Collaborative Product Innovation Model with Large Language Models and Agentic Systems

Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan

In Proceedings of the Tenth International Congress on Information and Communication Technology (ICICT 2025), LNNS 1441, pp. 271–281. Springer, 2025.

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Abstract

Product innovation is critical in strategizing business decisions in highly-competitive markets. For product enhancements, the entrepreneur must garner data from a target demographic through research. A solution to this involves qualitative customer feedback. The study proposes the viability of artificial intelligence (AI) as a co-pilot model to simulate synthetic customer feedback with agentic systems. Prompting with ChatGPT-4o’s homo silicus attribute can generate feedback on certain business contexts. Results show that large language models (LLM) can generate qualitative insights to utilize in product innovation. Results seem to generate human-like responses through few-shot techniques and Chain-of-Thought (CoT) prompting. Data was validated with a Python script. Cosine similarity tested the similarity of datasets to quantify the juxtaposition of synthetic and actual customer feedback. This model can be essential in reducing the total resources needed for product evaluation through preliminary analysis, which can help in sustainable competitive advantage.

Keywords: product innovation; qualitative customer feedback; ChatGPT-4o; agentic AI systems; homo silicus; large language model; few-shot inferencing; cosine similarity

About this copy

Preprint (version submitted for peer review). This is a preprint of the following chapter: Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan, Collaborative Product Innovation Model with Large Language Models and Agentic Systems, published in Proceedings of Tenth International Congress on Information and Communication Technology (ICICT 2025), edited by Xin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi, 2025, Springer reproduced with permission of Springer Nature Singapore Pte Ltd. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-981-96-6938-7_23

Cite as

Zachary Matthew Alabastro, Stephen Daeniel Mansueto, Joseph Benjamin Ilagan (2025). Collaborative Product Innovation Model with Large Language Models and Agentic Systems. In Proceedings of the Tenth International Congress on Information and Communication Technology (ICICT 2025), LNNS 1441, pp. 271–281. Springer. https://doi.org/10.1007/978-981-96-6938-7_23