Methodology · Cross-industry · In use
Data & AI Value Method
ADET: our method for data and AI projects
ADET takes every project through four phases from first analysis to handover: Assess, Design, Engineer, Transfer. In each phase it is clear who decides, what is delivered and how your team is involved.
Starting point
Many data projects fail not because of technology but because of unclear goals, a team that is not involved and solutions nobody can continue once the project ends.
We therefore developed a method that safeguards exactly these points from the start.
What we did
- Assess: capture the decision question and the data situation, without assumptions about technology.
- Design: agree on solution structure, scope and responsibilities together.
- Engineer: deliver robustly, in short, verifiable steps.
- Transfer: secure the handover so the solution works without us.
Outcome
Every project on this page follows this method. It builds on the core principles of CRISP-DM and is tailored to mid-sized companies.
For us, a project is only finished when the solution works without us.
More projects
Research project in archaeology
DiDo: AI that detects stones in archaeological images
In the DiDo research project we are building an AI model that detects stones in archaeological images. After a feasibility test with a pre-trained model, we are training our own detection model, supported by AI:AT.
Two tourism regions in Tyrol
Understanding guest behaviour from guest card data
Which guests come, which offers do they use and when, and how does their behaviour change? We analysed the guest card data of two regions so that strategic decisions rest on data.
Mid-sized company
Starting with AI, together with the whole team
A company is starting its AI journey and bringing every employee along. After a joint kick-off and clarifying the legal foundations, the focus is now on the right use cases.
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