Matthew Kilbane

AI Governance as a Catalyst for Space Innovation Matthew Kilbane | ISU AI Applications in Space Workshop | November 2025 AI governance is not a constraint on space innovation — it is the engine that drives it. This presentation makes the case that embedding standards, policies, and regulatory frameworks directly into AI-powered workflows enables space organizations to operate more safely, transparently, and at scale. Drawing on the Ship of Theseus metaphor, Kilbane demonstrates how governance preserves mission identity and accountability even as spacecraft, crews, and systems continuously evolve. Through a Retrieval-Augmented Generation (RAG) approach, governance artifacts — ISO standards, IFRS, and global regulatory text — are injected into AI prompts, turning compliance into an automatic, traceable, and defensible part of daily operations across HR, IT, procurement, finance, and mission risk management. The presentation concludes with a scaling vision: governance that begins at the workflow level can align with international regulatory frameworks (EU AI Act, Japan's AI Promotion Act, China's AI Administrative Measures) and ultimately power interoperable, auditable AI systems across borders and sectors — positioning AI governance as a civilizational milestone on the path to planetary-scale coordination. Key Message: Embedded standards = Auditable, Defensible, Future-ready.

This presentation examines how AI is reshaping work not only by increasing productivity, but by forcing leaders to make hard choices about layoffs, shorter workweeks, and redeployment of human talent. It frames AI and labor as a governance and design challenge rather than a purely technical one, arguing that the future of work will be determined by the decisions organizations make when newly freed capacity appears. Drawing on the theme “From Mass Layoffs to Shorter Weeks,” the session explores how agentic AI can collapse traditional organizational layers, alter decision-making structures, and expose the limits of inherited workplace models built for the industrial era. It presents a practical framework for evaluating three divergent paths—labor extraction, shared productivity, and strategic redeployment—while highlighting the long-term consequences each path has for resilience, retention, innovation, and social stability. Designed for conference audiences interested in AI governance, workforce strategy, and the future of institutions, the talk combines systems thinking with concrete workplace scenarios, including productivity gains, tipping points for safe automation, portfolio careers, and the need to redesign learning and labor systems in parallel. The result is a forward-looking discussion of how AI can either deepen inequality or help build a more humane, flexible, and prosperous model of work