A problem-first system for making high-stakes AI decisions
Most AI initiatives fail for reasons that have nothing to do with models, tools, or talent.
They fail because of two recurring issues:
1) Teams start with a solution instead of a problem
2) Leaders miss a clear decision lens for when and how to pursue AI
Experienced leaders know this pattern well:
Promising ideas that lead to "Let’s just pilot and see"
Those pilots “work” technically but collapse when scaled
Trade-offs are being glossed over because problem is slowly reshaped to fit the solution
Costs, risk, and failure modes surface far too late
AI amplifies this problem because its requirements are hard to specify upfront, and once commitments are made, decisions become expensive or irreversible.
This course is anchored in decisions, not just knowledge. If these thoughts sound familiar, this course is for you:
We’re betting too much on something we barely understand.
This could blow up with severe consequences.
Everyone wants agents; no one wants ownership.
If this fails, it’ll be on me.
Everything is moving too fast, but business impact still feels shallow
.
By the end of this course, you’ll own AI decisions, without relying entirely on vendors, technical teams, or external opinions.
What you’ll learn
A systems-first leadership OS for decision velocity, intelligent execution & AI-native transformation
Scope the right AI initiatives with business-first thinking
Identify when uncertainty is acceptable and when it isn’t
What assumptions must be true for AI to work?
How to deal with ambiguous situations, without waiting on perfect data
Separate information that is nice to know from information that is required to decide
Build decision artifacts on "AI / Not AI / Not Yet" with clear rationale
Observe AI system behavior to analyze what breaks, what surprises, what this means for scale.
Capability vs expectation growth
Distinguish good, bad and premature AI initiatives.
Know when to use RAG, agents, open vs. closed models, and why.
What trade-offs are being made implicitly?
How reversible is this decision? What would cause you to change your mind later?
Identify silent failure modes and downstream consequences
Define explicit kill criteria before pilots begin. Evaluate blast radius and cost of error
Where most orgs go wrong in tracking AI value
Learn directly from Vidhi
Vidhi Chugh
AI Executive & Board Member | Microsoft MVP | AI Patentholder | 150k students
Who this course is for
Enterprise leaders (heads of AI, data, or innovation) asked to sponsor or approve AI initiatives.
Entrepreneurs & Founders who are either building an AI-first company or integrating AI agents into an existing business
Product & Strategy Leaders looking for the tools to prioritize AI opportunities, assess feasibility, and measure ROI effectively
What's included
Live sessions
Learn directly from Vidhi Chugh 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
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
6 live sessions • 8 lessons
Week 1
Feb
6
Optional: AMA with Vidhi and an industry leader
The Problem-First Discipline
Feb
7
Day 1, Week 1
Thinking in AI Systems & Execution Readiness
Feb
8
Day 2, Week 1
Week 2
Feb
12
Optional: AMA with Vidhi and an industry leader
AI Failure Patterns – The Hidden Risks That Can Derail Your AI Initiatives
Feb
14
Day 1, Week 2
Live AI System Workflow
Feb
15
Day 2, Week 2
Schedule
Live sessions
6 hrs / week
3 hours per day, 2 days over weekend
Fri, Feb 6
1:00 PM—2:00 PM (UTC)
Sat, Feb 7
12:30 PM—2:30 PM (UTC)
Sun, Feb 8
12:30 PM—2:30 PM (UTC)
Async content
2 hrs / week
Pre-read to help accelerate the learning during live-sessions
Frequently asked questions
$950
USD
2 cohorts