BMW and Capgemini Executives Frame AI as an Automaker 'Superpower'
Two executives with a close view of how artificial intelligence is landing inside the automotive business have laid out their thinking on what it takes to actually capture the technology's value. Serena Striegel, who leads new technologies at BMW Group, appeared alongside Ferdinand Schockenhoff, a director at Capgemini Invent, in a conversation centered on a striking premise: that AI can function as a kind of superpower for automakers — but only for companies that figure out how to wield it deliberately.
The framing matters because the auto industry has spent several years in an awkward middle phase with AI. Nearly every major manufacturer now runs pilots, hackathons and internal tooling experiments. Far fewer have managed to push those experiments into production systems that touch design decisions, factory throughput or the software customers interact with every day. The gap between demonstration and deployment is where the discussion between the BMW technology lead and the Capgemini Invent director becomes relevant to anyone planning automotive strategy.
Who Is Speaking, and Why the Pairing Is Notable
Striegel's role at BMW Group places her at the intersection of research and product reality. Heading new technologies at a premium automaker means evaluating which emerging tools are mature enough to survive contact with engineering standards, regulatory requirements and global supply chains — and which are not. That is a different job from building a prototype in a lab.
Schockenhoff's position at Capgemini Invent, the consulting and digital innovation arm of Capgemini, reflects the other side of the equation: the advisory and systems-integration work that increasingly accompanies large industrial AI programs. Consulting firms of that scale typically help manufacturers map processes, restructure data pipelines and manage the organizational change that determines whether a model in a notebook ever influences a vehicle.
Putting an automaker's technology executive next to a consultancy director is itself a signal. It suggests the conversation is less about raw model capability and more about execution — the unglamorous work of data governance, talent, process redesign and measurement that separates a compelling demo from a durable competitive advantage.
Where AI Could Reshape the Automotive Value Chain
The phrase "full power" implies breadth, and the automotive value chain offers plenty of surfaces for AI to act on. The most credible near-term gains tend to cluster in a handful of areas.
Engineering and product development
Vehicle programs involve enormous numbers of design iterations, simulations and compliance checks. Tools that accelerate simulation, generate candidate geometries or surface relevant past designs can compress development cycles — provided the underlying engineering data is clean enough to trust.
Manufacturing, quality and logistics
Factories generate dense streams of sensor and inspection data. AI applied to quality control, predictive maintenance and production scheduling can reduce downtime and scrap. Supply chain forecasting is another area where better models have obvious financial value, particularly after the disruptions of recent years.
Software, assistants and the customer experience
Modern vehicles are increasingly software-defined, which means AI shows up directly in the product: voice interfaces, driver assistance features, personalization and over-the-air service updates. Here the automaker's challenge is as much about reliability, safety and privacy as it is about model performance.
- Development: faster simulation and design exploration
- Production: inspection, maintenance and scheduling
- Supply chain: demand and disruption forecasting
- Product: in-car assistants, assistance systems and personalization
- Back office: service, sales and aftermarket support
The Real Obstacle Is Organizational, Not Technical
Discussions like this one usually circle back to the same constraint: the hardest part of enterprise AI is not acquiring a model. It is assembling the data, the governance and the people required to make that model dependable at industrial scale. Automotive adds its own pressures — long development horizons, stringent safety expectations, complex supplier networks and a regulatory environment that varies by market.
That combination rewards incremental, well-instrumented deployments over sweeping transformations. A manufacturer that can prove measurable improvement in one factory line or one engineering workflow has something more valuable than a company with dozens of stalled proofs of concept. The "superpower" framing, in that reading, is less about magic and more about accumulated operational advantage: each successful deployment makes the next one cheaper and faster.
Partnerships between manufacturers and consultancies fit that logic. Automakers bring domain depth, proprietary data and product accountability. External partners bring cross-industry pattern recognition, delivery capacity and the ability to staff specialized teams quickly. The tension is familiar — outside help can accelerate execution while also creating dependencies that must be managed carefully.
What to Watch Next
For observers of the automotive sector, the useful question is not whether AI will matter — that debate is effectively settled — but where measurable results will surface first. Watch for automakers publishing concrete metrics tied to AI programs: reduced development time, improved first-pass quality, lower unplanned downtime, or software features shipped on a faster cadence.
Also watch the talent story. The competition for engineers who understand both machine learning and physical products is intense, and it extends well beyond traditional automotive hubs. Companies that treat AI capability as a core engineering discipline rather than a side project are likely to be the ones whose executives can credibly talk about superpowers a few years from now.
Finally, watch how the language evolves. When technology leaders and consultants converge on the same vocabulary — capability, scaling, value realization — it usually indicates that the early experimentation phase is giving way to something more disciplined. Whether that discipline produces the promised superpowers will depend on execution far more than on any single breakthrough model.
This article is based on reporting by Automotive News. Read the original article.
Originally published on autonews.com
