An institutional investor based in Asia held a position in a publicly listed logistics software provider whose modules cover routing and telematics, customs and trade compliance, global trade intelligence, transportation management and e-commerce fulfillment. Through 2025 the investment team watched generative and agentic AI move into supply chain software and asked a direct question: does AI threaten the integrity of this holding? Nobody had asked the provider's customers systematically whether they were satisfied, whether they planned to add or drop modules, whether they were evaluating AI-native alternatives, and whether they might build their own AI capabilities on proprietary logistics data. The investor needed that customer evidence, plus an outside view of which competitors and which technology shifts posed the biggest risk, before deciding how to treat the position over the next three to five years.
PP&A designed a two-part voice-of-customer study and ran it in roughly six weeks between December 2025 and February 2026. The qualitative part comprised seven in-depth interviews with logistics and supply chain leaders who use the provider's software in their operations, drawn from third-party logistics, retail, pharmaceutical procurement and freight forwarding in North America and Europe. The interview guide moved from current AI adoption and maturity, through the specific impact of generative AI and large language models, to switching criteria, in-house build appetite and the value of proprietary logistics data.
The quantitative part was an online survey of 94 decision makers at companies running the provider's software, fielded in December 2025 across manufacturing, third-party logistics, retail and e-commerce, warehousing, freight forwarding and carriers, with more than three quarters of respondents in a final or joint decision-making role. The survey measured satisfaction by dimension, expansion intentions, switching intent and drivers, perceived barriers to switching, AI exploration, and awareness and use of the provider's own AI features. PP&A segmented respondents into AI explorers and loyalists, integrated both data sets with desk research on competitor AI roadmaps, and delivered a synthesized report with a module-level risk assessment, three strategic scenarios for the provider and a monitoring framework for the investor.
The study gave the investor customer evidence that no filing or analyst report could supply. The report set out how satisfied customers were on each dimension of the software, whether they planned to add or drop modules, what would push them to switch and what holds them in place, how seriously they were exploring AI-native alternatives, and how far they had adopted the provider's own AI features.
The module-level risk assessment showed which parts of the platform were defensible and which were exposed, and the competitive review named the kinds of rivals that posed a structural threat. The three strategic scenarios and the monitoring framework gave the investment team a basis for deciding how to treat the position over its three-to-five-year horizon, and a set of near-term, medium-term and inflection indicators to track as agentic AI matures.
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