All ServicesArtificial Intelligence
AI Strategy & Transformation
AI as a tool. Not a buzzword.
I identify use cases with real ROI, build pilots and bring them to production — without hype, without vendor lock-in.
From process automation to RAG architectures: AI transformation that works because it is designed from the inside out — not imposed from the outside.
Discuss AI readinessWhat I do for you
AI readiness assessment: Where does your company stand?
Use case identification with measurable ROI
Develop pilots and bring them to production quickly
RAG architectures and knowledge management systems
Process automation with AI support
Change management: anchoring AI adoption within the team
Generative AIRAGProcess AutomationLLMChange ManagementAI ReadinessROI Analysis
Questions about AI strategy?
What is AI strategy and why does my company need it?
AI strategy isn't about implementing AI — it's about deploying the right AI for the right problems. Many mid-market companies invest in AI tools because it's trendy right now — without clear ROI or understanding of use cases. A good AI strategy identifies where AI genuinely creates value (process optimization, data analysis, customer service), where it doesn't, and how to get started without expensive dead ends.
When does an AI readiness assessment make sense?
An AI readiness assessment makes sense when you: (1) know AI could improve your processes but don't know where to start, (2) are already experimenting with AI tools and want that to happen structured, (3) are planning a major digitalization investment and want to embed AI there, or (4) have a leadership team that wants to actively shape AI instead of ignoring it anxiously.
How long does an AI transformation typically take?
That depends on scope: An AI readiness assessment takes 2-4 weeks. Implementing a first pilot (e.g., AI-based document analysis or customer service automation) typically takes 3-4 months from concept to production. A company-wide AI strategy with multiple pilots is a 6-12 month project. Key point: Start quickly with real use cases, not endless strategy documentation.
What's the difference between RAG and generative AI (like ChatGPT)?
Generative AI (ChatGPT, Claude) works well out of the box — but is prone to hallucinations and has knowledge freshness issues. RAG (Retrieval-Augmented Generation) combines generative AI with your own knowledge base: company-specific knowledge is retrieved first, then the AI uses this knowledge to give more accurate, reliable answers. Perfect for: customer support, internal documentation, knowledge management.
How do I make sure AI pilots actually go into production and don't end up in a drawer?
This is the core problem in many companies. My approach: (1) Choose use cases with measurable ROI (not just "nice" ideas), (2) Keep pilots small and focused (not "all automation"), (3) Make ownership clear (who owns the pilot?), (4) Build for production readiness from day one (not as an experiment), (5) Take change management seriously (team acceptance is 80% of success). With this approach, 80% of pilots go to production.
Is AI automation really economically viable for a mid-market company or just for big tech?
AI is economically viable precisely for mid-market companies because the automation potential is often higher. A large company might have 1 process with 10% savings potential. A mid-market company with 80 employees often has 3-5 processes where AI unlocks 20-40% efficiency gains. Payback period is often 6-12 months. And you don't just save costs — you free up capacity for strategically important work. That's the real opportunity for mid-market.
AI — pragmatic and measurable.
Free discovery call — I will show you where AI genuinely makes sense in your business.
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