Route optimization with AI – Leadership Performance

01 Starting point
A chimney sweep company works with its own software for job and route planning. The goal was to use AI to plan employees’ routes more efficiently. The challenge: meaningful optimization requires processing real address and job data.
02 Strategic tension
InnovationGovernance
The leadership question
How can we realize the benefit while ensuring data protection and governance?
Powerful route optimization needs enough concrete data. At the same time, sensitive customer and address data must not be transferred to external AI services without control.
03 Intervention
1. Workshop
2. Problem analysis
3. Search for solutions
4. Vendor tests
5. Pilot
In a structured workshop, the problem was first defined precisely and the specific process analyzed. Solution options were then developed, technical and data protection requirements defined, and various vendors and models tested.
04 Decision
Don’t scale right away. Validate under real conditions first.
Instead of an immediate full rollout, a clearly scoped pilot project was launched with a solution that met the defined data protection and governance requirements. This made it possible to validate benefit, technical integration and data protection requirements together under real conditions.
05 Result
Rollout to all employees.
The pilot confirmed that the solution was feasible. The AI-supported route optimization was then rolled out to all employees and integrated into the existing work process.

What this example shows

AI value creation does not come from choosing a tool alone. What matters is solving benefit, technical feasibility and governance together.

The case in the KI-Strategie-Loop®

Relevant leadership fields

Not every organization starts in the same place. Find out where your organization stands today – or talk through your specific situation directly.