Microsoft Platform Orchestration

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

3 weeks, 1 hour a week
Sessions

Self-Paced
Level

Advanced
Subtitles

English

Found in

Part of

Microsoft AI Transformation Leader

Overview

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This course prepares Senior Operations Leaders, IT Architects, and C-Suite Executives to move beyond initial tool deployment and drive autonomous business models across the enterprise. You will explore the transition from static software-as-a-service (SaaS) to dynamic "Service-as-a-Software," where autonomous agents execute complex, multi-step business processes with minimal human intervention. Throughout the course, you'll learn to manage the "Human-Agent Symbiosis"—redesigning workforce structures and operational incentives to transition your teams from traditional creators to strategic orchestrators. Ideal for leaders navigating outcome-based procurement and platform scalability, the course tackles the emerging complexities of multi-agent orchestration, cross-border AI compliance, and the structural future of the AI-native enterprise. By the end of this course, you will be equipped to project the 3–5 year impact of AI on your business model, defend against frontier risks like agentic drift, and lead your organization through the post-SaaS transition.

Syllabus

From Copilots to Agents: Analyze the Assisted vs. Agentic Distinction

Most organizations deploying AI today are deploying Assisted AI—tools that augment human decisions by generating outputs that humans then act upon. Agentic AI is fundamentally different: It executes sequences of actions autonomously, makes intermediate decisions without human review at each step, and takes actions in the world—sending communications, updating records, triggering downstream processes—on the organization's behalf. The executive's analytical challenge is not choosing between the two; it is knowing precisely which workflows are appropriate candidates for agentic execution and which ones require the human judgment that autonomous systems cannot provide.

From Copilots to Agents: Architect the High-Level Agentic Workflow

Authorizing an agentic deployment requires the executive to evaluate whether the proposed workflow architecture is sound—not to build the architecture, but to assess it. This module gives executives the architectural vocabulary and design principles to evaluate a proposed agentic workflow design: Whether the data connections are governed, whether the self-correction mechanism is designed or assumed, whether the oversight touchpoints are built into the architecture or left to informal monitoring, and whether the workflow boundary is defined precisely enough to prevent agent scope creep.

The Post-SaaS Economy: Evaluate the Shift to Outcome-Based Pricing

Seat-based SaaS licensing gave organizations predictable costs, clear per-user accountability, and relatively straightforward vendor relationships. Outcome-based AI pricing changes all three: costs become variable and tied to AI task execution volume, accountability shifts from user seats to business outcome metrics, and vendor relationships become performance contracts rather than software subscriptions. This module gives executives the evaluation framework to assess what this shift means for their organization's financial exposure, vendor leverage, and procurement governance.

The Post-SaaS Economy: Develop the AI Interoperability Procurement Strategy

The organizations that will have the most strategic flexibility in a rapidly evolving AI market are the ones that built interoperability and data ownership requirements into their procurement strategy before signing multi-year AI commitments. This module gives executives the procurement strategy framework to evaluate vendor lock-in risk, define data ownership requirements, and build interoperability standards into AI contracts so that the organization's ability to move, switch, and reconfigure as the market evolves is protected rather than surrendered in the initial procurement decision.

Workforce 2.0: Analyze Workflows and Reconstruct Org Structures

Organizational structures were designed around the assumption that humans do the production work. When AI agents take over production execution in defined workflow segments, the organizational structure built around that assumption becomes misaligned—roles are defined by tasks that AI now performs, reporting structures reflect production hierarchies that agentic workflows have made obsolete, and headcount is allocated to execution capacity that the organization no longer needs at human scale. This module gives executives the analytical framework to identify where organizational structure has become misaligned with agentic workflow reality and reconstruct it around orchestration rather than production.

Workforce 2.0: Design Orchestration Incentive Structures

Redesigning roles around orchestration without redesigning the incentive structures that govern performance is the most common organizational design failure in AI-driven workforce transitions. Employees whose performance is still measured by production output will optimize for production—even when the AI is producing at scale, and the organization needs them to optimize for oversight quality. This module gives executives the framework to design incentive structures that make orchestration the rewarded behavior—aligning employee performance measurement with the organizational value that only humans provide in a human-agent hybrid model.

Systemic Resilience: Appraise Frontier Risks

Frontier risks in agentic AI are distinct from the deployment risks executives have been managing in Assisted AI environments. They operate at the system level rather than the use-case level, affecting not just the specific workflow where AI is deployed but also the interconnected organizational and supply chain structures in which the AI is embedded. This module provides executives with an appraisal framework to evaluate three frontier risk categories—agentic drift, deep-tier supply chain vulnerabilities, and cross-organizational agent interaction risks—and determine the governance architecture required for each.

Systemic Resilience: Formulate the 3–5 Year Strategic Roadmap

A 3–5-year AI strategic roadmap is not a technology deployment schedule—it is an organizational transformation architecture that sequences capability development, governance investment, workforce transition, and frontier risk management to enable the organization to move aggressively without exceeding its structural integrity. This module provides executives with the roadmap formulation framework that integrates the full program's strategic, governance, workforce, and risk dimensions into a single forward-looking document.

Project Module: The Enterprise Transformation Blueprint

Learners receive a provided Enterprise AI Transformation Blueprint submitted by a fictional strategy team and produce an Executive Review and Authorization Memo. The blueprint is realistic but contains specific gaps that reflect the most common strategic planning failures in long-horizon AI transformation documents — agentic workflow architectures that are ambition-driven rather than governance-grounded, procurement strategies that do not address interoperability or data ownership, workforce design sections that redesign roles without redesigning incentive structures, and a 3–5 year roadmap that front-loads capability deployment without sequencing the governance and frontier risk architecture that must precede it. Learners identify what is well-constructed, what is incomplete or misaligned, what specific changes must be made, and deliver a final authorization decision.