Stellantis is making its clearest bet yet that AI is no longer a side project but a core operating system for a modern automaker. The five-year partnership with Microsoft, reported on April 16, 2026, stretches from employee productivity and cybersecurity to customer-facing vehicle insights and in-car assistance. It also arrives at a moment when Stellantis is trying to prove that digital transformation can translate into real-world speed, lower costs, and better customer experience rather than just another glossy technology promise.
The automotive industry has spent the last decade talking about software-defined vehicles, but the gap between ambition and execution has often been wide. Traditional carmakers have had to modernize legacy manufacturing, fragmented dealer networks, and deeply siloed IT systems while also competing with EV-first companies that are native to software culture. That pressure has pushed automakers to look for partners that can help them industrialize AI rather than merely experiment with it.
Microsoft has become one of the clearest beneficiaries of that shift. The company has spent years turning Copilot, Azure, and Copilot Studio into an enterprise AI stack that can serve productivity, governance, data integration, and workflow automation all at once. Microsoft’s own documentation frames Copilot Studio as the place for more sophisticated agents with lifecycle management, connectors, and governance controls, while Microsoft 365 Copilot is positioned for lighter-weight use cases inside the productivity suite. citeturn0search0
That distinction matters because Stellantis is not just buying chatbots. It is stepping into a platform model where AI can be deployed across departments, environments, and business systems with policy controls and telemetry. Microsoft’s guidance stresses structured development, connector governance, environment-level policies, and approval workflows for production deployment. In other words, the platform is designed to satisfy the exact concerns that large manufacturers tend to have: security, compliance, and operational control. citeturn0search0
The timing also reflects a broader industry pattern. Automakers increasingly need partners that can connect enterprise data, field service, vehicle telemetry, and customer interactions into something actionable. A useful AI strategy in this sector is not about chasing the flashiest model; it is about building a dependable system that can surface insights, automate repeat work, and improve service at scale. That is where Microsoft’s enterprise positioning intersects with Stellantis’ need to become faster, more data-driven, and more competitive.
Stellantis itself has been under pressure to prove that scale can be an advantage in the software era rather than a liability. A giant portfolio of brands can either be a source of leverage or a source of complexity. A five-year Microsoft partnership suggests the company is choosing to treat AI as a unifying layer across that complexity, not as an isolated innovation lab. The strategy is bold because it implies lasting dependence on a platform partner, but it is also practical because it may be the fastest way to operationalize AI across a global automaker.
The announcement also suggests that Stellantis is looking for an AI stack with enough governance to satisfy a multinational enterprise. Microsoft’s guidance repeatedly highlights enterprise-grade security, data policies, auditability, and environment controls. Those are not just nice-to-haves; they are the prerequisites for a manufacturer that handles sensitive customer data, dealer relationships, engineering IP, and regional regulatory obligations. citeturn0search0
In strategic terms, the partnership is less about one app and more about a long-term operating model. That matters because the value of AI in manufacturing is often hidden in the seams: faster documentation, better service routing, more consistent diagnostics, cleaner knowledge retrieval, and fewer manual handoffs. The biggest gains usually come from mundane but expensive friction.
That duration implies several things:
Microsoft’s Copilot architecture is relevant here because it is built to ground responses in enterprise data, apply permissions, and deliver outputs within a managed environment. Microsoft describes Copilot as operating through input, grounding, processing, response, and post-processing, with security and compliance controls layered in. That kind of design is particularly useful for a company like Stellantis that needs AI to respect permissions and policy boundaries. citeturn0search0
There is also a competitive reality. Rivals are not standing still. Other automakers are pursuing in-car AI, predictive maintenance, dealer automation, and software-defined vehicle programs. The winner will not necessarily be the company with the most eye-catching demo; it will be the company that makes AI dependable enough to live inside a real organization.
For Stellantis, that means the real question is not whether the company can build an AI assistant. The question is whether it can standardize AI across brands, regions, and teams without losing control. That is much harder, but it is also where the payoff lives.
A platform approach can help in several ways:
For Stellantis, this means the partnership can potentially stretch across internal collaboration, field operations, and customer support without forcing the company to stitch together a dozen unrelated vendors. That kind of consolidation can lower complexity, which is one of the biggest hidden costs in digital transformation. It also gives Stellantis a more coherent vendor relationship to manage.
