Persona Buying-Center Mapper
Map the buying center for a named persona at a target account — departments and named contacts and the right opening angle per role.
Persona Buying-Center Mapper
Sample output
Buying Center Map: AI Infrastructure Decision-Makers @ Siemens AG
1. Account Context
Siemens AG (siemens.com) is a Forbes 2000 #62 industrial technology conglomerate headquartered in Munich, Germany, with ~316,000 employees globally and ~€88.7B in revenue (HG, May 2026). IT spend is modeled at ~$4.0B annually, making Siemens one of the largest IT-budget organizations in EMEA. The company operates across industrial automation (Digital Industries), smart infrastructure, mobility, and healthcare technology — each a structurally distinct AI buyer. The ML workload is concentrated in Engineering (see FAI below), with a globally distributed signal footprint spanning India, Germany, US, Hungary, Mexico, and Portugal. Siemens runs both Azure ML and AWS ML simultaneously, signalling a multi-cloud ML posture rather than a single-vendor dependency, which is a relevant angle for any platform, tooling, or MLOps conversation.
2. Departmental Layer (FAI)
FAI was run against Azure Machine Learning (product ID 16146), AWS Machine Learning (product ID 16820), and DataRobot (product ID 16150). Results below. (HG, May 2026)
| Product | Department | Usage Share | Signal Strength | Decision-Maker Flag | Influencer Flag | Key Roles Observed | Signal Geographies |
|---|---|---|---|---|---|---|---|
| Azure Machine Learning | Engineering | 91.3% | 441 (high) | No | ✅ Yes | Data Scientist (75.5% role share), Software Architect, Software Engineer, DevOps Engineer, Data Engineer | India, US, Germany, Hungary, Mexico, Portugal |
| Azure Machine Learning | Unknown / Unclassified | 5.4% | 26 | ✅ Yes | No | — | Germany, Spain |
| Azure Machine Learning | Finance | 2.9% | 14 | No | No | Finance Manager | Hungary, Germany, France, Portugal, Denmark |
| Azure Machine Learning | IT | 0.4% | 2 | No | ✅ Yes (Security Engineer) | Security Engineer | India |
| AWS Machine Learning | Engineering | 98.3% | 173 (high) | No | ✅ Yes | Data Scientist (72.3% role share), Software Engineer, Software Architect, DevOps Engineer | India, US, Portugal, Germany, UK |
| AWS Machine Learning | IT | 1.1% | 2 | No | ✅ Yes (Security) | Security Engineer | India |
| DataRobot | IT | 100% | 2 (low) | No | No | IT Analyst, Other | US |
FAI reading:
- Azure ML and AWS ML are overwhelmingly Engineering-owned workloads (91–98%), with Data Scientists carrying the dominant role-usage share (~72–75%). This is the operational buyer layer. Budget authority for a 316,000-person org typically sits one level above: VP/Head of Data & AI and divisional CIOs.
- Finance at 2.9% on Azure ML is a reach signal — Finance Managers in EU markets (Hungary, Germany, France) have Azure ML touchpoints, likely for financial modeling. This is a secondary conversation, not the primary entry.
- DataRobot at 100% IT is a weak signal (signal strength: 2). Treat as an IT-managed pilot, not a scaled deployment. The conversation here is procurement/evaluation, not operational ownership.
- No Sales or Marketing department signal on any ML product — this is purely a technical buying center.
3. Named Contacts — Grouped by Persona Shape
🔧 Technical Decision-Makers (Run the Workload)
Operational owners of the ML platform layer. FAI: Engineering 91–98% on Azure ML / AWS ML. These contacts own the toolchain, evaluate new platforms, and control practitioner access. They are the primary "does this work?" gate.
