AI Strategy and Transformation Planning

Microsoft via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

9 weeks, 1 hour a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

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Overview

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This course establishes the strategic foundation for enterprise AI transformation by translating emerging AI capabilities into a structured, value-driven roadmap. You'll assess your organizational readiness across data, policy, and talent to ensure your strategy is built on a viable foundation. The course covers how to sequence AI initiatives based on business impact and technical feasibility, with a focus on the Microsoft ecosystem's infrastructure requirements. You'll design transformation plans that account for the total cost of ownership, including the human verification cycles required for accurate AI outputs. By the end of this course, you'll be able to synthesize your North Star Vision, phased transformation roadmap, and TCO model into a single board-ready Strategic AI Investment Case — one that defends the balance between innovation speed and architectural integrity with quantitative evidence, governance-grounded recommendations, and a clear authorization narrative designed for board-level review.

Syllabus

Before You Fund AI: Assess Readiness and Risk

This module helps leaders evaluate whether an AI initiative is ready to move forward before committing resources. Rather than focusing on what AI could do, the emphasis is on what the organization can realistically support today. You'll learn how to assess key readiness factors, including data maturity, security considerations, and operational capability, and how these influence risk at scale. The goal is to ensure that any AI vision you approve is grounded in what the organization can execute, not just what it aspires to achieve. By the end of this module, you'll be able to make more informed, defensible decisions about when to move forward with AI, and when additional preparation is needed.

Define Your AI Strategy: What to Prioritize and Why

With readiness established, this module focuses on defining a clear and actionable AI strategy. The goal is not to create an aspirational vision, but to set a direction that the organization can execute with confidence. You'll learn how to translate market opportunities and internal capabilities into a focused strategy that defines where to invest, what outcomes to prioritize, and where to set boundaries. This includes aligning ambition with operational reality and ensuring that expectations around speed, scale, and risk are clearly understood. By the end of this module, you'll be able to define an AI direction that guides decision-making across teams, supports consistent execution, and provides a clear basis for evaluating progress and impact.

AI Roadmap: Sequence Initiatives by Impact and Feasibility

Sequencing is one of the most consequential and least discussed executive decisions in AI transformation. This module gives leaders the evaluation framework to move from a list of AI opportunities to a defensible, sequenced portfolio, making the "invest vs. delay" and "scale vs. pilot" calls based on data rather than competitive pressure or internal advocacy.

AI Roadmap: Draft the Foundational Readiness Roadmap

The sequenced initiative portfolio tells you what to do and in what order. The foundational readiness roadmap tells you what must be true before any of it can safely begin. This module guides executives through constructing the prerequisite architecture of the transformation: the data hygiene, security, and governance work that determines whether the roadmap will hold or collapse on first contact with deployment reality.

Change Management: Map the Creation-to-Curation Transition

This module gives executives the analytical tools to diagnose how AI fundamentally reshapes work, shifting human effort from generating outputs to curating, verifying, and governing AI outputs. The core risk executives face is not that employees will refuse to use AI. It is that they will use it without a redesigned verification layer, producing the "productivity paradox" where AI investment increases rework rather than reducing it. Learners apply a structured process mapping framework to identify where value creation shifts and where new human accountability requirements emerge.

Change Management: Design the Trust-Building Communication Strategy

The process map tells you what will change. The communication strategy determines whether the organization accepts the change or resists it. This module guides executives through designing a communication strategy that addresses the three sources of AI adoption resistance—fear of displacement, distrust of AI outputs, and perception of top-down imposition—with transparency, evidence, and genuine two-way engagement. The strategic risk is not that employees will refuse to use the tools. It is that they will use them without the verification discipline the workflow requires—because the communication strategy never made clear why their judgment is more important, not less, in the new model.

Strategic Resource Allocation: Allocate Capital and Talent Across the Roadmap

Resource allocation for AI transformation is one of the highest-stakes executive decisions in the program and one of the most commonly distorted by optimism bias, political pressure, and incomplete cost data. This module gives leaders the evaluation framework to assess how capital and talent should be distributed across the roadmap to sustain long-term value, with particular attention to the foundational infrastructure investments that rarely win the internal advocacy competition but consistently determine whether the transformation succeeds.

Strategic Resource Allocation: Model the Total Cost of Ownership

The investment case that wins board approval is rarely the investment case that reflects true costs. This module gives executives the financial modeling discipline to build a TCO model that includes the costs that conventional AI budgets systematically exclude: the "Hidden Tax" of human rework, data remediation, and ongoing verification labor. The result is not a more expensive investment case. It is a more credible one that does not collapse under scrutiny when the costs it excluded begin to appear in operational budgets six months after deployment.

Strategic AI Investment Case

Learners synthesize the strategic, architectural, change management, and financial work from LC 1 into a single unified executive deliverable: a Strategic AI Investment Case designed for board-level review. This project integrates the North Star vision, the foundational readiness roadmap, the transformation roadmap, the TCO model, and the change management framework into a coherent investment proposal that defends the strategic balance between innovation speed and architectural integrity.