Discussion: View Thread

As organizations deploy AI systems that operate continuously and with increasing autonomy, the economics of these systems are shifting from predictable, budget-based models to usage-driven and probabilistic ones.

We recently examined how traditional financial planning approaches break down in such environments - where costs, revenues, and value creation are tied to system behavior, user interaction patterns, and feedback loops rather than stable demand assumptions.

For analytics and OR professionals, this raises questions about how planning, forecasting, and optimization methods need to evolve when economics become tightly coupled to dynamic system activity. 

Article (California Management Review - Insights):
https://cmr.berkeley.edu/2026/02/financial-planning-for-agentic-ai-systems-managing-volatility-in-the-age-of-autonomy/

Cost structures: How are teams modeling compute and operating costs that vary with system usage rather than stable demand? Are stochastic or simulation methods being used?

ROI under uncertainty: What probabilistic or scenario-based approaches are being applied when adoption, performance, and outcomes evolve over time?

Pricing: Which optimization or analytics methods are emerging for usage-based or performance-linked pricing with feedback loops between demand and system behavior?

Analytics setup: What forecasting pipelines or decision-support systems are enabling continuous planning instead of periodic cycles?

Curious how others are modeling these dynamics as autonomous systems become more prevalent.