Page 94 - 36-1

Integrating Artificial Intelligence into Product Life Cycle Value and Activity Value Management: A Case Study
of P Channel Agent

as product portfolio adjustments, marketing resource deployment, and sustainability
performance metrics.
Qualitative validation is further conducted through semi-structured interviews with
the business leader (founder). The findings verify interpretability, usability, and strategic

relevance of the system outputs.

3. Findings

The research yields several significant findings.
First, the integration of AI into PLCV-AVM substantially improves predictive
accuracy and managerial relevance. The AI algorithms outperform traditional regression
models in forecasting product profitability and life cycle trajectories, enabling management

to proactively allocate resources and adjust pricing or promotion strategies. This finding
is consistent with recent meta-analytic evidence showing that AI-enabled and automated
systems can achieve performance comparable to or exceeding that of human decision
agents across a broad range of customer-related outcomes (Gelbrich, Roschk, Miederer,

and Kerath, 2025).
Second, the study demonstrates that combining activity-level data with product-
level life cycle indicators enhances visibility into how operational processes contribute to
long-term product value. The cross-level integration allows decision-makers to trace the

impact of each activity on value creation and to redesign processes accordingly. Beyond
predictive accuracy, the PLCV-AVM system reshapes how managers conceptualize product
performance by shifting evaluation criteria from short-term margins to projected long-
term value contribution. Such a shift aligns with emerging perspectives that emphasize

the strategic role of customer insights in guiding organizational learning and innovation-
oriented decision processes (Stremersch, Cabooter, Guitart, and Camacho, 2025).
Third, the model reveals that demographic and transactional factors, including
customer mix, order frequency, and service complexity, significantly influence PLCV, and

their effects vary across life cycle stages. This highlights the necessity of integrating both
B2B distributor data and B2C consumer insights to capture holistic product performance,
consistent with recent research emphasizing that customer insights must be translated from
individual-level observations into firm-level decision support and innovation-relevant

86