The article argues that traditional digitalization has hit its ceiling and only firms that treat AI as a core productivity engine—led personally by CEOs, aligned on strategy, and executed through fast wins, robust mechanisms, and AI‑native organization—can avoid collapse and achieve a second growth curve.
The article breaks down three AI product manager roles—business‑oriented, platform‑centric, and technical—detailing their core responsibilities, essential skills, industry examples, recruitment profiles, transition strategies, and future demand trends, helping professionals choose the right path.
Amid the 2025 surge of large language models, this article demystifies misleading SOTA claims, critiques benchmark reliability, and presents a comprehensive, business‑focused evaluation framework—including dataset construction, metric selection, automated scoring, and practical guidelines—to help developers and product teams choose the right model for real‑world applications.
