From lab automation to tech scouting — how AI is becoming a natural part of our workflows.
Artificial intelligence is increasingly shaping how we approach research and innovation — from lab automation to literature analysis. At Gubra, we’ve been integrating AI into our work because it helps us move faster, think broader, and make more informed decisions. Whether it’s scouting emerging technologies, interpreting behavioral data, or connecting insights across teams, AI is becoming a natural part of how we work.
In this post, we share how AI fits into our workflows today, what we’re learning through hands-on use and collaboration, and how we separate the hype from what’s practically useful.
Supporting Innovation and Scouting
One of our current focus areas is exploring how AI can strengthen early-stage tech scouting. As part of this, we’ve initiated a collaboration with the Norway-based AI company zitilite to co-develop an AI-powered approach to innovation screening — combining AI reasoning models and AI reasoning agents in conjunction with structured expert input to help identify relevant signals and emerging opportunities. Human feedback to the AI systems is an integrated part to improve the output iteratively and to enable learning of the AI systems.
The goal is to enhance how we evaluate new technologies, connect dots across domains, and prioritize where to dig deeper. Beyond tech scouting, we also use AI to support rapid literature synthesis, compare trends across areas, and explore early-stage ideas. These tools help us stay responsive and open, especially in fast-moving or unfamiliar domains.
Together with zitilite we have developed an AI strategy to lay the foundation and support our decisions on practical AI implementation. Moreover, a company-wide AI potential evaluation was conducted to identify close to 200 AI use cases across the department and business functions – include value estimation hereof for prioritization and roadmap for the AI use case development.
It’s an exciting step toward building a more scalable and insight-driven scouting process. At zitilite, we are immensely proud and inspired by the close collaboration with a partner like Gubra,
Working With Experts, Not Replacing Them
AI only becomes valuable when it’s used in partnership with expertise. Across Gubra, we work closely with scientists, analysts, and external collaborators to ensure that AI outputs are relevant, accurate, and meaningful.
For example, when exploring AI-assisted interpretation of behavioral data, our in vivo teams play a central role in reviewing and validating which signals matter — and which ones require a closer look. This collaborative loop is what makes the technology useful in practice.
A Realistic Approach
We treat AI like any other scientific tool — one that needs to be tested, evaluated, and refined. Some models help us draft summaries or visualize data more clearly. Others are useful for exploring new ideas or identifying patterns. But no tool replaces critical thinking or biological insight.
That’s why our approach is based on small pilots, feedback loops, and a clear focus on what actually works in a real-world setting.
Looking Ahead
The field is evolving quickly, and so are we. From behavioral data exploration to AI-enhanced scouting, we’re actively building new capabilities that combine scientific expertise with digital tools. Our work is grounded in experimentation, collaboration, and a commitment to real-world impact.
If you’re looking for a partner pushing the boundaries of translational research and innovation, do not hesitate to contact us.
