eBok

Industrializing AI for the battery industry

On the right of the image, a battery engineer works using AI to simulate battery performance. On the left of the image, a battery is shown as a visualisation of the concept he is workong on.

The battery industry is in a multi-domain execution race. While AI experimentation is common, a 2025 McKinsey report shows only 15% of organizations have moved beyond the pilot phase. To compete, manufacturers must evolve toward the agentic enterprise model, where humans and AI agents collaborate using shared context across the entire value chain.

Progress from isolated pilots to a governed agentic enterprise.

A modern gigafactory generates up to 50TB of data daily, yet 90% of this is underutilized. Get the roadmap to turn fragmented battery lifecycle data into repeatable, scalable processes.

Key insights include:

  • The context layer: Creating a semantic thread so AI understands data across engineering and manufacturing.
  • Intelligence portfolios: Applying the right models (prediction, synthesis, or navigation) to specific workflows.
  • Closing the loop: Moving from static dashboards to agents that investigate and execute with human oversight.
  • Govern and scale AI: Establishing the governance, traceability, and auditability required for safety-critical products.

Download the full e-book to see how, by connecting data and embedding human-in-the-loop governance, battery leaders can build a resilient, repeatable enterprise capable of competing with confidence.

Dele