Microsoft also benefits because automotive is a high-visibility industry with a lot of downstream ecosystem value. If Stellantis uses Microsoft AI in ways that are commercially successful, that becomes a proof point Microsoft can take to other manufacturers, suppliers, and mobility companies. In a market where enterprise buyers are still asking what AI is really for, reference customers matter enormously.
This matters because many AI tools are easy to demo and hard to govern. A company can impress executives with a chatbot in a week, but that does not mean the tool is ready for a regulated enterprise. Microsoft’s ecosystem is attractive because it offers a path from prototype to production without changing the control framework every time the use case gets more serious.
Key strengths Microsoft contributes:
The productivity story is especially important because employee adoption can make or break an AI initiative. If workers do not trust the tools, the project becomes a press release. Microsoft’s platform approach is useful here because it places AI inside systems employees already know, rather than forcing them into a separate, unfamiliar environment. That can lower resistance and accelerate adoption.
Cybersecurity is another obvious internal target. Automakers sit at the intersection of enterprise IT, supplier networks, and customer-facing digital systems, which makes them attractive to attackers. AI can help with detection, triage, and response, but only if the underlying platform is secure and auditable. Microsoft’s enterprise framing is clearly designed to support that need.
The key use cases are likely to include:
This is also where expectations get dangerous. Consumers are not interested in enterprise architecture; they care whether the assistant is useful, fast, and trustworthy. If the experience feels gimmicky, it will be ignored. If it feels intrusive, it will be rejected. The bar is very high because users compare car interfaces not only with other vehicles, but with smartphones and voice assistants they already use every day.
Microsoft’s AI stack can help if it enables better grounding, safer responses, and more connected services. But automotive UX is unforgiving. A poor assistant can create frustration in a space where attention is already divided. That is why any in-car AI effort must be designed with restraint and real-world usability in mind.
For Stellantis, the upside is obvious:
This is important competitively because the AI market is becoming less about generic model access and more about distribution plus trust. Microsoft has both. Its documentation shows a platform designed for secure grounding, governed deployment, and flexible integration across Microsoft 365 and Azure-based environments. citeturn0search0
That positioning makes Microsoft hard to displace. If a customer already trusts Microsoft for identity, collaboration, and cloud, adding AI through the same vendor reduces procurement friction. It also creates a bundled story that rivals have to beat either on capability or on cost. That is a tall order.
That leaves competitors with a few options:
There is also the classic enterprise risk of overpromising on AI outcomes. Generative tools can produce impressive demonstrations without being reliable enough for production use. Microsoft’s own guidance on Copilot Studio repeatedly emphasizes governance, lifecycle management, testing, and monitoring for exactly this reason. citeturn0search0
For Stellantis, the risk is that AI becomes a layer of added complexity instead of a simplifier. If employees need too much training, if the data is too messy, or if the in-car experience is too fragile, the initiative could stall. That would not necessarily mean the strategy is wrong; it would mean the implementation was too ambitious for the current state of the organization.
Risks to watch:
That helps explain why this Stellantis deal matters beyond the two companies involved. It reinforces the idea that AI is becoming a platform layer for industrial and consumer businesses alike. The same logic shows up in manufacturing, logistics, retail, healthcare, and financial services: use AI where it can reduce friction, and anchor it in an ecosystem that enterprise buyers already trust.
The automotive sector is especially ripe for this kind of convergence because vehicles are now rolling software platforms. The line between car, device, and service bundle keeps getting thinner. As that happens, partnerships between automakers and cloud vendors become more strategic, more durable, and more politically sensitive.
That difference should shape expectations:
The second thing to watch is how deeply the AI stack is integrated into Stellantis’ existing systems. A thin layer of copilots is one thing; a real enterprise transformation is another. Microsoft’s documentation suggests that the most durable deployments will be the ones built with lifecycle management, telemetry, governance, and controlled rollouts. citeturn0search0
The third watchpoint is the customer experience. In-car and connected-service features will reveal whether the partnership is delivering something useful or merely technologically impressive. That distinction will determine how the broader market judges the deal.
The bigger story is that the automaker of the future will not just build vehicles; it will orchestrate software, services, and intelligence across the entire customer and employee journey. Stellantis is now betting that Microsoft can help it do that at scale, and that is a bet with real strategic weight.
