Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

AI Operating Models and Organizational Design

IBM via Coursera

Go to class Write review

Details

Go to class

Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

IBM Lead AI Transformation: Strategy, Governance & Execution

Overview

Google, IBM & Meta Certificates – 40% Off

One Coursera Plus subscription covers most Professional Certificates on Coursera.

Unlock All Certificates

Scaling AI is about organizational design as much as technology. This IBM course helps you structure the teams, roles, decision rights, and foundations that let AI move quickly and responsibly. Compare operating-model archetypes, such as a centralized Center of Excellence, a federated model, and a hybrid hub-and-spoke. Match a model to an organization's context and maturity, by weighing speed against consistency and control. Define the new roles AI creates, from AI product owners to governance leads. Map who makes decisions when an AI system is involved, closing the accountability gaps. Examine why data readiness, infrastructure, and hybrid-cloud scalability make or break AI at scale. Clarify which decisions you own and which you should delegate. Finally, extend a working model across business units, adapting it to a Sample Enterprise that’s scaling up quickly. Methods are taught tool-agnostic, with IBM and other tools demonstrated by industry experts.. You'll be able to design a practical operating model for a realistic enterprise.

Syllabus

Choose Your AI Operating Model
- How should an enterprise organize itself to do AI? This module gives AI transformation, digital transformation, and organizational-design leaders a way to choose an AI operating model that fits their organization's context and maturity. It compares the main structural options for scaling AI — centralized Centers of Excellence, federated business-unit ownership, and hybrid hub-and-spoke models — and lays out the trade-off reasoning that leading enterprises use to balance speed, consistency, and governance. The module weighs AI maturity, decision rights, governance and regulatory exposure, talent distribution, and workflow ownership as the situational factors that drive operating-model design. No coding background is required. The module turns scattered AI pilots and experiments into a deliberate structure for enterprise AI capability, adoption, and value at scale.
Roles, Responsibilities, and Decision Rights
- This module helps leaders design the roles, responsibilities, and decision rights that keep enterprise AI accountable. Learners define the new accountability roles AI adoption creates — AI product owners, model owners, data and AI stewards, responsible-AI leads, and AI governance leaders — and map who decides what when an AI system shapes a consequential decision. Using established decision-rights frameworks such as RACI, DACI, and RAPID, learners assign a single accountable owner, set how much authority an AI system holds, and build the human oversight, escalation, and audit trails that catch a wrong recommendation. Built for AI transformation, organizational-design, and AI governance leaders with no coding background, the module turns AI governance from a policy document into a working accountability structure — closing the gaps where ownership over AI decisions otherwise falls through.
Data and Infrastructure Foundations for Leaders
- Which data and infrastructure decisions does an AI leader own, and which belong to the technical teams? This module helps non-technical leaders build the data and infrastructure foundations that let enterprise AI move from pilot to scale. Learners read the AI foundation as a three-layer stack of infrastructure, platforms, and applications; assess data readiness, data quality, and data governance; and weigh data fabric versus data mesh as answers to a fragmented data estate. They compare cloud strategy options — hybrid cloud, multi-cloud, and per-workload placement — and make build, buy, or partner decisions with a clear method. Built for AI transformation, data strategy, and digital transformation leaders with no coding background, the module turns data architecture, AI governance, and infrastructure into deliberate leadership decisions rather than delegated plumbing.
Scale the Operating Model Across Units
- This module shows non-technical leaders how to take an AI operating model that already works in one business unit and extend it across the whole enterprise, without creating a central bottleneck or a fragmented shadow build. Learners choose among centralized, federated, and hub-and-spoke operating models; read organizational maturity and readiness before scaling; and split primary ownership between a shared central hub and locally adapted business units. They learn to operationalize AI governance so risk scales with the model, compose cross-functional fusion teams, and roll out the model unit by unit. Built for AI transformation, digital transformation, and enterprise AI leaders with no coding background, the module turns scaling AI, organizational design, decision rights, and change management into a repeatable, tool-agnostic leadership skill.