The Chief AI Officer: Does Germany Need a New C-Level Role?

8 min Reading Time

In January 2024, SAP appointed Philipp Herzig as its Chief AI Officer – reporting directly to CEO Christian Klein. In July 2025, Siemens hired Vasi Philomin from AWS to scale Industrial AI. According to Wharton, 60 percent of large U.S. corporations already have a CAIO – or an equivalent function. But MIT Sloan warns: “Do You Really Need a Chief AI Officer?” The answer is far less clear-cut than job postings suggest. Germany doesn’t need another acronym in its org charts. It needs a clear answer to one question: Who is accountable?

TL;DR

The Numbers: How Many CAIOs Are There, Really?

CAIO adoption figures contradict each other – and therein lies the first insight. The Wharton/GBK AI Adoption Report 2025 (surveying 800 U.S. firms with ≥1,000 employees) reports 60 percent. IBM measures just 26 percent globally (up from 11 percent two years earlier). Gartner projected in October 2024 that 35 percent of large enterprises would appoint a CAIO by end-2025.

The Wharton study explains the gap: “In many cases, it’s an added responsibility for an existing executive – not a new headcount position.” A CTO who now oversees AI counts as a CAIO. A CDO leading an AI project counts as a CAIO. The real question isn’t how many companies have a CAIO – but how many have installed a dedicated, full-time, C-level AI leader. That number is significantly lower than 60 percent.

Comparable data for Germany is lacking. What we do know: According to Bitkom 2025, only 36 percent of German companies actively use AI. Fifty-three percent cite legal uncertainty and lack of expertise as their top barriers. For most German SMEs, a CAIO is neither structurally nor financially viable. So the question becomes: For whom is it viable?

Sources: Wharton AI Adoption Report 2025, IBM IBV CDO Study 2025, BCG AI Radar January 2025

SAP and Siemens: Two German Models

In January 2024, SAP appointed Dr. Philipp Herzig as Chief Artificial Intelligence Officer – with direct reporting lines to CEO Christian Klein. Herzig’s remit spans SAP Business AI’s entire value chain: from product development and research to customer implementation. He coordinates cross-functional AI integration across SAP’s entire portfolio. In 2025, Herzig additionally assumed the role of global CTO. CAIO and CTO in one person – this signals SAP views AI not as a side project but as core to its technology strategy.

Siemens chose a different path: In July 2025, the conglomerate recruited Vasi Philomin from AWS, where he served as VP for Generative AI and co-led Amazon Bedrock. His title at Siemens: Executive Vice President and Head of Data and Artificial Intelligence – not formally “CAIO,” but a dedicated, full-time AI leadership role at senior-executive level. He reports to Peter Koerte (CTO and CSO on Siemens’ Executive Board) and is tasked with building an industrial foundation model for Siemens customers. Recruiting from AWS sends a clear message: Siemens does not intend to develop AI internally, but to operate at the same level as hyperscalers.

Deutsche Telekom pursues a distributed approach without a dedicated CAIO: internal AI guidelines since 2018, a “KI-Fabrik” (AI Factory) strategy for Europe, and AI governance as a standing committee topic. Bosch likewise has no CAIO at board level. Both demonstrate: Skipping the CAIO title doesn’t automatically mean missing AI leadership – but it makes answering the accountability question harder.

Why the Role Can Make Sense: Three Arguments

Argument 1: The scaling gap. The McKinsey State of AI 2025 (surveying ~1,500 organizations) shows: 88 percent use AI regularly; 79 percent use generative AI. Yet only one-third have scaled AI enterprise-wide. The decisive lever is workflow redesign – only 21 percent of generative AI users have redesigned workflows. That rarely happens without a dedicated driver, because it demands collaboration across departmental boundaries. McKinsey finds CEO ownership of Gen-AI governance correlates most strongly with EBIT impact – and 30 percent of organizations assign direct accountability to the CEO – double the share from a year earlier. But CEOs lack both the time and technical expertise to steer AI operationally. A CAIO fills that gap.

Argument 2: The 70-20-10 formula. BCG (January 2025, surveying 2,400 executives) offers a crisp breakdown: Successful AI implementation is 70 percent people, processes, and culture; 20 percent data and technology; and 10 percent algorithms. Only 25 percent of companies achieve measurable ROI from AI investments – even though 75 percent rank AI among their top three priorities. The cultural transformation BCG identifies as the 70-percent lever requires a responsible leader. Per BCG (September 2025), AI Leaders report double the revenue growth and 40 percent greater cost savings versus AI Laggards.

Argument 3: Regulatory pressure. The EU AI Act enforces board-level accountability. Since February 2025, the AI Literacy obligation (Article 4) applies: All personnel working with AI systems must possess sufficient competence. From August 2026, full obligations for high-risk AI take effect – requiring demonstrable oversight, logging, risk management, and human supervision. Boards bear fiduciary duties – intentionally ignoring AI compliance risks personal liability. A CAIO delivers the clear accountability the AI Act demands – even if it doesn’t explicitly require the title.