Source: Investing.com Stellantis, Microsoft sign five-year partnership for AI push By Reuters
Background
The automotive industry has spent the last decade talking about software-defined vehicles, but the gap between ambition and execution has often been wide. Traditional carmakers have had to modernize legacy manufacturing, fragmented dealer networks, and deeply siloed IT systems while also competing with EV-first companies that are native to software culture. That pressure has pushed automakers to look for partners that can help them industrialize AI rather than merely experiment with it.Microsoft has become one of the clearest beneficiaries of that shift. The company has spent years turning Copilot, Azure, and Copilot Studio into an enterprise AI stack that can serve productivity, governance, data integration, and workflow automation all at once. Microsoft’s own documentation frames Copilot Studio as the place for more sophisticated agents with lifecycle management, connectors, and governance controls, while Microsoft 365 Copilot is positioned for lighter-weight use cases inside the productivity suite. citeturn0search0
That distinction matters because Stellantis is not just buying chatbots. It is stepping into a platform model where AI can be deployed across departments, environments, and business systems with policy controls and telemetry. Microsoft’s guidance stresses structured development, connector governance, environment-level policies, and approval workflows for production deployment. In other words, the platform is designed to satisfy the exact concerns that large manufacturers tend to have: security, compliance, and operational control. citeturn0search0
The timing also reflects a broader industry pattern. Automakers increasingly need partners that can connect enterprise data, field service, vehicle telemetry, and customer interactions into something actionable. A useful AI strategy in this sector is not about chasing the flashiest model; it is about building a dependable system that can surface insights, automate repeat work, and improve service at scale. That is where Microsoft’s enterprise positioning intersects with Stellantis’ need to become faster, more data-driven, and more competitive.
Stellantis itself has been under pressure to prove that scale can be an advantage in the software era rather than a liability. A giant portfolio of brands can either be a source of leverage or a source of complexity. A five-year Microsoft partnership suggests the company is choosing to treat AI as a unifying layer across that complexity, not as an isolated innovation lab. The strategy is bold because it implies lasting dependence on a platform partner, but it is also practical because it may be the fastest way to operationalize AI across a global automaker.
What the Partnership Signals
At the simplest level, the deal signals that Stellantis wants AI embedded in the daily mechanics of the company. That means productivity tooling, developer workflows, cybersecurity posture, and customer engagement are all being treated as connected parts of the same transformation. This is exactly the sort of platform thinking that Microsoft wants to promote.The announcement also suggests that Stellantis is looking for an AI stack with enough governance to satisfy a multinational enterprise. Microsoft’s guidance repeatedly highlights enterprise-grade security, data policies, auditability, and environment controls. Those are not just nice-to-haves; they are the prerequisites for a manufacturer that handles sensitive customer data, dealer relationships, engineering IP, and regional regulatory obligations. citeturn0search0
In strategic terms, the partnership is less about one app and more about a long-term operating model. That matters because the value of AI in manufacturing is often hidden in the seams: faster documentation, better service routing, more consistent diagnostics, cleaner knowledge retrieval, and fewer manual handoffs. The biggest gains usually come from mundane but expensive friction.
A five-year horizon matters
A five-year term is long enough to matter and short enough to expose execution risk. It gives both companies a window to integrate, iterate, and expand the use cases beyond the initial announcement. It also makes the relationship feel more structural than opportunistic, which is important in an industry where vendors are often parachuted in for pilot projects and then quietly forgotten.That duration implies several things:
- Stellantis expects AI to become part of core operations, not a temporary experiment.
- Microsoft is willing to compete for a durable seat inside an automaker’s digital stack.
- Both companies are betting that value will compound through repeated deployment.
- The partnership will be judged on measurable outcomes, not keynote language.
- Integration quality will matter more than the novelty of the initial features.
Why Automakers Need Platform AI
Automotive companies are under unusual pressure because they must modernize two worlds at once: industrial operations and digital customer experience. The factory still matters, but so does the app, the connected service layer, the sales platform, and the software update pipeline. AI is attractive because it can sit across all of those surfaces if it is implemented well.Microsoft’s Copilot architecture is relevant here because it is built to ground responses in enterprise data, apply permissions, and deliver outputs within a managed environment. Microsoft describes Copilot as operating through input, grounding, processing, response, and post-processing, with security and compliance controls layered in. That kind of design is particularly useful for a company like Stellantis that needs AI to respect permissions and policy boundaries. citeturn0search0
There is also a competitive reality. Rivals are not standing still. Other automakers are pursuing in-car AI, predictive maintenance, dealer automation, and software-defined vehicle programs. The winner will not necessarily be the company with the most eye-catching demo; it will be the company that makes AI dependable enough to live inside a real organization.
