Leading Through Context: Leadership in the Age of Autonomous AI Agents

Combining the precision of the automotive industry with the speed of international tech startups, Daniel Gamber pursues a clear vision: AI must move beyond showcase projects and deliver measurable impact in core operational workflows. In this interview, he explains why context-based leadership is becoming a critical capability in the era of AI agents, how companies can implement high-impact use cases within days, and why Germany holds enormous potential for sovereign and economically sustainable AI solutions.

Your career in the field of artificial intelligence is impressive. What was the pivotal moment that led you to pursue this path as a leader? And when was the moment you deeply regretted it? 

I started my career in the planning-intensive 'zero margin for error' world of automotive, then experienced the complete opposite, building startups in Silicon Valley, China, and Europe.

The pivotal moment for starting Cambrion was the realization that the way we process unstructured information in business processes is set to change radically. Think, for example, of engineering requirements, freight documents, or product and supplier master data.

What excites me most now is seeing us move beyond 'chatting with AI' into an era where AI agents take on real value creation in processes.

Regrets? Honestly, none yet. But I do get impatient when I see pilot projects being launched without clearly defined success criteria that then fail to scale up. That's exactly why, especially when compared internationally, we see enormous potential in Germany for consistently integrating AI into core operational workflows with a clearly measurable ROI.

Leading is always related to transformation and development. Could you please be so kind and describe the company from this perspective as an environment you are operating in? How did the company manage to make AI a strategic priority and a commercial success? 

As a startup, we focus heavily on product development, but at the same time, we are in a constant race with well-funded competitors and large tech companies. Meanwhile, our customers are undergoing their own parallel triple transformation: cultural, process-wise, and technological. 

We respond to the resulting uncertainties as a reliable partner with an easy-to-use toolbox for structured data that can be deployed productively within days and delivers immediate, measurable impact. This creates trust and drives adoption.
Incidentally, this also applies to established software companies, whose margins are currently under severe pressure. We see a strong symbiosis here, as existing products need to be enhanced with robust AI functionality in order to remain competitive.

The increasing focus, including political focus, on sovereign EU solutions also helps.

You have been recognized by Capital as one of the top AI leaders in the DACH region. In your opinion, what makes leadership in the field of Data & AI special, and what aspect are you particularly passionate about?

An AI leadership role is currently unique in that it combines human leadership with the management of AI agents. As abstract as this may sound, it is already a reality. In both cases, leadership based on context is crucial – meaning clearly defined objectives, guardrails, and continuous feedback loops.

This also applies to our own core product, which increasingly collects user feedback automatically, thus improving the AI agents in the hands of customers continuously based on new context.

For efficient internal processes, I am a very passionate advocate of targeted AI assistants, such as in software development and sales. This is the only way we can manage our workload with such a compact team.

As a leader in such a dynamic field as AI, how do you inspire your stakeholders in top management and your team? And what strategic vision have you aligned them with? 

The gap between human intent and machine execution is increasingly disappearing. This inevitably changes the benchmark for productivity forever. Claude Code and, more recently, OpenClaw are opening more and more eyes to the capabilities of multi-agent systems that work effectively across functions. 

In my first role as a project manager at BMW, I would have loved to have had something like this at the core of a virtual PMO. However, it is important that such solutions can be used in a quasi-deterministic, secure, and robust manner.

By 2030, agents will account for up to 60% of the software market. With our agentic infrastructure, we are helping our customers on their way to this future and will be growing alongside them.

How does the company master the challenge of implementing and scaling successful use cases? Can you please share an example? 

We primarily scale via our product and our so-called Agent Lab. Today, non-technical users can use it to generate a targeted AI agent within minutes, using natural language and based on a sample document. This agent then immediately helps to process documents autonomously in the background, according to the respective process requirements. This flexibility is particularly exciting because it allows us to serve use cases across departments – whether in logistics, HR, finance, or engineering.

At LAUDA, specialized AI agents built on our platform handle the transformation and validation of handwritten test reports containing more than 200 measured values. They convert unstructured documents into structured, machine-readable data in under one minute, compared to the 30 minutes of manual processing previously required. This enables an automated compliance workflow within a controlled human-in-the-loop framework.

A leading automotive supplier relies on dedicated Cambrion agents to automatically classify several hundred new patent applications per quarter by relevance, technology field, and competitive context. Such a task previously required weeks of manual review.

You have been successful in the AI field for quite some time. How will this technology change leadership? And how will your leadership specifically evolve over the next three years?

I find it particularly interesting that we can now focus more than ever on the "what" rather than the "how." With the technological tools available today, new goals can often be achieved much more quickly. 

Combined with AI, good judgment in particular becomes an absolute turbocharger. However, creative processes and critical thinking must not be completely outsourced to tech. For me personally, no tool can beat a good whiteboard session.

In general, we at Cambrion also experience daily how radically different the working environments are. Your own AI bubble is one extreme, the decades-old paper process at the next German Mittelständler is the other. The only thing that helps here is to stay curious, meet quickly in the middle, and have some fun while doing so. 

As a successful participant in the second cohort of the “Best of AI-Award", have you experienced positive effects from your participation?

Absolutely. In the current market environment, it is difficult to distinguish substance from hype – so we can only express our gratitude for the trust placed in us and the extra attention we have received. 

In fact, since the award ceremony in Berlin alone, we have already had several interactions with participants, ranging from a joint webinar to collaboration on new use cases.
We’re excited about the continuous expansion of this high-quality network.

Mr. Gamber, thank you very much for this insightful conversation.