You bought the AI tools. Why is your company still working the same way?
Individual productivity is up. Business results are not. I help leadership teams change how the organization works so AI investment actually pays off.
We discuss what you funded, what you expected to improve, and what the company experiences instead.
Software founder and operator. Master's focus in machine learning and software engineering. Built predictive-agent prototypes in Silicon Valley as early as 2005.
Organizations Matthias has worked with in consulting, training, or speaking
- 39%
- reported any enterprise-level EBIT impact attributable to AI
What the market shows
AI use is widespread. Enterprise-level financial impact is not.
McKinsey's 2025 State of AI found that only 39% of respondents reported any enterprise-level EBIT impact attributable to AI. Workflow redesign was the organizational factor most associated with stronger impact.
01
AI without results
People are using AI. The company is not faster.
These are the situations leaders describe after a large rollout.
What you bought — and what changed
Start with delivery, revenue, cost, risk, and customer results rather than login or prompt counts.
Individual tasks move faster. End-to-end delivery does not.
Copilot for everyone
There is plenty to show. Revenue, cost, risk, and customer results have barely changed.
Pilots and demos
The work still waits for approvals, handoffs, and decisions from other teams.
More output
02
Why this keeps happening
Your AI tools changed. Your organization did not.
Most companies add AI without changing who decides, who owns the result, or how work moves between departments. One task becomes faster, but the feature or customer request still waits in the same places.
Faster individual tasks
The same approvals and handoffs
The company is not faster
- 01
-
The task is faster. The customer still waits.
Writing, coding, or analysis may take less time while approvals and handoffs still take days or weeks.
- 02
-
Each department reports progress. No one owns the whole result.
The gain disappears when responsibility stops at the boundary between teams, budgets, or managers.
03
What happens in the diagnostic
Follow one expected result from the AI tool to the customer or the P&L
We choose one result leadership expected and follow the work until we find where it still waits, escalates, or loses ownership.
-
1
Define the result that should have moved
Choose revenue, cost, risk, customer experience, or delivery time. Do not start with adoption or prompt counts.
-
2
Trace where the gain disappears
Look at the approvals, handoffs, managers, budgets, and other teams between the faster task and the result.
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3
Decide what leadership should change first
Choose one decision, responsibility, approval, or measure to change and see whether the result improves.
First step
Executive diagnostic
- Who
- The leader responsible for the result, plus one person who knows the daily work if useful
- Format
- A 90-minute conversation after a short email describing the investment, expected result, and what has changed
- You bring
- What you funded, what leadership expected, and what has or has not changed
- You leave with
- A short written summary of the likely cause and what leadership should change first
- Fixed price
- €1,750 net
- What happens next
- Use the summary with your own team, or ask Matthias to help with the next step
04
What this is not
This diagnostic does not cover the following work.
- AI tooling, LLM architecture, or model selection consulting
- Employee prompt-training workshops
- Another glossy AI transformation deck with no organizational follow-through
05
Proven in practice
Why clients bring Matthias into difficult situations
He works with leadership, product teams, engineers, and the way responsibility is divided between them.
Broad experience
- Built and operated production SaaS used by paying customers for almost 15 years
- Master's focus in machine learning and software engineering; built predictive-agent prototypes in Silicon Valley as early as 2005
- Led organizational change across leadership, product, and engineering in startups and global organizations
Organizations Matthias has worked with in consulting, training, or speaking
Siemens, Volkswagen, Ford, ESA, Commerzbank, Lufthansa
What people say about working with Matthias
“His ability to distill complex ideas into simple, powerful insights is a gift. But what struck me even more was his attitude: curious, generous, and deeply human.”
“Your point of view is not ordinary, is not cleaned up, is not a book point of view. When I hear true stories from someone with experience, it’s something very different.”
What did leadership expect AI to improve?
Tell me what you bought, what you already tried, and what has barely changed.
06
Request an executive diagnostic
Tell me what you funded, what result should have moved, and what still has not changed.
Matthias reads every request and replies within two business days. He will tell you directly whether he can help.
Personal reply
Within two business days
No sales handoff
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