AI as Consumer and Participant: A Co-Design Agenda for MBSE Substrates and Methodology

TL;DR

This paper advocates for a co-designed approach to develop MBSE models and methodologies that enable AI tools to function as knowledge-based participants, rather than just consumers, in systems engineering.

Contribution

It identifies the gap in current MBSE practices for AI integration and proposes principles for co-designing models and methodologies to support AI participation.

Findings

Current models function as prompts, not knowledge bases.

Different AI tools produce inconsistent results over the same model.

A concrete workflow scenario illustrates the existing gap.

Abstract

AI tools are being deployed over MBSE models today, and those models were not designed for this kind of consumption. The problem is not simply that tools hallucinate: well-prompted frontier models produce competent, useful output over a conformant SysML model, but the reasoning they produce is drawn from training rather than retrieved from the model itself, and different tools over the same model produce different results with nothing in the record to adjudicate between them. The model, in other words, is functioning as a prompt rather than as a knowledge base. Attaching better tools to the same model does not resolve this. The model and the methodology that governs its construction need to be designed together for AI participation, treating the model as a machine-queryable knowledge substrate rather than a structured artefact for human navigation, and that co-design has not yet happened in any systematic way. This paper works through a concrete workflow scenario to show what that gap looks like in practice, proposes three principles that jointly characterise what model and methodology must achieve together, and closes with a call to the community to begin this work before the architectural decisions about AI integration settle without the methodological foundation they require.