How do you steer IT, AI, and automation across a group with 27 subsidiaries? Rüdiger Strelow (DVV) and Dennis Schiedat (Detecon) discuss an in-house AI platform, more than 100 automated processes and the biggest hurdle: data quality. Their verdict: technology alone is not enough. What counts is good data, control over their own infrastructure and people who are engaged, trained, and empowered.
DVV and Detecon in Dialogue: Good data, Good AI
In-house AI, 100+ automated processes and one lesson: data and people make the difference.
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More InformationIT portfolio management at DVV.
How do you manage an IT portfolio in a group with 27 subsidiaries?
With clear governance and a central IT function closely aligned with the holding and its subsidiaries. The group strategy is broken down into every project. That way, municipal utilities, grid operators and transport companies all build on the same infrastructure and architecture.
What is the advantage of running your own infrastructure?
Speed, synergies, and control. DVV first checks whether an application can run in its own data center. Only then does it turn to cloud providers. Others have to commission many service providers separately. At DVV, almost everything comes from a single source.
Why is DVV building its own AI platform?
As a critical infrastructure (KRITIS) operator, it must meet data protection rules and the requirements of the German Federal Office for Information Security (BSI). Its own AI runs on its own servers in its own data center. Employees no longer have to ask whether they are allowed to enter certain data. Netze Duisburg already uses the platform, and other subsidiaries will follow step by step.
How do you scale automation across a group?
With lighthouse projects and measurable benefits. In the first project, each subsidiary was asked to name one or two processes. In the end, there were 17. Today, there are more than 100. One example: 15,000 work orders a year for field staff now run digitally instead of on paper. The key is to optimize every process before automating it.
What holds back automation and AI the most?
Data quality. The causes are heterogeneous system landscapes and a lack of data maintenance. With AI, this matters even more, because AI prepares decisions or makes them itself. Data maintenance is hygiene, “like brushing your teeth.” Employees need to see data as an asset and master the new tools.
Which course must municipal companies set for the next ten years?
Rüdiger Strelow names three. First, agile portfolio management instead of rigid five-year plans. Second, full control over critical infrastructure. Third, data literacy, with clear data owners alongside process owners. Dennis Schiedat adds: companies must build skills in AI, machine learning and energy industry data strategically.
Bad data, bad AI.
“Bad data is the basis for bad automation and bad AI. Good data is the foundation for everything we want to achieve with AI and automation.”
Rüdiger Strelow
Head of IT Portfolio Management, DVV
“In future, companies will no longer ask which processes to automate, but deliberately which ones not to automate.”
Dennis Schiedat
Energy Expert at Detecon
