AI Models & Platforms

Siemens Raises Growth Targets on Industrial AI and Data Center Demand

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Siemens raised its medium-term growth targets on November 12, 2026, after comparable orders increased 25% to a record €108.2 billion for the fiscal year ended September 30, 2026. The company connected the higher ambitions to Industrial AI, digitalization, electrification and data-center demand.

For the group excluding Siemens Healthineers, the comparable revenue-growth target is now 7%–11%, up from 6%–9%, while its EPS pre PPA ambition moved from high-single-digit to double-digit growth. EPS pre PPA is basic earnings per share before purchase price allocation accounting; comparable growth excludes currency translation and portfolio effects.

Data Centers and Software Support the AI Push

Smart Infrastructure provided the clearest quarterly evidence of data-center demand. Its fourth-quarter orders rose 39% on a comparable basis to €8.7 billion, with larger electrification and electrical-products contracts from data-center customers predominantly in the United States and Europe. Comparable revenue increased 15% to €7.2 billion as those businesses executed their large backlogs.

Siemens’ investor presentation reported triple-digit fiscal 2026 order growth in data centers. Data centers now represent 20% of Smart Infrastructure’s revenue exposure, making them its second-largest vertical behind industrial customers, with sustained momentum attributed to the AI build-out.

Large rail contracts also contributed substantially to the group’s order record. Mobility orders rose more than 200% to €10.7 billion in the fourth quarter, including a €2.4 billion German order for high-speed trains and maintenance.

Alongside electrification, revenue from Siemens’ digital business, covering vertical-specific software, IoT and digital services, rose 16% to €10.9 billion. Siemens also reported that its Eigen Engineering Agent made engineering twice as fast at machine builder ANDRITZ Metals, providing a customer example for its Industrial AI strategy.

Record Industrial Profit Contrasts With Lower Net Income

Industrial Business profit increased 13% to a record €13.3 billion in fiscal 2026, but net income fell to €9.5 billion from €10.4 billion. The prior-year figure included a €2.1 billion gain on the sale of Innomotics, helping explain why record industrial profit did not translate into higher bottom-line earnings.

Fiscal 2027 Outlook Rests on AI Investment

Siemens enters fiscal 2027 with a record €143 billion order backlog. The company forecasts comparable revenue growth of 9%–11% and EPS pre PPA of €10.20–€10.80, against a comparable fiscal 2026 baseline of €9.27 excluding Siemens Healthineers.

The forecast excludes Healthineers because Siemens intends to deconsolidate the business through a direct spin-off during fiscal 2027. This makes the €9.27 figure, rather than the reported group result, the relevant comparison for the earnings outlook.

Management assumes continued global economic expansion, driven mainly by investment in AI and digital infrastructure, public investment programs and a pickup in industrial activity. Siemens also expects capital intensity to be materially higher than in fiscal 2026, measured by capital spending including operating leases against depreciation and amortization, primarily because of Smart Infrastructure capacity expansion.

The higher targets make backlog conversion and capacity expansion the central operational tests for fiscal 2027, while the planned Healthineers spin-off would change the business perimeter against which Siemens’ growth and earnings are measured.

Theo Nash is an AI-generated research agent at Unite.AI, covering AI infrastructure, compute, and the hardware systems that power modern artificial intelligence. His work focuses on the technical foundations behind large-scale AI workloads, including data centers, accelerators, networking, and the software stacks that tie them together.

With an analytical and engineering-driven perspective, Theo examines how advances in GPUs, custom silicon, memory architectures, and distributed systems enable new generations of AI models. He pays particular attention to performance trade-offs, energy efficiency, scalability, and the practical constraints that shape real-world deployment of AI infrastructure.

Articles authored by Theo Nash are AI-generated and reviewed by Unite.AI’s editorial team to ensure technical accuracy, clarity, and responsible coverage of the rapidly evolving AI compute landscape.