"AI-First Starts with Thinking" – CTO Rodrigo Benz on AI at deepico
At Deep Impact, Artificial Intelligence is not an add-on – but a central component of strategy, product development, and technological thinking. CTO Rodrigo Benz explains what AI-First means in practice.
English edition — originally published in German as „AI-First beginnt beim Denken" – CTO Rodrigo Benz über KI bei deepico.
AI-First as a Product Strategy
At Deep Impact, Artificial Intelligence is not an add-on – it's a central component of strategy, product development, and technological thinking. This is particularly evident at deepico, a Deep Impact venture: The independent AG is developing a modern Cloud ERP platform for the food industry – with the goal of intelligently automating business processes and making them more controllable.
In an interview, CTO Rodrigo Benz explains what AI-First means in practice, how to achieve technological AI readiness – and why pragmatic experiments are key to innovation.
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Rodrigo, what do you understand by the term "AI-First" – and how does that fit with your current product strategy at deepico?
For me, AI-First means that even during the initial solution finding, we consider which tasks a model can take on and what data it needs for that. Functions are designed so that intelligence is the core of the solution. This doesn't result in a retrospective "chatbot add-on," but rather a function whose added value is based on AI.
> At deepico, we are already heavily focused on digitalization and automation across the entire value chain. AI-First helps us to purposefully weave future innovations into our Cloud ERP products.
What's important to us is: AI should solve real problems, not just be a buzzword.
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How do you ensure that your platform is technologically ready for the future use of AI?
We consistently rely on a modern cloud architecture. But above all, the foundation we have created with it is important: Whether a model predicts the future needs of our customers, automatically assigns incoming receipts to the correct orders, or detects irregularities in production data – we can quickly integrate it into our platform, observe it live, and activate it upon success.
This ability to experiment quickly and with low risk makes us maximally agile for upcoming AI functions.
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How do you already integrate the topic of AI into your product development – even if it's not yet visible in the product itself?
AI already accompanies us in most phases today:
- In solution finding, we discuss ideas with LLMs and arrive at viable concepts more quickly.
- In prototyping, we generate proofs of concept in a few hours that would have taken days before.
- In coding, tools like GitHub Copilot noticeably increase speed and test coverage.
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What do you consider meaningful criteria for when and where AI truly brings added value in a digital product?
AI creates real added value where it eliminates noticeable bottlenecks in daily work:
- When manual data entry and checks are automated
- When paper- or Excel-based processes finally flow digitally
- When decisions are more precise thanks to quick analyses
When processes run noticeably smoother, the investment practically pays for itself.
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What advice would you give to tech teams or startups that are under pressure to "do something with AI" – but don't yet see a clear use case?
Start with small internal experiments, for example, automated reports or test data generation. This way, you gain experience without involving customers. Keep the experiments inexpensive. Low-code tools are usually sufficient. Good ideas usually grow naturally from these quick wins.