Vehicle development is becoming increasingly complex.
How does AI help manage that?
Poggenburg: The complexity, especially in ADAS validation
and software testing, is enormous. AI helps us manage that
by identifying the most relevant scenarios and detecting
inconsistencies between software versions. It also supports
engineers in finding new solutions by highlighting connec-
tions we might otherwise overlook.
Bruhnke: Complexity will not go away, but we can handle it
differently. With automated interfaces and consistent data
flow, AI helps us connect all development stages. Our Inte-
grated and Open Development Platform is the backbone for
this, integrating tools and data across domains, making the
process more transparent and the results more reliable.
Wanker: Much of the complexity is caused by delays in get-
ting the right information. AI acts as a catalyst by distributing
and structuring data so that engineers have exactly what they
need at the right time. We are already using it in field analyt-
ics to detect anomalies in vehicle data, which allows us to
pinpoint specific issues rather than analyze entire fleets.
What challenges do you see today?
Poggenburg: The biggest challenge is skills. Using AI effec-
tively requires more than knowing how to operate a tool. It
also means understanding risks, data, and responsibility.
Regulations also differ widely: Europe is more rule-based, the
U.S. moves faster on technology, and China is highly pragmat-
ic. For global companies like AVL, this creates very different
conditions for data use.
Wanker: Knowledge management is essential. The expertise
in people’s minds must be captured and made usable for AI
systems. Once that happens, knowledge becomes accessible
across teams and locations. This will fundamentally change
the engineering process and how value is created, as exper-
tise can then be applied at scale.
Bruhnke: Data quality is still a major challenge, especially for
training models in safety-relevant systems. But the goal is
clear: explainable and transparent AI that strengthens trust
and supports engineers in making better decisions.
Where is AI taking us next? What will vehicle development
look like in the coming years?
Bruhnke: We are moving toward self-learning vehicles that
improve continuously through data and feedback. They will
adapt their performance to real-world conditions and commu-
nicate with other systems to enhance safety and efficiency.
Wanker: The next big step is for AI to become invisible. It
will be embedded so deeply in our tools and workflows that
engineers will no longer notice it as a separate element. It will
simply be there, like electricity – powering everything we do.
At the same time, AI will democratize engineering knowledge,
making expertise accessible to many more people.
Poggenburg: AI will connect development, production, and
service even more closely. We will see systems that learn
from every stage of their lifecycle, from simulation and testing
to operation in the field. This creates a continuous improve-
ment loop that benefits both manufacturers and drivers.
Interview with
Stefan Bruhnke
EVP Sales and Business Development Engineering
Roland Wanker
VP Advanced Simulation Technologies
Jens Poggenburg
EVP Software Products, Emission and Services
Testing Solutions
2025