Why the Role Can Backfire: Counterarguments

MIT Sloan Management Review published perhaps the most nuanced counterargument in August 2024. Core points: Cross-functional roles like the CAIO are notoriously difficult to navigate and generate C-suite friction with the CIO, CTO, COO, and CDO. Research shows multiple parallel tech-leadership roles erode role clarity. The “alphabet soup” problem is real: Employees often don’t understand what these titles actually do.

Then there’s the “hammer-nail problem”: A CAIO may default to seeing AI as the solution to every problem – rather than solving problems with the best available tool. And once AI becomes normalized as part of everyday work, demand for a CAIO naturally declines. MIT Sloan therefore recommends treating the CAIO not as a permanent role – but as a temporary transformation agent with a defined exit point.

Google and Microsoft provide practical proof: Both pursue an “AI-first” strategy – yet neither has appointed a CAIO. Instead, they’ve embedded AI as a core element within existing C-level mandates. That works for companies whose business is AI (tech giants, platforms). For industrial firms deploying AI as a tool, the situation differs – there, AI transformation often needs a dedicated driver, because existing leaders are already stretched thin managing cloud migration, legacy system replacement, and skills shortages.

Five Models: Which Fits Which Company?

Model 1: Dedicated CAIO (SAP Model). Full-time C-level role reporting directly to the CEO. Suitable for companies where AI is core to the product – or where AI drives business model transformation. Cost: €300,000-€700,000 total compensation in DAX firms. Risk: Silos, if boundaries with CTO and CDO aren’t sharply defined.

Model 2: Hybrid CAIO/CTO (SAP 2025). Philipp Herzig now holds both roles at SAP. Advantage: No silos between technology and AI. Risk: Overload – if both mandates demand genuine full-time commitment. Best for firms where AI and technology strategy are inherently converging.

Model 3: AI Center of Excellence (Deutsche Bank Model). A centralized team of AI specialists acting as internal service provider. Well-structured AI CoEs are 2.6× more likely to scale AI enterprise-wide, per analysis. But they often become bottlenecks – the issue lies in governance design. Ideal for large organizations with many business units wanting decentralized AI deployment.

Model 4: AI Committee at Board Level. A dedicated supervisory or executive board committee overseeing AI strategy and compliance. Establishes accountability without adding a C-level role – or salary costs. Suited to mid-sized firms aiming to meet AI Act requirements without budgeting for a CAIO.

Model 5: Distributed AI Governance (Google/Microsoft Model). No CAIO – instead, AI is embedded into all C-level mandates. Works for companies where AI is central to the business. Harder for firms where AI is still emerging as a transformative force – without a dedicated driver, the impetus to scale often stalls.

What German Boards Must Decide Now

First: Answer the accountability question. The EU AI Act doesn’t prescribe a specific role – but it does demand accountability. Who on the board is responsible for AI compliance? Who approves risk mitigation measures? Who monitors execution? If the answer is “the CTO handles it,” accountability is insufficiently clear. The Reboot demands unambiguous responsibilities – not diffused mandates.

Second: Choose the right model. For DAX conglomerates with AI as a core strategic pillar: dedicated CAIO or hybrid model. For upper-tier SMEs (500-5,000 employees): AI Committee or AI CoE. For SMEs: Assign accountability directly to the managing director – and buy in external expertise. There’s no one-size-fits-all – but there is a minimum requirement: someone must be accountable.

Third: Ensure AI competence on the supervisory board. McKinsey shows CEO ownership of AI governance correlates most strongly with EBIT impact. But the supervisory board must be able to evaluate the executive board’s AI decisions. If no one on the supervisory board understands what a foundation model is – or why bias monitoring matters – oversight devolves into formality. The AI Act’s AI Literacy obligation applies de facto to oversight bodies too.

Fourth: Think of the CAIO as a transformation role – not a permanent post. MIT Sloan is right: Once AI is embedded as standard practice, a dedicated AI chief is no longer needed. The CAIO is a transformation agent with a clear mission: integrate AI into processes, products, and culture. When that succeeds, the role can merge into CTO or COO. SAP did exactly that: Herzig is now CAIO and CTO in one. The transformation absorbed the role.

The talent challenge remains the biggest hurdle: Per McKinsey, 46 percent of companies cite missing AI skills as their top barrier to faster AI development. CAIO career paths come from two directions: the technology side (ML/AI engineering plus product leadership – as with Herzig at SAP) or the business side (strategy plus digitalization plus data analytics – often with CDO backgrounds). Top international business schools – including Chicago Booth, Cornell, and Carnegie Mellon – already offer explicit CAIO certification programs. For Germany, this means: The next generation of AI leaders will be trained abroad unless domestic institutions step up. This is a location issue – not an HR problem.

Frequently Asked Questions

Further Reading

• AI Governance at the Board Level: Between Innovation and Regulation
• Skills Shortages as a CEO Responsibility: Strategies Beyond Recruitment
• Reboot Germany: €735 Billion, Three SMEs, and the Question of Whether the Crisis Is Really That Bad

Header Image Source: Pexels / Matheus Bertelli (px:8386440)

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