Scalability is the next filter. Use cases that address local business priorities but can be replicated across plants, regions, or brands unlock what Kaynaz calls“ disproportionate value.”
Safety and compliance form the final layer:“ We don’ t scale what we can’ t govern. Every use case must fit within our risk, cost and compliance framework so that it can be safe, transparent, auditable and sustainable as adoption grows.”
The barriers that matter Kaynaz is candid about the challenges facing AI adoption in the automotive industry. The main obstacles, she believes, are not technological but organisational, human and structural.
“ AI challenges how decisions are made and who owns them,” Kaynaz continues.“ Without clear leadership and operating models, many initiatives never move beyond pilots.”
The evolution of roles across the workforce presents an equally significant challenge. Engineers, operators and managers must shift from doing tasks themselves to supervising, interpreting and orchestrating intelligent systems.
“ When people don’ t understand their new role or aren’ t supported through upskilling and change, adoption slows dramatically,” Kaynaz goes on.“ Decades of systems, processes and fragmented data make it hard to scale AI consistently across plants, regions and partners.”
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