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AI & Robotics Institute
Making biological matter computable and testable, at scale, and by any scientist.
Gillian Davis
Gillian Davis is SVP, Strategic Delivery for AI & Robotics at EIT, where she partners with executive leadership to drive delivery, decision velocity, and organisational performance across complex, multi-stakeholder transformation. Her work focuses on turning ambitious strategy into execution by building the leadership infrastructure, operating cadence, and behavioural alignment required to scale.
Prior to joining EIT, Gillian was the founder of OverTime Leader, a leadership consultancy supporting founders, CEOs, COOs, and senior teams through growth, change, and performance pressure. Her work spans robotics, AI, venture-backed scale-ups, media, and creative industries, partnering with organisations across Europe and North America.
She is the author of “First Time Leader: Foundational Tools to Inspire and Enable Your New Team” (Wiley, 2014) and is known for combining commercial clarity with behavioural insight, enabling teams to move faster, together, without losing the human edge.
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By applying and extending frontier AI and robotic methods, the AI & Robotics Institute hopes to accelerate breakthrough scientific discoveries. This work is driven by the aim of exploiting the rich potential of biomolecular foundation models in areas such as bacterial genomics and the structure and dynamics of enzymes. Another area of focus is combining advanced AI models with robotic systems to run AI-guided experiments, including building the data and computing infrastructure that supports them. This enables ideas to be generated, tested, and refined in a rigorous and repeatable fashion. These groundbreaking technologies are designed in such a way as to enable scaling and translation into sustainable solutions that change society. The AI & Robotics Institute is strengthened through its close collaboration with the University of Oxford, including via a Centre for Doctoral Training funded by EIT. Our experts work across disciplines and are supported by access to large-scale compute and rich datasets.


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