Siemens CTO: AI is reshaping product teams - India moves from delivery to value creation
Siemens is rebalancing its workforce toward AI and data-heavy roles, and India sits at the center of that shift. With 38,000 employees in the country and about 10,000 software and AI experts across its innovation centers in Bengaluru and Pune, the company expects smaller, sharper teams building the applications layer for new products.
Dr. Peter Koerte, Chief Technology Officer and Chief Strategy Officer at Siemens AG, said the goal is clear: build fewer, higher-caliber teams that create direct product value rather than just meeting requirements. "Copilots and AI tools are terrific for coding," he noted. "Architecturally speaking, smaller teams with higher caliber in terms of knowledge - that's a trend we're seeing."
What's changing inside product teams
Siemens is hiring for skills that move AI from theory to deployment. Data scientists and ML engineers are critical, but they're not enough on their own. UX designers are now key to deciding where and how AI is consumed, especially in industrial environments where phones and laptops aren't always an option.
- Roles in demand: data science, ML engineering, MLOps, data engineering, and AI-focused UX.
- Domain experts from manufacturing, energy, and infrastructure to guide real-world use cases.
- Edge/industrial HMI skills to bring AI onto shop floors safely and intuitively.
Smaller teams, bigger responsibility
Koerte expects India's opportunity to be strongest at the applications layer. Teams will get leaner, but closer to product decisions. Throwing requirements "over the fence" won't work; squads must own architecture, integration, and outcomes end to end.
- Typical squad (6-10 people): product lead, software architect, ML lead, data engineer, MLOps, UX/HMI, and domain SME.
- Use AI copilots for code, test generation, documentation, and refactoring; keep humans on architecture, security, and edge cases.
- Replace long handoffs with short discovery→prototype→pilot loops tied to field trials.
Designing industrial AI interfaces
Consumer-style AI interfaces don't translate cleanly to the shop floor. Workers need hands-busy, eyes-busy interactions that are safe and fast. Siemens is exploring ways operators can interact with AI without a phone or PC.
- Voice UIs tuned for high-noise environments, with confirm/cancel flows that prevent errors.
- Wearables or AR overlays for checklists, alerts, and guided fixes.
- Machine panels that surface AI suggestions in context (not as separate dashboards).
India's edge: adoption and policy tailwinds
Koerte called physical and industrial AI a massive opportunity, adding that domestic AI adoption is already high, with the majority of enterprises using AI in some form. India is Siemens' fourth-largest market and a growth driver, with demand boosted by initiatives like the IndiaAI Mission and the push for semiconductors and data centers.
- Policy momentum: IndiaAI Mission
- Industrial focus: Siemens on Industrial AI
90-day playbook for product leaders
- Weeks 1-2: Map value streams. Identify tasks suited for copilots and ML (prediction, anomaly detection, optimization, assistive UIs).
- Weeks 3-4: Stand up a lean AI tooling stack (copilot, prompt policy, code scanning, feature flags). Define "human-in-the-loop" points.
- Weeks 5-6: Restructure into small squads. Assign an AI-savvy architect. Establish model/data ownership and SLAs.
- Weeks 7-8: Ship a narrow pilot on the applications layer that touches real users (e.g., yield optimization, downtime alerts, or guided maintenance).
- Weeks 9-12: Instrument everything. Compare AI-assisted throughput, defect rates, MTTR, and operator task time vs. baseline.
Metrics that matter
- Lead time from idea to field pilot
- % of code/test/docs generated with AI assistance
- Model reliability: uptime, drift alerts, false-positive/negative rates
- Operator adoption on the shop floor and time-to-task-completion
- Quality and safety outcomes tied to AI-assisted decisions
- Unit economics: cost per feature and cost per insight down quarter over quarter
Risks to manage early
- Data quality and access: define golden datasets, lineage, and retention upfront.
- Change fatigue: train operators and supervisors; co-design interfaces with them.
- Security and IP: keep sensitive code and data under enterprise controls; review copilot prompts.
- Vendor lock-in: abstract models behind internal APIs; keep portability in mind.
Koerte expects "a lot of applications coming from India for the world." For product teams, the mandate is simple: build smaller, smarter squads that ship AI where work actually happens - on machines, panels, and shop floors - and tie every release to measurable outcomes.
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