Typical format: 90–120 minute facilitated enterprise workshop; adaptable for private leadership or cross-functional teams.
Move trustworthy AI from principle to system design
Enterprise AI conversations often acknowledge the importance of governance, security, human oversight, and responsible use without translating those principles into the way an actual AI system should behave.
Building Trustworthy AI Systems is a practical workshop for leaders and delivery teams who need to connect AI opportunity with the information, evaluation, permissions, controls, operating practices, and accountability required for real-world use.
What we will explore
- How the consequence of an AI workflow should change the level of control and evidence required.
- Why information quality, provenance, access, and retrieval are part of trustworthy AI—not separate data concerns.
- How to distinguish recommendation, decision, and action so human accountability remains explicit.
- What evaluation should measure beyond whether a model produces a plausible answer.
- How permissions, logging, escalation, failure handling, and monitoring can become part of the product design.
- Where governance should enable safe use rather than becoming paperwork added after a prototype succeeds.
Who this is for
The session is designed for CIOs, CTOs, data and AI leaders, enterprise architects, security and governance stakeholders, and senior product or transformation teams working on consequential AI use cases.
What participants should leave with
Participants should leave with a clearer way to frame AI risk around a real workflow, identify the controls and evidence that matter most, and distinguish use cases that are ready to prototype from those that need stronger foundations first.
Adapt the workshop to your organization
The workshop can be tailored around an existing AI initiative, regulated workflow, internal assistant, retrieval system, decision-support capability, or early-stage AI portfolio.