From pilot projects to operating systems
The most important shift in enterprise AI right now is that large companies are moving away from isolated pilots and toward reusable systems. Microsoft’s Copilot Studio materials emphasize lifecycle management, analytics, and governance because organizations increasingly want AI to behave like infrastructure, not a side project. citeturn0search0For Stellantis, that means the real question is not whether the company can build an AI assistant. The question is whether it can standardize AI across brands, regions, and teams without losing control. That is much harder, but it is also where the payoff lives.
A platform approach can help in several ways:
- It reduces the burden of building separate point solutions for each business unit.
- It creates a common governance layer across departments.
- It improves reuse of knowledge, connectors, and data pipelines.
- It helps IT and business teams speak the same operational language.
- It makes future AI deployments faster and less expensive.
What Microsoft Brings to the Table
Microsoft’s strongest advantage is not just model access. It is the combination of cloud scale, identity, security, productivity software, and governance. That is exactly what enterprise buyers want when they are trying to deploy AI at scale rather than merely test it in a sandbox. Microsoft’s documentation makes clear that Copilot Studio supports multistep logic, connectors, role-based access, telemetry, analytics, and deployment across environments. citeturn0search0For Stellantis, this means the partnership can potentially stretch across internal collaboration, field operations, and customer support without forcing the company to stitch together a dozen unrelated vendors. That kind of consolidation can lower complexity, which is one of the biggest hidden costs in digital transformation. It also gives Stellantis a more coherent vendor relationship to manage.
Microsoft also benefits because automotive is a high-visibility industry with a lot of downstream ecosystem value. If Stellantis uses Microsoft AI in ways that are commercially successful, that becomes a proof point Microsoft can take to other manufacturers, suppliers, and mobility companies. In a market where enterprise buyers are still asking what AI is really for, reference customers matter enormously.
Governance is part of the product
One reason Microsoft keeps winning these deals is that it sells governance as a feature, not as an afterthought. Its guidance for Copilot Studio highlights phased governance, safe sharing, auditability, compliance, and lifecycle controls. That is highly relevant for a global automaker that cannot afford to improvise around permissions or data residency. citeturn0search0This matters because many AI tools are easy to demo and hard to govern. A company can impress executives with a chatbot in a week, but that does not mean the tool is ready for a regulated enterprise. Microsoft’s ecosystem is attractive because it offers a path from prototype to production without changing the control framework every time the use case gets more serious.
Key strengths Microsoft contributes:
- Familiar enterprise identity and access controls.
- Deep integration options across productivity and cloud stacks.
- Mature admin and governance tooling.
- A partner story that can extend into devices, endpoints, and service applications.
- An AI platform that already speaks the language of business process.
- Analytics and monitoring that help justify ROI.
How Stellantis Can Benefit Internally
Internally, Stellantis likely sees AI as a way to reduce friction across a sprawling organization. That means helping employees find information faster, improving knowledge sharing, automating repetitive workflows, and supporting faster decisions across teams. Those are not glamorous use cases, but they are often the ones that create the earliest measurable returns.The productivity story is especially important because employee adoption can make or break an AI initiative. If workers do not trust the tools, the project becomes a press release. Microsoft’s platform approach is useful here because it places AI inside systems employees already know, rather than forcing them into a separate, unfamiliar environment. That can lower resistance and accelerate adoption.
Cybersecurity is another obvious internal target. Automakers sit at the intersection of enterprise IT, supplier networks, and customer-facing digital systems, which makes them attractive to attackers. AI can help with detection, triage, and response, but only if the underlying platform is secure and auditable. Microsoft’s enterprise framing is clearly designed to support that need.
The productivity playbook
If Stellantis executes well, the internal gains may follow a familiar pattern: better knowledge access, faster approvals, shorter cycle times, and fewer manual handoffs. These are not theoretical benefits. They are the kinds of incremental improvements that add up across a company with tens of thousands of employees and a very large operational footprint.The key use cases are likely to include:
- Internal knowledge retrieval for engineering and operations.
