Sizing AI-Driven Enterprise Engagements Under Uncertainty: A Probabilistic Scope and Pricing Framework for Pre-Sales Architects
DOI:
https://doi.org/10.30871/jaic.v10i5.13744Keywords:
Pre-Sales Solution Architecture, AI Engagement Estimation, Probabilistic Sizing, Three-Point Estimation, Multi-Phase Pricing, Scope UncertaintyAbstract
Pre-sales architects who size AI-driven enterprise engagements confront a problem that conventional estimation methods were not built to handle. These engagements combine deterministic components, which established techniques size with reasonable confidence, and probabilistic components, whose effort remains unresolved until implementation begins. Treating every component as deterministic tends to produce chronic underestimation, eroded margins, and delivery teams that inherit commitments they cannot meet. Treating every component as probabilistic surrenders competitive bids to providers willing to offer false certainty. This paper presents a structured framework that sizes each class of component on its own terms and prices the resulting engagement to protect client and provider together. The approach separates stable, fully specifiable work from work that depends on data quality, model behavior, prompt iteration, and evaluation design. Stable components are estimated deterministically. Uncertain components are estimated with three-point ranges aggregated through Monte Carlo simulation rather than summed at their expected values. Pricing follows a multi-phase structure, with discovery, pilot, and scale phases that each carry independent scope, price, and gating criteria, supported by disclosed assumptions that feed contract change management. Operational disciplines of peer review and instrumented post-engagement learning convert individual estimates into an improving organizational capability. A worked illustrative example demonstrates decomposition, ranged sizing, and phased pricing for a customer service assistant engagement. The framework is offered as practitioner guidance grounded in enterprise solutioning experience, with controlled empirical validation identified as future work.
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