Christoph Malassa
- Title: Head of Data & AI Platforms
- Location: Mannheim, Baden-Württemberg, Germany
- Tenure: In role since June 2024; 15 years at Siemens total (former Head of Analytics & Intelligence Solutions 2019–2024)
- LinkedIn: linkedin.com/in/christoph-malassa-a4462188
- Why load-bearing: Direct owner of the Data & AI platform layer — the exact function that provisions, governs, and scales Azure ML and AWS ML tooling across Engineering. His prior role as Head of Analytics & Intelligence Solutions means he has a strong point of view on what the current stack can and cannot do.
- Opening angle: "Christoph, your team's Azure ML footprint at Siemens spans India, Germany, Hungary, Mexico, and Portugal — multi-region ML platform governance at that scale is where we're seeing the highest MLOps friction. [Two or three concrete observations about cross-region model governance or data residency pain]. Worth a 20-minute compare against what you're running today?"
Marco Vernaza
- Title: Head of AI Platforms
- Location: Munich, Bavaria, Germany
- Tenure: In role since December 2025 (recently promoted from Senior AI Solutions Consultant)
- LinkedIn: linkedin.com/in/marco-vernaza-02656a95
- Why load-bearing: Directly titled "Head of AI Platforms" — this is the person who owns platform selection and configuration for AI workloads. His recent promotion (Dec 2025) means he is likely in an evaluation phase for the current platform state, making this a well-timed entry point.
- Opening angle: "Marco, you stepped into the Head of AI Platforms role at Siemens in December — platform owners in their first six months typically face a short window to re-baseline the stack before roadmaps lock. [Specific capability gap or integration angle relevant to Azure ML / AWS ML]. Happy to share what we're seeing in comparable industrial orgs."
Tobias Stangl
- Title: VP, Head of Data Analytics and AI
- Location: Munich, Bavaria, Germany
- Tenure: In role since June 2022 (VP-level since 2022, former Siemens Management Consulting)
- LinkedIn: linkedin.com/in/tobiasstangl
- Why load-bearing: VP-level owner of the Data Analytics & AI function — sits above Malassa and Vernaza in the operational hierarchy. He is the primary budget stakeholder for the Data & AI platform layer and the person who would approve a platform consolidation, expansion, or replacement. Four years in role means he has sponsored the current Azure ML + AWS ML dual-cloud architecture.
- Opening angle: "Tobias, Siemens is running both Azure ML and AWS ML at significant scale — a dual-cloud ML posture at 316,000 employees creates real governance and cost optimization decisions. [Specific observation on unified ML lifecycle management or model registry consolidation]. Would a 30-minute discussion on how other industrial enterprises are handling this be useful?"
🏢 Business Stakeholders (Sign the Renewal / Set Strategic Direction)
Budget owners and strategic sponsors. FAI confirms no business-unit ownership of ML workloads — these are IT/Engineering-governed tools. Budget authority for platform-level decisions flows through divisional CIOs and the Group CIO.
Hanna Hennig
- Title: CIO, Siemens AG (Group-level)
- Location: Munich, Bavaria, Germany
- Tenure: CIO since January 2020; prior CIO at OSRAM; VP IT at Telefónica O2 Europe
- LinkedIn: linkedin.com/in/hanna-hennig
- Why load-bearing: Group CIO with full infrastructure mandate. At Siemens' scale, she sets the enterprise technology architecture guardrails within which Azure ML and AWS ML operate. Any platform-level decision with cross-divisional implications or significant spend would require her visibility or sign-off. She is also currently a Supervisory Board member at Eneco (since April 2025) — a signal of broader strategic influence.
- Opening angle: "Hanna, Siemens is running Azure ML and AWS ML across a globally distributed Engineering footprint — the group-level question is usually about standardization vs. divisional autonomy at that scale. [One observation grounded in how peer industrial enterprises (ABB, Honeywell, GE) are handling group-vs-division ML platform governance]. We work with several Forbes 2000 industrial CIOs on exactly this tension — happy to share the pattern."
