Generative AI systems such as ChatGPT, Perplexity or Google AI Overviews are changing how users find information and choose providers – classic SEO alone no longer answers that.

• Generative engine optimization (GEO): visibility in AI answers, not just in search results
• Assessing how robust AI visibility metrics are (mentions, citations, prompt rankings)
• What is left of the contact with the company after an AI answer
• Content strategy for being findable, understandable and trustworthy in AI systems

AI systems increasingly take over parts of the purchase decision themselves – from product comparisons to the first autonomous agent purchases. The relevant question shifts from “How do we rank?” to “How does an AI system compare us, recommend us or use us directly for the transaction?”

• How AI systems recommend in product comparisons and purchase decisions
• Agentic commerce: machine-readable product data, prices and availability for AI agents
• Effects on marketplace, pricing and distribution strategies
• New transaction standards for autonomous agents

AI agents increasingly take over not just information gathering but executable work steps of their own. What matters is which tasks can really be automated sensibly – and where human involvement remains structurally necessary.

• Assessment: processes suited to AI vs. only partly automatable ones
• Building and steering agent workflows, internally and externally
• Interfaces for agents (APIs, structured data) instead of human-only interfaces
• Governance and failure modes of autonomous systems

Not every problem needs an AI system – but some only become economically solvable with one. This field starts with the concrete question, not with the technology.

• Feasibility and value analysis: where AI really pays off and where it is hype
• Prototyping and piloting concrete use cases
• Data quality and availability as a prerequisite
• Decision support rather than premature full automation