Engineering Responsibility and Capability: A Scope-Based Organizational Framework

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Abstract

Engineering organizations depend on expertise that differs not only by discipline and method, but by the scope of responsibility assumed. Yet responsibility is frequently conflated with capability, technical sophistication, or organizational position, complicating organizational design, workforce planning, coordination, and development. This distinction is becoming more consequential as generative artificial intelligence (GenAI) rapidly expands the analytical, modeling, synthesis, and design capabilities available to engineers without necessarily expanding their judgment, authority, or accountability. This research develops a non-hierarchical conceptual taxonomy that separates engineering responsibility from the capabilities used to discharge it. A structured conceptual-analysis and typology-development approach synthesizes literature in engineering design, systems engineering, organizational learning, architecture, and professional engineering around three constructs: outcomes, boundaries, and consequences. The framework identifies four complementary responsibility scopes: foundational understanding, domain-constrained technical realization, integrated technical realization, and system mission and outcomes. An orthogonal dimension organizes capability into four cross-cutting categories: knowledge and skills, methods and processes, tools and technologies, and learning and development. The central proposition is that responsibility defines scope, while capabilities define means. The framework provides engineering managers with a common language for mapping responsibility needs to capability, allocating and coordinating responsibility, identifying gaps, and sustaining and adapting organizational capability. As GenAI increases technical reach, the framework emphasizes the growing importance of judgment, boundary awareness, validation, continuous learning, and explicit accountability.