Matthias Sabel

Senior Communications Strategist (AI)
Ex-Aleph Alpha
MBA Strategy
Startup, Corporate & Consulting Experience
Thriving in decoding complexityLinkedIn

Matthias Sabel specialises in increasing brand visibility and coherence in generative AI systems, including chatbots, copilots, AI search and answer engines.Matthias is based in Munich and works as an independent Senior Communications Strategist focused on generative AI systems. Most recently, he served at Aleph Alpha, a leading European AI company, in a senior communications role during 2024 and 2025.Why is that a thing — and why does it matter for brands in generative AI systemsAs user interactions increasingly shift from classical search results to AI generated answers, the way brands are discovered is changing fundamentally. According to scientific as well as practical research at the Kellogg School of Management, McKinsey or Deloitte, generative AI is expected to contribute trillions of dollars in annual economic value, largely by reshaping how knowledge is accessed, processed and communicated.In this environment, tried principles of brand appearance through owned channels and ranking positions and more through language models that summarise, compare and contextualise information on behalf of users.What works – and what doesn’t in generative AI environmentsClassic SEO and brand communication strategies optimise for linguistic visibility: how content is written, structured and ranked for human consumption. Generative AI systems, by contrast, operate on semantic visibility: how meaning, relationships and relevance are represented, connected and reproduced by language models.The core of this work is translating brand positioning, narratives and value propositions into machine-readable concepts that operate on this additional layer. This addresses a structural gap many organisations are facing: investment in AI systems is accelerating, but brand and communication structures are still largely designed for human interpretation alone.Why this is not just theoryDeloitte and Accenture both highlight that AI-driven interfaces increasingly mediate customer interactions, from research and evaluation to recommendation and explanation. In such systems, large language models do not retrieve brand statements directly. They generate answers based on semantic relationships, retrieval mechanisms and probabilistic associations.Machine-readable brand structures allow language models to recognise, contextualise and reproduce brands more consistently and closer to their intended positioning. Rather than relying on chance or prompt-level optimisation, this approach focuses on how brands are represented within semantic retrieval, embeddings and model-driven response generation.The objective is not to replace existing brand, PR or SEO logic, but to complement it at the level where AI systems construct relevance and meaning. The goal is to increase the probability that brands show up on brand in AI-generated answers across different systems and interfaces, as these systems increasingly become a primary layer between organisations and their audiences.

Matthias' background and experience
Matthias operates at the intersection of deep tech, brand strategy and communication. His work has focused on large language models, retrieval augmented generation and semantic search, with the aim of creating a shared language between abstract AI systems and real-world brand communication.Most recently, he served as Senior Communications Manager at Aleph Alpha, one of Europe’s leading and best-funded AI companies, where he translated advanced AI research, systems and product strategy into narratives usable by executives, partners and the public, and worked on integrating classical brand, PR and SEO logic with AI driven environments.Brand visibility in generative AI systemsIn 2028, AI search traffic is expected to go beyond As interactions shift from search results to AI-generated answers, brands increasingly appear through language models rather than owned channels. In this environment, visibility is no longer only a question of ranking, but of how a brand is understood, contextualised and reproduced by AI systems.What does machine-readable brand design mean?Machine-readable brand design describes the structuring of brand attributes, narratives and value propositions in a way that can be interpreted by large language models and retrieval systems.How is this different from SEO?While SEO optimizes for ranking in search results, AI-driven systems generate answers based on semantic relationships. Brand visibility therefore depends on how models understand and retrieve brand-related concepts.