Running AI as an Enterprise Capability

Microsoft via Coursera

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Overview

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Running AI as an Enterprise Capability focuses on sustaining AI initiatives beyond initial deployment. As organizations expand artificial intelligence across business operations, long-term success depends on governance, risk oversight, value measurement, and structured change management. This course explores responsible AI frameworks, security and compliance considerations, and enterprise risk management practices. You’ll examine how to communicate AI strategy clearly to stakeholders, support organizational adoption, and align initiatives with defined business priorities. You will also evaluate AI investments using business value metrics and structured portfolio frameworks to support informed decision-making within established strategic direction. By the end of this course, you’ll be able to contribute to sustainable AI governance programs, support compliance and risk oversight, and coordinate AI initiatives in ways that balance innovation with accountability in enterprise environments. Who this is for: This course is designed for senior project managers, program managers, and emerging AI leaders who communicate AI progress to executives and guide organizations through AI-driven change. It's ideal for professionals who present to leadership teams, facilitate stakeholder alignment conversations, and ensure that AI initiatives succeed not just technically but organizationally. Learners should have completed foundational courses covering AI strategy, technical delivery, and project execution.

Syllabus

Communicating AI strategy to executives

This module builds your ability to communicate AI strategy to executive audiences in ways that support clear decision-making. You'll learn how AI communication differs from traditional technology updates, how to structure strategy briefings around outcomes and decisions rather than technical details, and how to facilitate discussions involving competing priorities and differing risk tolerance. By the end of this module, you'll be able to deliver AI strategy updates that help executives understand what matters, translate technical risks into business terms, and guide stakeholders toward aligned decisions.

Managing organizational change for AI adoption

This module develops your ability to lead organizational change when AI solutions alter how work gets done. You'll learn why AI initiatives often fail at the adoption stage rather than the technical stage, how to assess organizational readiness and identify who will be affected, and how to plan communication and support strategies that address resistance before it derails progress. By the end of this module, you'll be able to develop change strategies that match the scale and risk of your AI initiatives, communicate role and process changes clearly, and guide teams through the human side of AI transformation.

Managing ethical risk in AI projects

This module builds your ability to identify and manage ethical risks in AI projects with the rigor that stakeholders and regulators expect. You'll learn where ethical review fits into real AI project lifecycles, how to evaluate use cases against responsible AI principles, and how to document risks and mitigation actions in ways that create accountability and support audit. By the end of this module, you'll be able to conduct ethical reviews that surface meaningful risks, make defensible decisions about whether use cases should proceed, and communicate ethical considerations clearly to stakeholders.

Validating AI security and compliance requirements

This module develops your ability to validate AI solutions against security and compliance requirements with the thoroughness that enterprise environments demand. You'll learn when security, privacy, and compliance checks must occur across AI project lifecycles, how to review access controls and audit mechanisms at a decision level, and how to turn compliance findings into actionable remediation plans. By the end of this module, you'll be able to identify security and compliance gaps, coordinate remediation with technical teams, and ensure that AI systems operate within organizational and regulatory boundaries.

Choosing and managing AI vendors and partners

This module builds your ability to evaluate AI vendors and partners with the rigor that enterprise investments require. You'll learn how to assess vendors across technical capability, security posture, financial stability, and strategic alignment, understanding where compromises are acceptable and where risks must be mitigated contractually. By the end of this module, you'll be able to participate confidently in vendor selection discussions, synthesize technical and commercial input into clear recommendations, and document rationale in ways that support accountability and future review.

Deciding which AI projects to fund and how to measure success

This module develops your ability to make and defend AI investment decisions at the portfolio level. You'll learn how to evaluate and prioritize AI initiatives across the organization by balancing strategic fit, risk exposure, resource constraints, and expected value—making trade-offs explicit and defensible. You'll also learn to define KPIs and ROI models that connect AI outcomes to business metrics and track performance over time. By the end of this module, you'll be able to recommend portfolio priorities, measure AI value in terms executives understand, and adjust investments based on evidence rather than assumptions.

Building your AI leadership brand

This module builds your ability to describe your AI experience clearly and credibly to hiring managers and stakeholders. You'll learn where AI-related roles are growing inside enterprises, what hiring managers actually look for in candidates who have contributed to AI initiatives, and how to translate your project experience into narratives that demonstrate judgment, collaboration, and readiness for increased responsibility. By the end of this module, you'll be able to articulate your AI contributions authentically, identify gaps between your current experience and leadership expectations, and develop a career strategy focused on practical growth.

Preparing for AI leadership opportunities

This module develops your ability to prepare for AI leadership opportunities through effective application materials and interview performance. You'll learn how to write resume content that reflects contribution and impact without overstating authority, how to prepare for common interview questions in AI project roles, and how to demonstrate judgment and accountability when discussing your experience. By the end of this module, you'll be able to create credible application materials, respond confidently to AI leadership interview questions, and present yourself as a professional ready for increased responsibility.

Project: Leading an AI initiative with enterprise impact

In this project, learners act as an AI initiative lead responsible for analyzing a realistic AI use case, coordinating inputs, surfacing risks, and forming a clear recommendation. The focus is on decision logic, trade-offs, stakeholder communication, and change implications—not designing enterprise strategy, governance models, or long-term AI roadmaps.