Modernising Toyota’ s manufacturing operations
However, leaving behind legacy systems and scaling AI out of an initial pilot phase can be done successfully – as seen in AWS’ s partnership with Toyota Motor North America.
“ Toyota North America’ s legacy mainframe systems managed 90 % of their supply chain operations,” Bill says.“ And a mainframe outage would basically halt car sales entirely.”
To evolve this system, AWS deployed AWS Transform for Mainframe, using AI to analyse millions of lines of code. This, Bill says, was the first Agentic AI service designed to modernise mainframe workloads at scale.
“ After validation from Toyota’ s own COBOL engineers, the teams produce complete, high-quality documentation in a single day. This is work that would have taken them months to do.”
On the business side, this transformation helped shift Toyota from legacy buildto-stock push models to a modernised, customer-centric‘ pull’ model.
“ This really allowed the seamless integration between sales and product manufacturing, fundamentally enhancing the customer experience and unlocking new profit opportunities,” says Bill.
Building future-ready systems
“ We’ re seeing a shift from AI as a tool to AI as an autonomous operating layer for manufacturing and supply chains,” Bill says.
He suggests that future systems will autonomously orchestrate demand sensing, inventory positioning and logistics optimisation within defined guardrails – resolving supplier descriptions“ before a human even sees the alert”. However, he advises that manufacturing leaders first ensure they have a“ unified data fabric” before they apply AI models.
“ Manufacturing organisations’ first priority should be connecting the manufacturing floor data to the ERP system, to supplier networks, to logistics platforms,” Bill says.“ AI is only as good as the data it can access in real time.”
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