AI strategy and deployment
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Identifying high-impact use cases, success criteria and feasibility
- Selecting the right AI models (generative, agentic, explainable AI) and technology
- Guidance through technology selection and implementation
- Measuring success
I am working with explainable AI in HR since 2019 — trained at IBM: pragmatic, results-oriented, and compliant with the EU AI Act for high-risk use cases.
From usage to impact
AI enriches our toolkit: automation potential and data-based insights provide the foundation for new forms of task distribution, role descriptions, and skill requirements — and decision-making on a new level.
The key: understanding the available AI models, along with their opportunities and challenges.
My offer
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Developing a results-oriented AI strategy
- Identifying and prioritizing business-centered AI use cases, workflow redesign (AI / human), selecting the right type of AI
- A pragmatic approach to iterative implementation, using existing and/or new technology
- Change management
I work with both regional and international providers of AI and related technologies.
Workshops
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Short and punchy: fundamentals, effectiveness, and application examples for the 3 types of AI (generative, agentic & explainable AI) — 3h, online or on-site
- Concrete and actionable: identifying and prioritizing AI use cases for your organization, including a roadmap for implementation — 1–2 days, on-site
The three types of AI
- Generative AI delivers fast results based on mathematical probability, requiring validation
- Agentic AI supports with autonomous decision-making and execution of tasks, based on generative AI
- Explainable AI is for data-based insight generation: traceable, trustworthy, transparent, and legally compliant in high-risk use cases
If you know the types of AI and select the right tool for your use case, you don't get burned in the execution. Cases like the Workday Bias Lawsuit or the Eightfold Lawsuit are speaking a clear language.
This understanding is the essential prerequisite for using AI effectively.
AI is a business risk
Artificial intelligence can no longer be treated as a mere efficiency tool running in the background. The cost of trust is rising.
Meinolf Sellmann, Feb 2026:
"Companies mistakenly assume AI = LLMs. Using an LLM as a decisioning engine... will get you burned. ... Know the different branches of AI and use the right tool and the right approach for the right tasks."
Screenshot shared by Nikki Pilkington. 16 Feb 2026
Explainability = Trust = Focus on results = Impact
An illustrative comparison of the difference between a chatbot based on genAI versus XAI:
For low-risk applications, a genAI bot can be very helpful. In high-risk settings, you need trustworthy, traceable statements (e.g. healthcare/finance, in the HR context, or for legal questions).