This IEEE paper introduces a practical framework for evaluating where AI can effectively support engineering knowledge work while maintaining technical rigor, engineering judgment, and accountability.
Presented and awarded 2nd Place Paper Award at the 2026 IEEE Rural Electric Power Conference (REPC).
As electricity demand grows and engineering organizations face increasing pressure to deliver projects faster, firms are increasingly exploring how AI tools can improve productivity without compromising quality. However, many organizations are adopting AI without a structured method for determining which engineering tasks are appropriate for AI assistance and which should remain primarily human-driven.
This paper presents a framework for evaluating engineering tasks based on their characteristics, helping organizations identify where AI can automate work, accelerate analysis, formalize ambiguous problems, or support human exploration.
Why This Matters
- Engineering organizations are under increasing pressure to deliver more work with limited engineering resources. At the same time, advances in AI have introduced new opportunities to improve efficiency, support decision-making, and accelerate knowledge work.
- The challenge is no longer whether AI can be used in engineering. The challenge is determining where AI can genuinely enhance engineering workflows while preserving technical quality, engineering judgment, and accountability.
- This paper provides a structured methodology for evaluating engineering tasks and identifying the most appropriate role for AI within the engineering design process.
Key Topics Covered
- Framework for evaluating AI suitability across engineering tasks
- Human-AI collaboration in engineering workflows
- Risk-based AI governance and oversight
- Task classification using problem structure and outcome convergence
- Practical deployment strategies for engineering organizations
- Consequence-of-error considerations for AI-assisted engineering work
Framework Overview
The framework classifies engineering tasks based on two key dimensions: degree of problem structure and degree of outcome convergence. These classifications help organizations determine whether AI should be used to automate, accelerate, formalize, or support exploration within engineering workflows.
Authors
Jasmine Badiee Konrad, PhD – NEI Electric Power Engineering
Jake Wiggins, PE – NEI Electric Power Engineering
IEEE Notice
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