Battery recycling has emerged as one of the most critical, and problematic, bottlenecks within modern e‑waste management systems. Batteries are chemically diverse, often damaged or degraded at end‑of‑life, tightly integrated into products, and subject to rapidly evolving regulatory requirements. Unlike battery production, recycling operates under fundamentally different conditions: heterogeneous inputs, safety‑critical handling, uncertain states of charge, and strong economic pressure. As a result, battery recycling cannot be scaled through isolated process optimization alone.

This panel addresses battery recycling as a system‑level challenge that requires alignment across four domains: material realities, industrial automation, artificial intelligence, and product design. Starting from the physical and regulatory constraints of real battery recycling streams, the discussion explores where conventional automation approaches reach their limits and why many pilot solutions fail when transferred to industrial scale. Building on this foundation, the panel examines how recent advances in AI enable new levels of flexibility, perception, and decision‑making—while also critically assessing where AI capabilities are still immature or economically unjustified.

A central focus of the discussion is the missing feedback loop between downstream recycling processes and upstream product design. “Design for recycling” has long struggled to influence real products; however, emerging automation and AI‑enabled recycling technologies may fundamentally change what is measurable, enforceable, and economically viable in product architecture.

By combining perspectives from battery recycling, industrial automation, AI integration, and strategic product design, this panel offers a grounded, non‑idealized view of what it will take to make battery recycling safe, scalable, and economically sustainable. The session is designed for practitioners, technology developers, OEMs, and policymakers seeking clarity on what can change today - and what must change next.