI are those who master unit economics to drive better decisions.
👇 It would be great to hear from you:
McKinsey & Company raises a critical question for the future: As AI agents work increasingly across functional boundaries, how should cost and pricing decisions be made across your organization?
Are you currently tracking the "human oversight" costs of your AI agents?
💬 Drop your thoughts and strategies in the comments below!