- Automated drafting and summarization for routine business tasks.
- Workflow assistance for procurement, support, and HR.
- Security analytics and incident response support.
- Smarter search across enterprise documents and systems.
Customer Experience and the Vehicle Layer
The most intriguing part of the partnership is the possibility that AI will influence the customer-facing side of Stellantis’ business. That can include vehicle insights, connected services, and in-car assistance. These are the areas where automakers can turn software into a recurring relationship rather than a one-time sale.This is also where expectations get dangerous. Consumers are not interested in enterprise architecture; they care whether the assistant is useful, fast, and trustworthy. If the experience feels gimmicky, it will be ignored. If it feels intrusive, it will be rejected. The bar is very high because users compare car interfaces not only with other vehicles, but with smartphones and voice assistants they already use every day.
Microsoft’s AI stack can help if it enables better grounding, safer responses, and more connected services. But automotive UX is unforgiving. A poor assistant can create frustration in a space where attention is already divided. That is why any in-car AI effort must be designed with restraint and real-world usability in mind.
Consumer trust will decide the outcome
Consumer-facing AI in cars must solve real problems, not decorate dashboards. Good use cases include service reminders, route-related insights, owner manuals, maintenance guidance, and context-aware support. Bad use cases include vague voice features that sound clever in demos but do not help drivers when they need clarity.For Stellantis, the upside is obvious:
- Better owner engagement after the sale.
- Higher service retention through smarter assistance.
- More value from connected-car data.
- Opportunities for recurring digital revenue.
- A stronger brand perception around innovation.
Competitive Implications for Microsoft
For Microsoft, the Stellantis deal is another sign that enterprise AI is moving into industry-specific workflows. That matters because the company has spent the last few years proving that Copilot can sell across productivity and cloud. Automotive gives Microsoft a chance to show it can also anchor operational transformation in a vertical with real complexity.This is important competitively because the AI market is becoming less about generic model access and more about distribution plus trust. Microsoft has both. Its documentation shows a platform designed for secure grounding, governed deployment, and flexible integration across Microsoft 365 and Azure-based environments. citeturn0search0
That positioning makes Microsoft hard to displace. If a customer already trusts Microsoft for identity, collaboration, and cloud, adding AI through the same vendor reduces procurement friction. It also creates a bundled story that rivals have to beat either on capability or on cost. That is a tall order.
What rivals have to overcome
Competing against Microsoft in this context is not just a technical challenge. It is a platform challenge. Rivals must either provide a dramatically better experience or convince buyers to fragment their stack, which is often a hard sell in large enterprises.That leaves competitors with a few options:
- Offer a more specialized automotive AI product.
- Compete on openness and multi-cloud flexibility.
- Win on price by undercutting Microsoft’s bundle.
- Focus on niche functions Microsoft does not prioritize.
- Build deeper OEM-specific integrations.
Risks and Integration Challenges
Every large AI partnership carries the same hidden danger: the announcement comes fast, but the operational change comes slowly. Stellantis and Microsoft can sign a five-year deal in a day; integrating the workflows, data controls, service models, and governance processes could take much longer. That delay is where many partnerships lose momentum.There is also the classic enterprise risk of overpromising on AI outcomes. Generative tools can produce impressive demonstrations without being reliable enough for production use. Microsoft’s own guidance on Copilot Studio repeatedly emphasizes governance, lifecycle management, testing, and monitoring for exactly this reason. citeturn0search0
For Stellantis, the risk is that AI becomes a layer of added complexity instead of a simplifier. If employees need too much training, if the data is too messy, or if the in-car experience is too fragile, the initiative could stall. That would not necessarily mean the strategy is wrong; it would mean the implementation was too ambitious for the current state of the organization.
Governance and data boundaries matter
The biggest long-term concerns are not flashy ones. They are about data access, auditability, regional compliance, and lifecycle control. In a global enterprise, those issues can be more important than model quality because they determine whether a system can actually be deployed at scale.Risks to watch:
- Vendor lock-in if the partnership becomes too tightly coupled.
- Data governance failures if permissions are not cleanly enforced.
- Slow adoption if employees see AI as an IT initiative rather than a business tool.
- Consumer backlash if in-car features feel unnecessary or distracting.
- Integration complexity across legacy systems and regional business units.
- Security exposure if AI expands the attack surface.
- ROI pressure if results are not measurable within a reasonable time.
