From Concept to Execution: An AI-Agentic Decision Intelligence Framework for Product Planning and Concept Development
<div class="section abstract"><div class="htmlview paragraph">The early stages of product planning and concepting in advanced engineering domains are often hampered by high uncertainty, fragmented decision-making, and unstructured data. Traditional planning methodologies routinely lead to misalignment, inefficient risk assessments, and suboptimal product strategies. To address these challenges, we propose an AI-agentic decision intelligence (DI) framework that leverages Large Language Models (LLMs) to enhance decision-making in product planning and concept development. The proposed framework uses the transformative natural language processing capabilities and comprehensive knowledge of LLMs to capture and refine stakeholder intent, improve stakeholder engagement, and optimize workflow orchestration. Implementation of the framework is facilitated by state-of-the-art and rapidly evolving open-source tools, ensuring scalability and readiness for corporate environments. By enhancing decision confidence, adaptability, and automation, the framework provides a valuable platform for both defense and commercial product development environments.</div></div>
Authors
- Kurt Schaldemose Hansen (ORCID: https://orcid.org/0000-0001-7109-3855)
- Ratna Babu Chinnam (ORCID: https://orcid.org/0000-0003-0980-1544)
- Alper Murat (ORCID: https://orcid.org/0000-0002-1906-6777)
- Stephen H. Rapp (ORCID: https://orcid.org/0000-0001-6753-3029)
- J. Bechtel
- Satyendra Rana
- Todd A. Richman
Institutions
- Broadcom (Israel) (IL)
- Integrity Systems (United States) (US)
- Agile Systems (United States) (US)
- Universidad Técnica de Ambato (EC)
Publication Details
- Journal
- SAE technical papers on CD-ROM/SAE technical paper series
- Published
- 2025-09-16
- DOI
- https://doi.org/10.4271/2025-01-0455
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00