Léa Steinacker
Most organisations evaluating AI can assess technical performance. Few can assess what AI systems do to decision-making structures and accountability lines once deployed. That gap, between what AI promises and what it changes about how organisations operate, is where governance risk accumulates before it becomes visible.
A social scientist and Spiegel bestselling author, Dr. Léa Steinacker helps boards and executive teams understand the governance, workforce, and accountability consequences of AI adoption, grounded in peer-reviewed research and her doctoral Code Capital framework.
Full Profile
Why organisations work with Léa Steinacker
- Her “Code Capital” framework, developed through her University of St. Gallen doctorate and published by Nomos Verlag, gives executive teams a structured way to analyse what is embedded in AI systems from design through deployment, something most AI governance conversations currently lack.
- Her peer-reviewed research covers specific, contested AI applications, including cross-national surveys of facial recognition acceptance published in Government Information Quarterly and Public Understanding of Science, which gives her arguments empirical grounding rather than assertion.
- Her 2024 book co-authored with Prof. Dr. Miriam Meckel (Rowohlt Verlag) reached both the Spiegel and Manager Magazin bestseller rankings, evidence that her framing of AI’s organisational and societal consequences resonates with senior business audiences, not only specialist ones.
- As co-founder of ada Learning, which has equipped thousands of employees from DAX companies, SMEs, and governments with AI transformation skills, she has tested her frameworks inside major institutions at scale, not only described them from the outside.
- Board-level experience at Weleda AG and active leadership as CEO of GaiaLogic AG mean her perspective on AI governance comes from someone directly accountable for it, not only someone who analyses it.
Biography highlights
- PhD from the University of St. Gallen; doctoral thesis “Code Capital” published as an academic monograph by Nomos Verlag (2022)
- Co-author with Prof. Dr. Miriam Meckel of “Alles überall auf einmal” (Rowohlt Verlag, 2024), ranked on both the Spiegel and Manager Magazin bestseller lists; published in English as “Everything Everywhere All At Once: How Artificial Intelligence Impacts Our World” and in Korean
- CEO of GaiaLogic AG; co-founder and former Executive Chairwoman of ada Learning GmbH, which has equipped thousands of employees from DAX companies, SMEs, and governments with AI transformation skills
- Member of the Board of Directors of Weleda AG; former member of the AI Advisory Board of Ringier AG
- Lecturer at the University of St. Gallen, teaching “Social and Economic Impacts of Artificial Intelligence”
- Peer-reviewed publications in Government Information Quarterly, Electronic Markets, Public Understanding of Science, and Data and Policy (Cambridge Core)
- Degrees from Princeton University (A.B.), Harvard Kennedy School of Government (MPP), and the University of St. Gallen (PhD)
- Forbes 30 Under 30 Europe; Medium Magazine Top 30 Under 30 Journalists; BCG Thought Leader; Atlantik-Brücke Young Leader; Munich Young Leader (Munich Security Conference and Körber Stiftung); Henry Richardson Labouisse Prize, Princeton University
Biography
Organisations moving quickly on AI adoption are finding that their governance structures are not keeping pace. Boards are increasingly accountable for what AI systems do to their decisions and their people, yet most lack the analytical frameworks to evaluate it. Léa Steinacker has spent a decade building exactly those frameworks.
Her doctoral research at the University of St. Gallen produced Code Capital, a sociotechnical framework for analysing AI systems from design through deployment. Published by Nomos Verlag in 2022, it gives leaders a structured way to identify what AI systems actually contain, from design assumptions to deployment risks. Her peer-reviewed work, covering facial recognition adoption across four countries and published in journals including Government Information Quarterly and Public Understanding of Science, brings empirical grounding to arguments that too often stay at the level of principle.
Her 2024 book with Prof. Dr. Miriam Meckel (Rowohlt Verlag) became an immediate Spiegel and Manager Magazin bestseller and was subsequently translated into English and Korean. It moved the question of what AI means for people, organisations, and societies from ethical debate into practical leadership conversation. She now carries that perspective directly into practice as CEO of GaiaLogic AG and as a lecturer at the University of St. Gallen.
As a member of the Board of Directors of Weleda AG, her perspective on AI governance is shaped by institutional accountability, not only academic analysis. She co-founded ada Learning, which has equipped thousands of employees from DAX companies, governments, and civil society organisations with AI transformation skills. She holds degrees from Princeton University, the Harvard Kennedy School of Government, and the University of St. Gallen.
Key speaking topics
- AI governance and responsible deployment
- The societal and organisational implications of AI
- Code Capital and sociotechnical analysis of AI systems
- AI regulation and the European policy framework
- Workforce transformation and AI readiness
- Human-AI collaboration and decision-making
- AI and the future of knowledge work
Ideal for
- Boards and C-suite executives evaluating AI governance and accountability structures
- Chief Technology Officers, Chief Digital Officers, and transformation leads navigating AI adoption
- Policy, legal, and compliance teams working on AI regulation and enterprise risk
- Senior HR and people leaders managing workforce readiness and AI-driven organisational change
Audience outcomes
- A framework, grounded in the Code Capital model, for evaluating what AI systems do to organisations beyond their stated technical capabilities
- Clearer understanding of where AI governance risk accumulates before it becomes visible at board level
- Practical grounding in European AI regulation and what it means for organisational decision-making and liability
- A more precise vocabulary for asking the right questions about AI in leadership and governance contexts
- Greater clarity on how to structure workforce readiness initiatives that treat AI transformation as an organisational challenge, not only a skills one
Talks
Drawing directly on the Spiegel bestselling book co-authored with Prof. Dr. Miriam Meckel, this talk examines how AI is reshaping society, organisations, and work, and why the governance decisions made now will define the terms on which that reshaping happens.
Key takeaways:
- Why the current moment in AI development makes governance choices unusually consequential for organisations and societies
- What organisations stand to gain from AI adoption, and where structural risks are embedded rather than obvious
- A frame for approaching AI not as a single technology to adopt but as a set of decisions about values, accountability, and what organisations want to become
This talk maps the practical governance terrain organisations face as European AI regulation becomes operational, and gives senior leaders a way to navigate compliance requirements without treating them as a ceiling on what is possible.
Key takeaways:
- What the EU AI Act requires of organisations at different levels of AI system risk, in terms a board can act on
- How to align AI governance frameworks with competitive strategy rather than treating them as forces in opposition
- Where compliance gaps are most likely to emerge during AI deployment, and how to address them before they become costly
This talk addresses workforce AI readiness as an organisational transformation question rather than a training exercise, drawing on Steinacker’s experience co-founding and scaling ada Learning with thousands of employees from DAX companies, governments, and civil society organisations.
Key takeaways:
- Why most AI upskilling programmes fail to change how organisations actually work, and what a structural approach looks like instead
- How to identify the capability gaps that matter most at different levels and functions within the organisation
- A practical framework for moving from AI awareness to genuine AI readiness across a diverse, multigenerational workforce