Responsible AI Governance and Oversight for Managers
Who approved this is not a question you want asked later.
Almost every organization using AI has a governance gap it cannot see. Someone approved a tool without authority to. A system went live without anyone naming who owns it. A model has been running for a year and nobody has checked whether it still works. None of it is visible until something goes wrong and the question becomes who decided.
This workshop builds the machinery. Not principles, which your organization already has and nobody applies, but the concrete structure: who may approve what, at what threshold, with what evidence, reviewed by whom, and recorded where.
Senior leaders and managers use this where AI is already in use and oversight has not caught up, or ahead of a rollout where getting it right first is cheaper. It is built for environments with real accountability obligations, including public sector settings subject to records requests, audit, and legislative scrutiny. The named-framework version of risk work is AI Risk Management Leadership Using the NIST Framework.
Content, scenarios, and examples are built around your actual work and your approved tools before delivery. Most groups train live online, and on-site in-person delivery is available by request. This workshop is available by request only, with a three participant minimum, and sits alongside our other leadership development workshops.
This course includes
- Live, instructor led workshop delivered in two 4-hour sessions
- A map of how AI tools actually get approved in your organization today
- Decision authority and approval gates defined by risk tier
- A governance structure for your area drafted during the workshop
- Digital learning materials and take home reference guides
- Certificate of completion for every participant
Description
The day opens with the gap. Participants map how an AI tool actually gets adopted in their organization today, from someone's idea to something in production, and identify every point where a decision was made and nobody was accountable for it. Most find that the real approval path and the official one are different documents.
The middle is structure. Participants define decision authority by risk tier, so a drafting assistant and a system that touches benefits eligibility do not go through the same gate. They build the approval questions that actually catch problems, decide what evidence is required before deployment, and work out who signs.
The last stretch is the lifecycle, which is where governance usually stops. Participants learn to monitor a system after the attention has moved on, catch drift when the world changes and the model does not, define the conditions that trigger a review, and build a record that answers who decided, on what basis, and when. They leave with a governance structure drafted for their own area.
What You'll Learn at a Glance
- Map how AI actually gets adopted versus how policy says it does
- Define decision authority proportionate to risk
- Build approval gates that catch problems before deployment
- Decide what evidence is required before a system goes live
- Monitor a system after launch and catch drift over time
- Define the conditions that trigger a review
- Build a record that answers who decided, on what basis, and when
Details
Duration
Prerequisites
There are no prerequisites for this workshop. It is designed for managers and senior staff accountable for AI oversight. Leaders who need a named, auditable risk framework can pair it with AI Risk Management Leadership Using the NIST Framework.
Your AI Training Goals, Customized to You
DWC provides expert AI training aligned to your organization’s needs, your funding requirements, and your participants’ skill levels. With customization at every level, from content to scheduling to delivery format, DWC builds AI training programs that produce measurable outcomes.
20,000+ Participants Trained | 90% Completion Rate | 1,500+ Organizations Served
Tuition
Responsible AI Governance and Oversight for Managers is available by request only, scheduled around your team.
- $495 per participant, with a three participant minimum
- Private training: request a custom quote. One on one sessions and teams below the three participant minimum are priced case by case
- Larger groups: request a custom quote. We price larger cohorts case by case based on group size, schedule, and delivery format
- Optional courseware: the Ethical & Responsible AI Student Guide (Logical Operations) is available at $65 per participant and is not included in the course price
Colorado state agencies, higher education institutions, cities, counties, school districts, and certified nonprofits may purchase this course directly through our State of Colorado Price Agreement. Request a quote and we will build pricing around your team.