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Chronicles

The story behind the story

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Manufacturers say that AI is fundamentally changing how some new products are created, finding unexpected solutions and reducing R&D time from weeks to days

PPG, 3M and other manufacturers say digital tools can suggest counterintuitive solutions and do weeks of work within days

Wall Street Journal John Keilman

Context & Ripple Effects

This moves the AI story from general workplace experimentation toward product-development workflows. Earlier coverage showed companies testing AI for productivity while cautioning that broad gains could take time; manufacturers now describe a more concrete R&D use case.

The report also extends evidence from a corporate-lab study in which AI-assisted teams found more new materials into commercial manufacturing, where PPG and 3M say digital tools are influencing how products are conceived.

First-order effects

  • PPG, 3M and peer manufacturers can shorten early R&D cycles by using AI tools to generate and evaluate less obvious solution paths, moving some work from weeks to days.
  • Product-development teams gain a faster way to explore alternatives, shifting immediate value toward workflows that combine domain experts with AI-generated candidates.

Second-order effects

  • Manufacturing rivals face pressure to adopt comparable AI-enabled R&D processes if faster iteration begins to affect product-launch timing and development costs.
  • The result strengthens the case for moving beyond the earlier phase of company AI experimentation toward deployment in technical functions where outcomes can be measured through development speed and output.

Third-order effects

  • If these gains prove repeatable, product development may become a core arena for AI industrialization, with differentiation increasingly tied to how well manufacturers integrate proprietary expertise and data into R&D workflows.
  • The pattern could gradually reallocate engineering work from producing initial options toward validating, selecting and refining AI-assisted ones; the durability of that shift depends on whether accelerated discovery translates into viable products.

The trend: This is one data point in AI’s shift from general-purpose employee assistance to embedded systems for industrial discovery and development.