Frederik Janssen
- Title: CIO, Siemens Digital Industries
- Location: Munich, Bavaria, Germany
- Tenure: CIO Digital Industries since January 2023; former VP IT Strategy, Architecture & Portfolio (2022); VP IT Strategy & Governance (2019–2022); 26 years at Siemens total
- LinkedIn: linkedin.com/in/frederik-janssen
- Why load-bearing: Divisional CIO for Siemens Digital Industries — the highest-revenue, most AI-intensive division (industrial automation, PLM, MindSphere IoT). FAI signals show Engineering in Digital Industries geography (Germany) among the Azure ML touchpoints. He is the most likely divisional budget owner for ML platform investments tied to industrial AI use cases. His prior VP IT Strategy & Architecture role means he is architecturally fluent — opening angles should be technical, not generic.
- Opening angle: "Frederik, Digital Industries is the natural epicenter of Siemens' industrial AI deployment — the Engineering footprint running Azure ML in Germany maps to exactly the kind of embedded ML in automation and PLM pipelines your division owns. [Specific observation on edge-inference or digital twin model lifecycle]. We're seeing peers run into [specific bottleneck] at this stage of maturity — worth 20 minutes to compare notes?"
🔍 AI Strategy / Center of Excellence (Influencers and Internal Champions)
Not budget owners, but internal champions who shape platform requirements and evaluation criteria. Useful as warm entry points before reaching Stangl, Hennig, or Janssen.
Katalin Westhoff
- Title: Head of AI Center of Excellence
- Location: Munich, Bavaria, Germany
- LinkedIn: linkedin.com/in/katalin-westhoff-9788b3b7
- Why relevant: CoE heads own evaluation frameworks, benchmark criteria, and internal AI governance standards — they are the people who produce the shortlist that reaches Stangl or Janssen. Engaging here seeds requirements before the formal evaluation begins.
Stephan Weiss
- Title: Head of Data and AI Scaling
- Location: Bavaria, Germany
- LinkedIn: linkedin.com/in/weiss-stephan
- Why relevant: "Scaling" in the title is a direct proxy for MLOps and productionization concerns — the exact friction point where most industrial ML platforms hit their limits. This is a practitioner-tier influencer with a remit that overlaps precisely with platform infrastructure pain.
Hesham Ghoneim
- Title: Head of Strategy — Data and AI
- Location: New York, New York, United States
- LinkedIn: linkedin.com/in/hesham-ghoneim-93b7624
- Why relevant: US-based strategy head for Data & AI — entry point for North American conversations and a potential co-champion for any strategic platform narrative that needs both EMEA and Americas sponsorship.
4. Opening-Angle Summary by Group
| Contact | Title | Group | One-Line Opening Angle |
|---|---|---|---|
| Christoph Malassa | Head of Data & AI Platforms | Technical DM | Multi-region Azure ML governance across 6+ countries — MLOps friction at scale is the angle |
| Marco Vernaza | Head of AI Platforms | Technical DM | New in role (Dec 2025) — re-baselining window is open; platform evaluation is live |
| Tobias Stangl | VP, Head of Data Analytics and AI | Technical DM (budget) | Dual-cloud Azure ML + AWS ML posture at 316K employees — consolidation / governance ROI |
| Hanna Hennig | CIO, Siemens AG | Budget Owner | Group-level standardization vs. divisional autonomy — peer CIO benchmark angle |
| Frederik Janssen | CIO, Digital Industries | Budget Owner | Industrial AI in PLM / automation — digital twin + edge inference ML lifecycle |
| Katalin Westhoff | Head of AI CoE | Influencer | Evaluation framework influence — engage before the formal shortlist forms |
| Stephan Weiss | Head of Data and AI Scaling | Influencer | MLOps / productionization friction — practitioner-tier platform pain |
| Hesham Ghoneim | Head of Strategy, Data and AI | Influencer | US-based co-champion for strategic platform narratives with Americas scope |
5. Quality Notes & Citations
- FAI: Azure ML — Engineering 91.3% / Unknown 5.4% / Finance 2.9% / IT 0.4% (HG, May 2026)
- FAI: AWS ML — Engineering 98.3% / IT 1.1% (HG, May 2026)
- FAI: DataRobot — IT 100%, signal strength 2 (low confidence, treat as pilot) (HG, May 2026)
- Contact search: 56 AI/Data candidates returned; 356 CIO/CTO/engineering leadership candidates returned (Apollo, May 2026). Top 5 enriched at high match confidence.
