Although startups are booming, many fail when it comes to scaling up. Nico Gaviola, VP of Digital Natives & Emerging Enterprise at Databricks EMEA, explains how Agentic AI can help.
The German start-up market is experiencing a historic boom. In January 2026, the Startup Association recorded a milestone: A total of 3,568 new companies were founded that month alone, which is around a third more than in the same period last year. Notably, 853 of these new companies are in the software sector, with a third of all founders reporting that they work with artificial intelligence. The association views technology as the driving force behind this growth.
The startup scene is also thriving across Europe. According to the State of European Tech 2025 report, commissioned by Atomico, around 44 billion US dollars was invested in the sector last year. Of this, 36 per cent went to AI and Deep Tech – fields that investors now consider essential for their portfolio companies.
However, record numbers and capital inflow alone do not guarantee sustainable growth. Many startups do not fail during the initial start-up phase, but rather when scaling up their business models. Bureaucracy, unclear data sovereignty, cross-border operations and compliance requirements can hinder companies that initially showed promise. This is where an approach increasingly being discussed as a strategic core in the industry comes into play: Agentic AI.
A solid data foundation is a prerequisite.
Agentic AI involves using autonomous AI agents that can independently perform tasks, make decisions and interact with other systems. For these agents to deliver reliable results, however, a crucial foundation is needed: a unified, structured data architecture. Start-ups have a structural advantage in this regard, as they do not have to overcome legacy IT systems and can implement a clean data strategy from the outset.
Founders who break down data silos early on and integrate AI agents into their core processes are better placed to achieve rapid, scalable growth. Well-trained agents, fed with company-specific data, can solve specialised, complex tasks. When multiple agents are linked together, they can handle even cross-cutting challenges automatically.
For example: A customer support agent and a forecasting agent can collaborate to automatically generate cost estimates upon receipt of a support case. This accelerates response times and increases customer satisfaction, which are critical factors for growing companies with limited personnel resources.
Internal processes and real-time decisions
The use of AI agents is not limited to customer contact. Routine processes can also be automated within internal administration. Management and investors can therefore access real-time overviews of liquidity, revenue and profit. This data transparency enables informed decisions to be made quickly – a crucial factor for companies that need to respond flexibly to market changes.
Governance as a Competitive Advantage
However, with the deployment of autonomous systems, the demand for control and regulatory compliance is also increasing. In Europe in particular, regulations such as the GDPR and the EU AI Act play a central role. Rather than viewing this compliance pressure as an obstacle, it should be seen as an opportunity. Start-ups that integrate governance into their AI strategy from the outset build trust with customers, employees, and regulatory authorities.
Transparency regarding data provenance, versioning and evaluation results is essential. This gives teams control over the data basis on which agents operate, their behaviour, and how their outputs change over time.
Parloa, a German startup valued at three billion US dollars, is a practical example. The company has built its customer service around AI agents while simultaneously developing a data architecture that complies with the GDPR and the EU AI Act. The “Privacy by Design” principle allows the use of sensitive customer data without losing control. Completely managing the agent lifecycle makes governance tangible and scalable for teams.
However, those who start too early risk creating more complexity than added value.
Despite all the opportunities, experts warn against hasty deployment. Startups that introduce AI agents without first establishing a unified data foundation and governance processes risk creating more problems than they solve. The correct order is to first secure the data architecture and then integrate the agents.
In the long term, the companies that will successfully scale are those that understand AI not as an add-on but as the strategic core of their business model and that focus on control, quality, and regulation from the beginning.
