Module 7 Lesson 1: Agile AI Projects

Standard software development follows a "Feature Backlog." You build the login page, then the profile page, then the checkout. AI is different. You are dealing with Probabilistic Outcomes. You don't know if a model will work until you try it with real data.

1. Why "Waterfall" Fails for AI

In a "Waterfall" project, you define everything upfront.

The Problem: In AI, you might find out in Month 3 that your data is too messy to achieve the required accuracy.
The Result: If you used Waterfall, you've just wasted 3 months of budget on a literal impossibility.

2. Iterative Loops: The "Research-to-Product" Cycle

An Agile AI project should look like a series of Sprints focused on Reducing Uncertainty.

Discovery Sprint: Can we even access the data? Is it clean enough?
Feasibility Sprint: Can a basic model get better than 50% accuracy?
Optimization Sprint: How do we get from 70% to 90%?
Integration Sprint: How do we connect this to the actual app UI?

3. The "AI Product Owner" Role

In an AI team, the Product Owner (PO) needs a new set of skills. They must manage expectations around Probability.

Don't Ask: "When will this feature be done?"
Do Ask: "What is our current confidence that this model meets our safety threshold?"
The Bridge: The PO translates the engineer's "We have a 0.82 F1 score" into the business's "The bot is ready for a limited pilot."

4. Adapting the "Definition of Done"

In traditional Agile, "Done" means code is written, tested, and merged. In AI Agile, "Done" includes:

Validation: The model was tested against a "Holdout" dataset.
Safety: The model passed our internal "Bias and Hallucination" checks.
Observability: We have a dashboard to watch the model's performance in production.

Exercise: The Agile Pivot

Scenario: You are 2 weeks into a 4-week sprint to build an "AI Document Summarizer" for the Legal team. You discover that the documents are scans of old papers that the AI cannot read clearly.

The Decision: Do you keep trying to fix the AI, or do you change the project goal?
The Communication: How do you explain to the Legal team (the stakeholders) that the project is delayed because of "Data Quality" without sounding like you've failed?
The Pivot: What is a "Smaller Win" you could achieve in the remaining 2 weeks? (e.g., "Summarizing only the modern digital docs").

Summary

Agile AI is about Learning over Building. By structured your project into small, data-driven experiments, you reduce "Project Risk" and ensure that you only spend big budgets on AI solutions that have already proven they can work.

Next Lesson: We look at the "Human Engine": Building Cross-functional AI teams.