From Customer Interaction to Organizational Intelligence: An AI-Native Operating Model for Continuous Product Learning
Abstract
This conceptual research paper proposes an AI-native operating model for transforming customer interactions into organizational intelligence and continuous product learning.
The paper introduces an Interaction-to-Outcome Loop that connects customer interaction, contextual data, AI-assisted interpretation, product decisions, workflow interventions, and outcome measurement. It examines how this model can support continuous product improvement across both business-to-business (B2B) and business-to-consumer (B2C) environments.
The paper also introduces AI as a Performance Operating System (AI-POS) as a conceptual framework for connecting organizational objectives, AI-assisted intelligence, workflow orchestration, performance measurement, and governance.
An illustrative Digital Witness use case demonstrates how authorized contextual information, telemetry, application activity, and support signals could be transformed into structured intelligence for enterprise troubleshooting and product improvement.
The paper develops conceptual propositions and a research agenda for future empirical validation. It is intended to contribute to discussions of AI-native product management, organizational learning, enterprise AI, and continuous product improvement.
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BibTeX
@misc{barigela2026from,
title = {{From Customer Interaction to Organizational Intelligence: An AI-Native Operating Model for Continuous Product Learning}},
author = {Richard Barigela},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23619772},
url = {https://zenodo.org/records/23619772},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - From Customer Interaction to Organizational Intelligence: An AI-Native Operating Model for Continuous Product Learning
AU - Richard Barigela
PY - 2026
DA - 2026/10/24
PB - Zenodo
DO - 10.5281/zenodo.23619772
UR - https://zenodo.org/records/23619772
AB - This conceptual research paper proposes an AI-native operating model for transforming customer interactions into organizational intelligence and continuous product learning. The paper introduces an Interaction-to-Outcome Loop that connects customer interaction, contextual data, AI-assisted interpretation, product decisions, workflow interventions, and outcome measurement. It examines how this model can support continuous product improvement across both business-to-business (B2B) and business-to-consumer (B2C) environments. The paper also introduces AI as a Performance Operating System (AI-POS) as a conceptual framework for connecting organizational objectives, AI-assisted intelligence, workflow orchestration, performance measurement, and governance. An illustrative Digital Witness use case demonstrates how authorized contextual information, telemetry, application activity, and support signals could be transformed into structured intelligence for enterprise troubleshooting and product improvement. The paper develops conceptual propositions and a research agenda for future empirical validation. It is intended to contribute to discussions of AI-native product management, organizational learning, enterprise AI, and continuous product improvement.
ER -