We believe AI is a great tool with a lot of yet-untapped potential- but only if applied with purpose. The real challenge lies in deciding what to build, how to train our teams, and which features will truly benefit our customers.
How can we steer Infodation into a future with AI on our side that gives us a competitive advantage?
Our answer: Building for a use case.
In short (TL;DR)
Infodation’s AI journey is driven by one principle: build for the use case. Instead of chasing trends or experimenting for the sake of innovation, the focus is on purposeful applications that create real value. By rapidly prototyping, documenting learnings, and aligning every experiment with a clear goal, Infodation ensures AI becomes a practical advantage, not just another buzzword.
Infodation’s AI strategy is built on rapid experimentation
We encourage quick prototyping and feedback loops, which gives us fast insight into the success rate of specific technologies. Anybody can propose an idea, we support them in finding additional resources and knowledge, and together we explore feasibility. But we’ve learned to say no to projects that lack a clear use case- whether internal or customer-facing.
This epiphany is very mundane, I admit. But making it one of the guiding principles of our AI strategy is surprisingly hard. A lot of people at Infodation, and especially those participating in our AI experiments are passionate about what they do. With that passion often comes a focus on what is possible, not what is useful. Building new features for the joy of exploring the edges of a new technology can be exhilarating, but without alignment to real-world needs, even brilliant tools risk becoming unused shelfware. A sad outcome, for the people, who build it, as well as the people who invested resources.
So we learn. And we adapt. We’ve introduced lightweight documentation standards for AI experiments. Each initiative now includes:
- Agile-style use case descriptions
- Clear learning objectives
- A post-experiment summary evaluating success
“AI isn’t about chasing what’s possible, it’s about building what’s useful. Purpose turns experiments into impact.”
Putting Our AI Principles into Practice
I think this approach is working. One of our longest running AI experiments will for the first time be applied to software not coded by Infodation. It creates automatic documentation based on Github repositories, and hopefully will save us days when the developers are starting to work on the unknown code. Another new experiment includes both technical and commercial use cases from day one. Multiple departments are collaborating to ensure the solution is viable and valuable.
We’re not chasing AI for its own sake. We’re building tools that solve real problems. That’s how we stay competitive. That’s how we make AI work for us.
