AI Project Management: Deliver AI Products That Ship
 

AI Project Management: Deliver AI Products That Ship
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 10m | Size: 1.39 GB

Techniques and Strategies for Successful AI Product Delivery

What you'll learn
Run AI initiatives through a four-phase framework (Intake, Design & Build, Readiness & Release, Monitoring) with clear go/no-go gates
Frame AI opportunities with a measurable value hypothesis, explicit scope, a risk tier, and a governance path before any build begins
Manage AI-specific risks such as hallucinations, agent autonomy, and drift using evaluation datasets, human-in-the-loop design, and red teaming
Release and operate AI products with ring-based rollouts, a health dashboard, AI incident playbooks, and keep/change/retire reviews

Requirements
No AI experience needed. Project Management , Program Management, Business Process skills are helpful.

Description

Most AI projects never make it to production. This course teaches you how to deliver the ones that do.
Companies are spending record amounts on AI, yet about 95% of enterprise GenAI pilots show no measurable impact on profit (MIT NANDA, 2025), and more than 80% of AI projects fail, twice the rate of other IT projects (RAND). The technology is rarely the problem. The problem is delivery.

AI products behave differently from traditional software. They give plausible answers that are sometimes wrong, they depend on data, and they change over time. Agents go further: they take actions in real systems. Pure waterfall breaks down because you can't specify accuracy up front. Pure agile breaks down because leadership, risk, and compliance teams need evidence before they approve a release.

This course gives program and project managers a practicalAI Delivery Framework that bridges both worlds. Executives get gates and evidence, and teams keep room to iterate.

What you'll learn
-Why AI delivery is unique: hallucinations, agent autonomy, human-in-the-loop design, evaluation, drift, and adoption

-Phase 1, Opportunity & Intake: turn vague AI ideas into scoped, measurable initiatives with a risk tier and a governance path

-Phase 2, Design & Build Iterations: make the four design decisions that shape every AI product, run a four-lane backlog, and apply an "AI Definition of Done" that gathers evidence as you build

-Phase 3, Readiness & Release: stress testing, red teaming, operational and people readiness, ring-based rollouts, and a documented go/no-go decision

-Phase 4, Monitoring & Evolution: one dashboard that answersIs it good? Is it working? Is it used? Is it safe? , plus drift detection, AI incident playbooks, and keep/change/retire reviews

See it applied
You'll follow a real-world initiative through all four phases and see every deliverable along the way: opportunity briefs, charters, evaluation plans, readiness checklists, rollout plans, and monitoring dashboards.

Make it stick
The course closes with concrete steps for your first week and first month, and the four traps that catch teams adopting a new framework.

No coding required. The framework works alongside the methodology you already use (Agile, Waterfall, SAFe, or hybrid) rather than replacing it.

About your instructorsZorina Alliata is Director of AI Enablement and AI Adjunct Faculty at Georgetown University and OPIT. She helps enterprises move AI from pilot to production, people first.Lyuba Berzinis the co-founder of DeepPlum AI, a consulting company advising small and medium business of using AI efficiently.

Who this course is for
Program and project managers who have been handed an AI initiative and need a proven way to take it from idea to production
Delivery leads and PMO leaders who need gates, evidence, and governance for AI work without slowing down their agile teams
Scrum masters and agile coaches supporting teams that build AI products, agents, or GenAI features
Managers in regulated industries (finance, insurance, healthcare, automotive) who must deliver AI that stands up to compliance and audit
Experienced project managers who are new to AI. No coding or data science background is required.

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