AI Governance in Practice: From Intake to Impact for Enterprise Leaders
Asheesh Biyala
AI Executive & MBA Professor
AI governance shouldn't be where good ideas go to die
Your organization has more AI ideas than it can safely approve. Vendors are embedding GenAI into tools you already own. Teams are piloting agents. And someone just asked you to "stand up AI governance."
So you write principles. Form a committee. Build a 40-question form. Six months later, good ideas are stuck in review, risky tools slipped in through the side door, and nobody is watching models after go-live.
Governance shouldn't be a wall. It should be a fast lane for low-risk AI and a checkpoint for the risky kind.
I've built AI and automation programs across insurance, financial services, and healthcare, and I teach AI strategy to MBA students. This course is the operating playbook I wish I'd had: who decides what, how to triage requests, how to score risk and vendors, how to build your rules into the platform, and how to govern AI once it's live.
You won't leave with theory. Each week you'll apply the work to a real initiative from your own organization, alongside peers solving the same problem. By the end, you'll have a governance playbook you can take to your leadership.
What you’ll learn
Go from ad hoc AI approvals to a governance program you run end to end, from first idea to impact, with your own playbook in hand.
Define the governance bodies, roles, and decision rights that move AI from idea to approval
Draft a governance charter you can adapt to your organization's size, industry, and maturity
Sort requests into traditional ML, GenAI, agentic AI, or not AI at all
Triage quickly with practical intake tools so the right ideas move forward
Connect every AI initiative to a measurable business outcome
Model ROI that reflects the true cost of AI at scale, including usage and human review
Tier AI use cases by risk so scrutiny matches the stakes
Evaluate vendors and document the residual risk your organization accepts, grounded in NIST and industry practice
Use an AI landing zone so low-risk use cases move faster without new reviews each time
Define what each approval gate requires and who must sign off
Measure the value AI delivers in terms leadership trusts
Monitor drift and vendor changes, and know when to retrain, re-approve, or retire
Learn directly from Asheesh
Asheesh Biyala
AI Executive & MBA Professor | Built AI COEs across regulated industries
Who this course is for
AI or automation leaders asked to stand up AI governance who need a working operating model, intake, and risk process, not a policy deck.
Risk, compliance, and IT leaders who approve AI tools and vendors, and need a repeatable way to assess risk without becoming the bottleneck.
Business leaders in regulated industries whose AI stalls in review and who want to learn how governance decides so they get approved faster.
What's included
Live sessions
Learn directly from Asheesh Biyala in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
Your purchase is backed by the Maven Guarantee.
Course syllabus
Week 1
Design Your AI Governance Operating Model
Week 2
AI Intake: Predict, Generate, or Act, or Not AI at All
Schedule
Live sessions
2-5 hrs / week
Projects
2 hrs / week
Async content
1 hr / week
Frequently asked questions
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Reimbursement
Get your company to pay
Everything L&D needs: email template, receipts, and certificate of completion.
Get reimbursedTeam discount
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USD