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Leading in the Age of AI · Course 02 of 03

Decision-Making When Machines Are in the Room

When to trust algorithmic outputs, when to apply human judgment, and how to remain accountable for outcomes in an AI-assisted decision environment.

6Modules
Self-pacedFormat
CertificateOn completion
About This Course

Who is responsible when a machine informs the decision?

Algorithms can detect patterns at a scale no individual can match, but they do not remove the need for judgment. They alter where judgment is required: in framing the problem, evaluating the data, interpreting confidence, recognizing exceptions, and accepting responsibility for consequences.

“The presence of an algorithm changes the decision process; it does not transfer accountability away from the leader.”

We cover when algorithmic outputs are reliable and when they aren't, a practical framework for deciding whether to trust or override a recommendation, and how to maintain clear accountability when a decision was AI-assisted rather than fully human. As the middle course in Leading in the Age of AI, it sits between the organizational mapping of Course 01 and the ethics-focused Course 03.

What you'll be able to do
Identify when algorithmic outputs are reliable and when they aren't
Apply a framework for deciding when to trust vs. override AI
Maintain clear accountability for AI-assisted decisions
Recognize and correct your own biases toward or against AI
Communicate AI-assisted decisions to stakeholders with transparency
Design decision processes that use both human and AI judgment well
Course Modules
01The New Decision Environment
How AI changes the inputs available to decision-makers
The risk of outsourcing judgment to algorithms
What human decision-making must now do that it didn't before
02When to Trust Algorithmic Output
The conditions under which AI outputs are reliable
How to interrogate a model's recommendation
What the algorithm doesn't know that you do
03When to Override
The legitimate cases for human override
How to override without simply ignoring the data
Documenting your reasoning when you override
04Accountability in AI-Assisted Decisions
Who is responsible for an AI-assisted outcome
How to communicate accountability to stakeholders
When an AI error becomes a leadership problem
05Cognitive Biases in AI-Assisted Decision-Making
Automation bias: over-trusting the machine
Algorithm aversion: under-trusting the machine
How to calibrate your relationship with algorithmic output
06Building Better Decision Processes
Designing decision processes that use AI and humans well
When to involve more people vs. fewer
How to get faster without getting sloppier
Questions about this course
Does this course teach me how AI models actually work?
No — it's deliberately non-technical, focused on the leadership decision of when to trust, override, or scrutinize an output, not on the mechanics of how the model produces it.
What if my organization doesn't use AI in decision-making yet?
The frameworks are built to apply as soon as any algorithmic input enters a decision process — from simple scoring tools to more sophisticated models — so the course is useful preparation even ahead of wider AI adoption.
How does this connect to the other courses in the path?
This is Course 02 of 03 in Leading in the Age of AI, following the organizational map in Course 01 and preceding the ethics and accountability focus of Course 03. It's the practical middle course between structure and principle.
What do I receive when I complete the course?
A Udemy certificate of completion, shareable on LinkedIn and downloadable as a PDF, plus lifetime access to the course and future updates.

Ready to start?

Self-paced, on your schedule, on Udemy.

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