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Mercedes-Benz’s AI strategy, as Group CIO Katrin Lehmann described it in a September 2024 interview, began with a less glamorous task: simplifying the technology estate. Her approach paired application rationalization and “Radical Standardization” with selective AI investment, human oversight, and stronger organizational learning. It is a useful executive case study in how technology choices can affect a company’s costs and capacity to innovate—not a current update on Mercedes-Benz’s systems or leadership.
Lehmann’s mandate: make room for change
When CIO published its interview on September 12, 2024, Lehmann said she had been Group CIO of Mercedes-Benz Group AG for about four months and led an IT organization of roughly 11,000 employees. Those figures describe the interview’s moment, not the company’s current organization. Read the interview.
The challenge she outlined was a familiar one for a large multinational: a historically accumulated application estate had to support new digital services and rising interest in AI, while the business also faced the cost and complexity of maintaining existing systems. Automotive companies were changing how they develop, build, sell, and support vehicles, amid differing regional compliance demands. Lehmann’s framing was not “add AI everywhere.” It was to simplify the foundation and direct limited people and investment toward capabilities with a business case.
Application rationalization comes before expansion
Lehmann described an effort to catalog applications and make explicit choices about what to retain, transform, or remove. The point is not simply to shrink a software inventory. Redundant applications and overlapping programs consume maintenance effort, complicate integration, and compete with new work for specialist attention. A clearer portfolio can make both technology costs and dependencies easier to manage.
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The interview does not name systems Mercedes-Benz planned to retire, provide a timetable, or quantify savings. Any rationalization effort also has to protect business continuity: older applications can contain undocumented rules or support critical processes. Inventory and dependency mapping, migration planning, and rollback options are therefore practical safeguards, even though the interview does not describe Mercedes-Benz’s specific methods.
What “Radical Standardization” means
Lehmann’s phrase describes a reuse-first decision rule, not a ban on custom software. In practice, the logic is to start with a business need, check for an internal component that already meets it, and then consider a standard product before commissioning something bespoke. Custom development still makes sense when business requirements or genuine differentiation justify the added complexity.
Standardization can reduce duplication and free capacity for innovation, including AI. Its trade-off is that a standard product may constrain business-specific capabilities or deepen dependence on a vendor ecosystem. The decision CIOs need to make is not “custom or standard everywhere,” but which capabilities are distinctive enough to warrant bespoke investment. Lehmann’s interview gives no quantified savings target, platform list, or implementation schedule for the initiative.
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What the interview says about AI at Mercedes-Benz
The examples span software development, customer service, sales, and vehicle development. They indicate areas of interest and activity as Lehmann described them in 2024; the interview does not establish present-day availability, performance, or scale for most of these uses.
Developer assistance
Lehmann said more than 5,000 software developers were using GitHub Copilot and that users reported saving at least 30 minutes per day. That is a self-reported productivity claim, not an independently verified measurement of output. The interview does not disclose the number of respondents, how savings were calculated, the deployment configuration, or effects on code quality, security, review time, or rework. Adoption and reported time saved are useful signals, but they do not by themselves establish net productivity gains.
Product discovery and digital commerce
The interview describes a virtual assistant intended to help customers find information about Mercedes-Benz products and services, alongside an expansion of online stores where customers could complete vehicle purchase and leasing processes digitally. It does not specify markets, supported languages, public availability, transaction capabilities, or customer outcomes. These examples should not be read as evidence that end-to-end online buying was available everywhere.
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Customer-service triage
AI was also being used to categorize and prioritize incoming customer-service emails, routing them to specialist teams according to their content. That is a targeted support function, not evidence that AI ran customer service end to end. The source reports no accuracy, escalation, response-time, or satisfaction metrics.
Development and digital twins
Lehmann linked AI and digitization to digital twins and to changes in how vehicles are developed and built. The interview offers no technical architecture, data sources, validation process, or production-impact figures. It therefore signals strategic importance rather than documenting a particular digital-twin deployment in enough detail to assess its results.
Where automation should stop
Lehmann’s example of a vehicle breakdown illustrates the boundary she drew around automation: a customer in a stressful situation may need human contact. AI can help sort routine requests or surface information, but a system that fails to recognize when a person needs direct assistance can make an already difficult experience worse. That makes escalation design part of the service, not an afterthought.
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The same principle applies to enterprise AI more broadly. Faster answers or automated routing are not sufficient measures of value if the system creates extra work, mishandles sensitive cases, or obscures who is accountable for a decision. The interview does not describe Mercedes-Benz’s escalation rules or monitoring metrics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Governance: principles, review, and regional rules
Lehmann said Mercedes-Benz had established internal AI principles in 2019 and described collaboration among IT, compliance, and legal teams. The company also used a database to collect, evaluate, qualify, and prioritize AI ideas, with a GenAI Accelerator at Mercedes-Benz Tech Innovation. She said regional requirements, including the EU AI Act, needed to be considered while the company’s AI principles were used globally.
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Lehmann said the company was well positioned for the EU AI Act; that is her assessment in an interview, not an independent compliance audit or proof of compliance with all applicable laws. Principles are only one part of responsible deployment. In practical terms, organizations also need suitable data access controls, evaluation, auditability, monitoring, and a clear way to manage incidents. The interview does not provide operating metrics or detailed controls for the database or accelerator, so it cannot establish how quickly projects moved from idea to production or how consistently risks were assessed.
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Learning as part of the operating model
Lehmann emphasized curiosity, continuous education, sharing discoveries, and being open about mistakes. She said she used Mercedes-Benz Direct Chat, an internal generative-AI solution, to learn and share useful findings with colleagues. The interview does not identify its underlying model, hosting, security controls, retention policy, user base, or approved use cases; it should not be described as a proprietary model on the evidence available.
This culture matters because a portfolio process or tool cannot, by itself, tell teams which use cases are useful, safe, and worth maintaining. Teams need to report what failed as well as what worked, and leaders need to create room for that learning. Lehmann also connected the quality of IT with the quality of Mercedes-Benz products, placing technology execution within the company’s broader operating ambitions.
What Lehmann said about representation
In the September 2024 interview, Lehmann said three of Mercedes-Benz’s eight board members were women and women held approximately 26% of senior-management positions worldwide. These are figures she cited at that time; they should not be treated as current company statistics.
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The operating lesson for CIOs
Read as a sequence, Lehmann’s approach is: simplify the application estate, standardize where that meets the need, use the capacity created to qualify AI opportunities, deploy selectively, and preserve human judgment where it matters. That sequence is an interpretation of the interview, not a formal Mercedes-Benz framework.
- Measure beyond adoption: compare time saved with quality, rework, security, and review effort.
- Make portfolio choices explicit: distinguish systems to keep, transform, and retire, while protecting continuity.
- Match governance to risk: a use-case funnel should enable safe experimentation as well as screen it.
- Design human escalation into customer-facing automation, especially for urgent or emotionally difficult situations.
The CIO interview documents priorities and examples as of 2024, but offers limited independently verifiable evidence of outcomes. That distinction matters: the strategy is a useful model for thinking about sequencing and trade-offs, not proof that every initiative produced the intended business result.
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