I’ve been writing the last several weeks about AI pricing, because it’s so critical to the whole AI project. If there’s no way for providers to price AI services that enterprises can stomach at scale, then enterprises can’t justify the “I” part of the critical ROI judgment. The cases of Uber and other businesses exhausting their annual AI token budget in less than half a year have become cautionary tales known to almost anyone paying attention to AI adoption issues. But there are nuances to AI pricing control that make the challenges even more complex.
This piece from Forrester, Your AI Bill Is A Context Problem, argues that, especially with agentic AI, enterprises need a more granular approach than simply capping a team’s token budget. The authors note that, to accomplish its assigned task, an AI agent must assemble a “scaffold” of context that ensures the process’s product is accurate, meaningful, and current.
“Some of that context is visible; much of it is not,” the authors write. “A user sees the prompt they typed, but the model call may also include platform-supplied instructions, prior interaction history, tool metadata, retrieval scaffolding, and other orchestration elements. That hidden context may be necessary, but it is also billable. So, you are not just paying for the answer; you’re paying for the full assembled information scaffolding required to produce the answer in a way that is safe and reliable in a world of probabilities.”
Forrester argues for creation of a new discipline, ContextOps, which they call, “the FinOps of the agentic era.” Its purpose would be to optimize the process of creating this context to ensure tokens are used most cost-effectively. This requires an intimate understanding of the processes and elements that support the agentic workflow, and an effort to streamline those processes wherever possible. As Forrester’s analysts put it: “Enterprises need attribution: which tokens reflected user intent, which grounded the agent in business context, which supported orchestration, and which were avoidable repetition. Without that breakdown, leaders can limit spend, but they cannot improve the economics of the work.”
At least in the Internet age, communications technology has been oriented toward abundance rather than scarcity. Not only did Moore’s Law drive bandwidth abundance, but Metcalfe’s Law encouraged broad-based if not ubiquitous deployment among the user base. We’ve tended to finesse issues like a lack of interoperability by using brute-force methods like simply deploying every application to every user – for example, it was easier to give everyone Teams and Zoom, so each person had the flexibility to use the platform of their choice, than it was to fight with the users by making them pick only one, or with the vendors by demanding their platforms interoperate.
At enterprise scale, AI doesn’t look it can permit this kind of approach, especially in a time of increasingly tight budgets. The cost and complexity of agentic AI workflows may force IT leaders to look for new models of cost-efficiency, and seek out new models for optimizing their spend.
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