- No email addresses are included in this deliverable per stakeholder-card discipline — enrich with
contact_enrich+revealEmail: trueif direct outreach is required. - No phone numbers. No LinkedIn URLs were constructed — all URLs sourced directly from Apollo contact_search results.
- Director minimum seniority filter applied. Westhoff, Weiss, and Ghoneim are surfaced as influencer context, not primary targets.
Overview
Map the buying center for a named persona at a target account. Marketing Ops feeds the target plus the persona (e.g., 'AI infrastructure decision-makers', 'CFO + finance leadership for budget approval'); the workflow uses HG FAI plus contact search to identify the departments and named contacts that own the relevant systems, plus their seniority and the right opening angle per persona role.
Use cases
Buying-center maps that don't miss the operational owner
Demand gen or ABM feeds the target account plus the persona. The workflow uses HG FAI for the departmental layer (which department owns the system, who's the decision-maker, what's the contact density) plus contact_search for the named-individual layer (filtered by the seniority floor). The output groups the buying center into the persona's natural shape and proposes opening angles per role.
View workflow prompt
# Persona Buying-Center Mapper
## Parameters
- `{{target_domain}}` *(required)* — The target account domain. Example: `siemens.com`
- `{{persona}}` *(required)* — The persona to map. Example: `AI infrastructure decision-makers`
- `{{category}}` *(optional)* — The system or category the buying center owns. Example: `Machine Learning Platforms`
- `{{seniority_floor}}` *(optional)* — Minimum seniority to surface. Example: `director`
## Purpose
Map the buying center for the {{persona}} persona at {{target_domain}}. When {{category}} is provided, anchor the FAI search on that category. Filter by {{seniority_floor}} minimum. Use FAI for the departmental layer (who owns what) and `contact_search` plus `contact_enrich` for the named-individual layer.
## Process
1. **Account context.** `company_firmographic` on {{target_domain}}: industry, revenue band, parent-subsidiary state. Surface relevant org context that shapes the buying center.
2. **Departmental layer.** `company_fai` scoped to {{category}} when present: which departments own the relevant systems, decision-maker vs influencer flags, role names and usage shares, geographic split.
3. **Named-individual layer.** Per the FAI-vs-contact-search discipline: `contact_search` for individuals whose titles map to the persona-relevant roles surfaced in step 2, filtered by {{seniority_floor}}. For the 3 to 5 most-load-bearing contacts, run `contact_enrich` to capture title, location, and LinkedIn (never fabricated emails).
4. **Persona-shaped synthesis.** Group the buying center into the persona's natural shape: technical decision-makers, business stakeholders, procurement stakeholders, end-users. Per group, list 1 to 3 named contacts AND the opening angle that fits their role (per the opening-line discipline). Never fabricate emails.
5. **Stakeholder cards.** Per the stakeholder-card-discipline skill: name + title + location + engagement strategy. Sparing use of LinkedIn URLs (only when verified).
## Output Format
- Account context (3 to 5 sentence summary)
- Departmental layer (FAI table: department, system owned, decision-maker flag, contact density, geo)
- Named contacts grouped by persona-shape (technical, business, procurement, end-users)
- Opening-angle suggestions per group (one-line per contact)
## Quality Checklist
- Departmental layer uses FAI; named-individual layer uses contact_search. Never conflate them.
- {{seniority_floor}} is respected; lower-seniority contacts surfaced only as context
- No fabricated emails or phone numbers (per HG citation discipline)
- Opening angles are persona-shaped, not generic