Industry Context and Market Timing
The broader market is pushing all large companies toward a similar conclusion: AI is only valuable when it is embedded into existing workflows. Microsoft’s recent documentation around Copilot Studio, agent governance, and secure deployment reflects that shift. It is no longer enough to say an AI system is smart; buyers want to know whether it is governed, observable, and ready for production. citeturn0search0That helps explain why this Stellantis deal matters beyond the two companies involved. It reinforces the idea that AI is becoming a platform layer for industrial and consumer businesses alike. The same logic shows up in manufacturing, logistics, retail, healthcare, and financial services: use AI where it can reduce friction, and anchor it in an ecosystem that enterprise buyers already trust.
The automotive sector is especially ripe for this kind of convergence because vehicles are now rolling software platforms. The line between car, device, and service bundle keeps getting thinner. As that happens, partnerships between automakers and cloud vendors become more strategic, more durable, and more politically sensitive.
Enterprise versus consumer impact
The enterprise side of the partnership is likely to pay off first because internal workflows are easier to control than consumer experiences. The consumer side could ultimately be more visible, but it is also more exposed to reputational risk. A useful internal AI system can quietly improve margins; a poor in-car assistant can become a headline.That difference should shape expectations:
- Enterprise AI is about efficiency, governance, and scale.
- Consumer AI is about usability, trust, and delight.
- Automotive AI must satisfy both at once.
- Success will likely arrive unevenly across use cases.
- The most valuable wins may be invisible to outsiders.
Strengths and Opportunities
The partnership has real strategic upside because it aligns a global automaker with one of the strongest enterprise AI platforms in the market. It offers Stellantis a way to unify internal productivity, security, and customer-facing initiatives under a single governance model, while giving Microsoft another high-profile industry anchor. If the execution is disciplined, both companies can turn the arrangement into a repeatable playbook.- Unified AI strategy across employee, operational, and customer use cases.
- Enterprise-grade governance that suits a multinational manufacturer.
- Faster deployment cycles by building on existing Microsoft tooling.
- Better internal productivity through grounded assistants and automation.
- Improved customer engagement through connected services and insights.
- Stronger security posture if AI is used to support detection and response.
- Commercial differentiation for Stellantis in a crowded automotive market.
Risks and Concerns
The danger is that the partnership sounds more transformative than it is in practice. AI announcements are easy to make, but integration, governance, and user adoption are where the hard work begins. If the project becomes another pilot-heavy story with limited real deployment, the long-term value will be muted.- Vendor lock-in could make future platform changes harder.
- Integration complexity may slow deployment across regions and brands.
- Data quality issues can undermine AI usefulness very quickly.
- Governance gaps can create compliance and security problems.
- User skepticism may limit adoption if the tools feel imposed.
- Consumer disappointment can follow if in-car AI is not genuinely helpful.
- ROI pressure will rise if benefits are not visible within the first phases.
What to Watch Next
The most important next step is evidence. If Stellantis and Microsoft can show concrete workflow improvements, stronger customer engagement, or measurable efficiency gains, this deal will look like a meaningful platform move rather than a marketing headline. The market will also want to see whether the partnership expands beyond initial use cases into a broader operating model.The second thing to watch is how deeply the AI stack is integrated into Stellantis’ existing systems. A thin layer of copilots is one thing; a real enterprise transformation is another. Microsoft’s documentation suggests that the most durable deployments will be the ones built with lifecycle management, telemetry, governance, and controlled rollouts. citeturn0search0
The third watchpoint is the customer experience. In-car and connected-service features will reveal whether the partnership is delivering something useful or merely technologically impressive. That distinction will determine how the broader market judges the deal.
- Pilot-to-production conversion rates.
- Whether AI use cases expand across more Stellantis brands.
- Early signs of employee adoption and workflow efficiency.
- Quality of any connected-car or in-car assistance features.
- Security, compliance, and governance disclosures.
- Evidence of measurable cost or time savings.
- Signs that rivals respond with similar platform partnerships.
The bigger story is that the automaker of the future will not just build vehicles; it will orchestrate software, services, and intelligence across the entire customer and employee journey. Stellantis is now betting that Microsoft can help it do that at scale, and that is a bet with real strategic weight.
Source: Investing.com Stellantis, Microsoft sign five-year partnership for AI push By Reuters
