Medium: Beyond the hype — transitioning to AI product management

Published in February 2026 on Medium, this article by Thongchan Thananate makes a direct argument: the 2026 technology market no longer rewards AI enthusiasm. Companies have accumulated enough model capability; the work now is turning that capability into user adoption and revenue. The article is aimed at product managers who are beginning to manage AI features and are finding the gap between a working model and a working product wider than expected.

The value chain framework

The central framework is a three-stage value chain: Capability → Usability → Monetization. Thananate’s diagnosis is that most organizations fail at the second stage. They can identify what an AI model can do, but they cannot make it reliable enough for users to build habits around it. This failure, the article argues, is not primarily technical — it is a product problem. The probabilistic nature of AI creates trust gaps that engineering alone cannot close. Closing them requires deliberate interface design, fallback patterns, and user habit formation.

Three competencies

Three areas structure the practical part of the article. The first is a behavioral economics approach to product design: users carry mental models built on deterministic systems, and when a model produces an unexpected output, trust erodes quickly. The PM’s job is not to improve the model but to redesign the feedback loop so that variance is contained and communicated. The second competency is product explainability — AI decisions need to be explained in terms users can verify, not just summarized. The fraud detection example is instructive: flagging a transaction is only useful if the investigator understands why the flag was raised and can act on that reasoning. The third is execution under real constraints: cross-platform reliability, graceful failure handling, and human-in-the-loop fallbacks treated as a design requirement rather than an edge case.

The customer support case study

The most concrete example in the article is a customer support triage system. A well-designed version routes password resets and similar low-judgment tasks to full automation, escalates emotional complaints to human agents immediately, and prepares draft responses for hybrid cases where human review is needed but manual composition wastes time. Each routing decision is deterministic from the product’s perspective — the probabilistic model is contained within defined categories that the PM defined and the system enforces. Thananate describes this as converting a probabilistic model into a deterministic business outcome, and frames it as the core work of an AI PM.

Who it is useful for

The article is most relevant for PMs who have shipped or are planning to ship an AI feature and are confronting the gap between demo quality and production reliability. It is practical, concise, and grounded in a single well-chosen example rather than broad generalizations.