Kaynaz Behdin does not talk about AI in the way many executives do. There are no vague promises about disruption, no breathless enthusiasm for the technology itself. Instead, her focus is simple: turning technology into measurable business performance.

As Senior Vice President of Digital, Data & AI at Stellantis, the multinational automotive group behind brands including Jeep, Peugeot, Fiat and Vauxhall, Kaynaz oversees a global organisation spanning data, AI, cloud infrastructure, software engineering and enterprise platforms such as ServiceNow. 

Her remit may be broad, but her philosophy is precise. 

“My focus is not AI as experimentation,” she says. “It's AI as an enterprise capability – a disciplined engine for customer satisfaction, value creation, operational performance and speed. 

“That means I spend as much time on governance, operating model, adoption and measurement as I do on technology.”

Operating in a distributed global environment, Kaynaz is also responsible for designing what she calls the right system of collaboration: local execution supported by global governance. Her role “sits at the intersection of AI strategy, enterprise platforms and execution at scale”. 

Kaynaz’s career began in core technology and security before evolving towards transformation leadership across cloud, platforms, data and enterprise operating models. What drove her throughout was a consistent interest in understanding complex systems and making them faster, safer and smarter.

From silicon to steering wheels

The automotive industry was a natural fit for Kaynaz as she sought a new role back in 2024. 

“Few industries combine physical assets, software, data and safety constraints at this scale, while still adapting to individual customer needs,” she explains. “This is precisely what makes technology a true competitive advantage.”

There was also an appeal in the “deeply personal” nature of cars. 

Kaynaz adds: “The experience of driving, of movement, of control, is emotional – and that experience is increasingly shaped by software, intelligence and digital services. That's why transformation in our industry cannot be slow or incremental.”

At Stellantis, the AI strategy is built around a distributed operating model with three layers. First, translating leadership priorities into a single execution framework with clear decision-making processes. Second, embedding AI and Data Business Hubs directly into functions, giving them ownership of the full chain from idea to adoption. Third, providing global platforms and shared talent pools to industrialise delivery rather than simply prototype it.

In practice, AI is applied across Stellantis’ full value chain: conversion and retention improvements in sales, warranty and quality cost reduction, logistics efficiency, manufacturing uptime and faster engineering cycles. Use cases range from dealer and field enablement tools to logistics optimisation and earlier detection of quality defects.

Choosing where AI goes first

With a global operation of Stellantis’ scale, prioritisation is everything. 

Kaynaz uses a value framework with several non-negotiable criteria. Business ownership comes first: every use case must be tied to a clear outcome and have a committed owner in the relevant function.

“We actively look for transformational, cross-functional use cases,” she reveals. “The strongest AI initiatives are those that cut across silos – engineering, manufacturing, supply chain, commercial, finance – and directly support the strategic direction set by our CEO and executive committee.”

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.”

To address this directly, Stellantis runs an AI Academy, a persona-based enablement system covering executive coaching, hands-on workshops and role-based training. The goal here is not simply awareness, but habit and confidence – AI literacy embedded in day-to-day working practice.

Trust, safety and regulation complete the picture: “Automotive is a safety-critical industry. As AI becomes more autonomous, companies must be confident in governance, cybersecurity, explainability and compliance. Without trust, AI simply won't scale.”

Agents and actions

Kaynaz pinpoints four areas where generative AI will have the greatest impact in automotive. 

In-vehicle experience is one, with AI increasingly enabling personalised, adaptive interactions that turn a vehicle into a living digital product. 

Engineering and the product lifecycle is another: “From requirements to design, simulation and validation, generative AI can dramatically compress cycle times and unlock large-scale knowledge reuse. This is a step change in how fast and how well products can be developed.”

Operations and maintenance, as well as commercial performance, round out Kaynaz’s list. Across all four areas, she sees the same fundamental transition: from AI that answers questions to AI that takes action.

At Stellantis, that transition is already underway. The company has deployed an Agent Gateway, a standardised, secure infrastructure layer that allows AI agents to interact with enterprise platforms. A system called Metabot is also rolling out inside Microsoft Teams, bringing the Stellantis agent ecosystem into the workspace employees use daily.

“The Agent Gateway provides a standardised and secure way for agents to interact with enterprise platforms,” Kaynaz explains. “It lets us connect agents to real enterprise tools and knowledge while keeping enterprise controls in place.”

Governance as an accelerator

For Kaynaz, the question of how to balance innovation with trust and safety has a clear answer: governance should function as an accelerator, rather than a brake. 

Risk assessment is built across the full data-to-AI lifecycle, with auditability aligned to incoming regulation, including the EU AI Act. Observability tools track what agents and models do, how they access knowledge and what they cost – before any use case is scaled.

As 2026 progresses, three priorities dominate Kaynaz’s agenda, beginning with embedding AI into the business itself, ensuring functions own it and workflows integrate it. 

The second is industrialising agentic AI at scale, with abstraction layers – software interfaces that protect the organisation from dependence on any single technology – ensuring flexibility as the landscape evolves.

“We work with strong technology partners and will continue to do so," Kaynaz asserts. “But we are very clear: innovation happens everywhere. By relying on open protocols and strong abstraction layers, we ensure we can always combine the best innovation, in the right place, at the right time.”

The third priority is value measurement and compliance by design — ensuring every new use case is safe, transparent and auditable from day one. 

Kaynaz concludes: “It's the year we move from ‘great use cases’ to true AI-first, enterprise-level transformation, with our customers and our people at the centre of everything we do.”