A practitioner-built framework for risk classification, governance architecture, and implementation roadmaps designed for leaders who must act on AI policy now, not study it indefinitely.
Most AI governance conversations inside large organizations still center on abstract principles: fairness, transparency, accountability. These matter, but principles without operational architecture produce policy documents that change nothing. Meanwhile, procurement decisions are being made, vendor contracts signed, and AI-enabled workflows adopted with no governance infrastructure to evaluate risk, assign oversight, or establish accountability when systems fail.
This program closes that gap. It moves leaders from conceptual fluency to operational readiness, providing a classification framework, a governance architecture, and a concrete implementation roadmap they can deploy within their own institutional context.
The program is organized around a tiered governance model developed through direct advisory work with the European Commission, EU Global Technical Assistance Facility, and multilateral development institutions. Each tier corresponds to a level of organizational readiness and risk exposure.
```Building institutional understanding of what AI systems actually do, how they are procured, and where they are already embedded in organizational workflows. Most institutions discover they are further along in AI adoption than their governance structures acknowledge.
Establishing a risk taxonomy calibrated to the institution’s mandate. Not every AI application requires the same scrutiny. This tier builds the internal rubric for distinguishing routine automation from high-stakes decision support and allocating oversight accordingly.
Designing governance structures that evolve with the technology. Static compliance checklists become obsolete within months. This tier introduces continuous monitoring mechanisms, escalation protocols, and review cycles that keep pace with rapidly shifting capabilities.
An honest assessment of where AI is already operating inside global institutions, often in places leadership has not yet examined. Participants conduct a guided audit of their own organization’s AI exposure, surfacing embedded systems, vendor dependencies, and unacknowledged automation that existing governance frameworks do not cover.
A technically grounded, non-promotional overview of what large language models, multimodal systems, and autonomous agents can and cannot do. The emphasis is on failure modes, hallucination patterns, and the specific risks these systems pose to organizations whose decisions carry public consequence. No vendor demos. No hype.
Building a risk taxonomy that reflects the institution’s specific mandate, stakeholder obligations, and operating constraints. Participants work with a classification matrix that accounts for autonomy level, reversibility of decisions, affected populations, and data sensitivity. The output is a draft risk register they can take directly into internal policy discussions.
Navigating the emerging regulatory environment, from the EU AI Act and its risk-based classification system to the evolving positions of OECD, G7, and multilateral standard-setting bodies. This module is not a legal briefing. It is a strategic reading of where regulatory momentum is heading and how institutions can position their governance to be durable rather than reactive.
Designing the internal structures required to make AI governance operational. Who holds authority over deployment decisions? What triggers an escalation? How are third-party systems evaluated against institutional standards? Participants build a governance architecture that maps decision rights, review processes, and accountability lines tailored to their own organizational reality.
AI governance fails most often at the procurement stage. Institutions adopt vendor systems without the internal expertise to evaluate their claims, audit their outputs, or negotiate meaningful accountability terms. This module provides a due diligence protocol for AI procurement, including red flags, evaluation criteria, and contract provisions that protect institutional interests.
Governance architecture is only valuable if the institution actually adopts it. This final module addresses the change management challenge directly: sequencing implementation, building internal champions, securing executive commitment, and designing feedback loops that allow the governance framework to improve with use rather than calcify on a shelf.
This program is designed for people whose decisions shape how large organizations engage with AI. It is built for institutional contexts where the stakes of getting governance wrong extend well beyond the organization itself, into public trust, regulatory exposure, and the wellbeing of affected populations.
Participants should arrive prepared to assess their own institution’s current posture candidly. The program creates space for that honesty without judgment, because building effective governance requires starting from where you actually are, not where your public communications suggest you should be.
Delivery options: Available as a 2–4 day intensive workshop, a multi-week executive seminar, or integrated modules within existing institutional capacity-building programs. Delivered in-person at your institution or remotely. Cohort sizes typically range from 15 to 40 participants.
This is not an academic survey of AI ethics principles, and it is not a technology vendor pitch dressed up as education. Every framework, tool, and protocol in this program has been developed through direct advisory work with institutions navigating real governance decisions under real constraints.
The curriculum reflects hands-on experience designing AI governance strategies for EU-funded technical assistance programs, advising multilateral organizations on risk classification, and helping leadership teams build oversight capacity from scratch in environments where institutional memory of technology governance simply does not exist yet.
Peter Johnson serves as AI Expert for the EU Global Technical Assistance Facility, where he designs governance frameworks and training architectures for EU-funded programs operating across multiple regions and institutional contexts.
```A Finance Fellow with Scale AI’s Human Frontier Collective, he works with ML research teams on how frontier models engage with complex financial and institutional systems. His doctoral research at the University of the Basque Country examines how cooperative governance models compare to emerging decentralized autonomous organizations, with implications for how institutions can design participatory oversight at scale.
A former U.S. Foreign Service Officer with experience across 34+ countries, Peter teaches executive education at Humboldt University Berlin and has delivered institutional capacity programs for the European Commission, UNDP, YPO, and the BMW Foundation. He is a Professional Member of the Association of Professional Futurists and serves on the Bretton Woods Committee AI Policy Working Group.
Describe your institution, governance challenge, and objectives. We respond with a tailored proposal within five business days.