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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From Concept to Execution: An AI-Agentic Decision Intelligence Framework for Product Planning and Concept Development

Kurt Schaldemose Hansen, Ratna Babu Chinnam, Alper Murat, Stephen H. Rapp et al.
SAE technical papers on CD-ROM/SAE technical paper series
Big Data and Business Intelligence
article

From Concept to Execution: An AI-Agentic Decision Intelligence Framework for Product Planning and Concept Development

Kurt Schaldemose Hansen, Ratna Babu Chinnam, Alper Murat, Stephen H. Rapp, J. Bechtel, Satyendra Rana, Todd A. Richman
article en

Abstract

<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>

SAE technical papers on CD-ROM/SAE technical paper seriesVol. 1
Broadcom (Israel) (IL), Integrity Systems (United States) (US), Agile Systems (United States) (US), Universidad Técnica de Ambato (EC)
Openalex Percentile: Top 59%
Big Data and Business Intelligence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.