COURSE TITLE: Critical Thinking for Strategic Decision Making in the AI Era

COURSE OVERVIEW

Artificial intelligence is reshaping how decisions are made across every industry — accelerating analysis, surfacing patterns, and generating recommendations at a scale no human team can match. But speed and scale are not the same as wisdom. As AI becomes embedded in strategic workflows, the professionals who thrive will not be those who defer to algorithms, but those who know when to trust them, when to question them, and how to think beyond them.

This course equips professionals with the critical thinking frameworks needed to make sound, high-stakes decisions in an environment increasingly shaped by AI tools, AI-generated data, and AI-influenced advice. It is not a course about how AI works technically. It is a course about how you think — and how to think better — when AI is in the room.

Learners will examine the cognitive biases that AI can amplify, the reasoning errors that emerge when humans over-rely on automated outputs, and the structured thinking approaches that distinguish reactive decision-makers from genuinely strategic ones. Real-world scenarios drawn from business, operations, and leadership contexts are used throughout to ground every concept in practice.
This course is designed for managers, analysts, team leaders, and professionals in any sector who are regularly working with AI-assisted tools and need to maintain rigorous, independent judgment in high-stakes environments.

Format: Self-paced online learning D

LEARNING OUTCOMES

By the end of this course, learners will be able to:

1. Explain the core principles of critical thinking and why they are essential for strategic decision-making in AI-assisted environments.
2. Identify the most common cognitive biases that affect human judgment and describe how AI systems can reinforce or mask these biases.
3. Apply structured reasoning frameworks — including argument analysis, assumption testing, and logical evaluation — to assess AI-generated outputs and recommendations.
4. Distinguish between decisions that are appropriate for AI augmentation and those that require independent human judgment as the primary driver.
5. Evaluate the quality and reliability of data, models, and AI outputs using a systematic critical lens rather than defaulting to algorithmic authority.
6. Construct a personal decision-making approach that integrates AI tools responsibly while preserving strategic clarity, ethical grounding, and accountability.