Step 1: Understanding AI Fundamentals for Scrum
AI is not magic. It is pattern recognition applied at scale.
Core Concepts Every Scrum Team Should Know
1. Large Language Models (LLMs)
Systems like ChatGPT generate responses by predicting likely word sequences based on training data.
They do not “understand” intent the way humans do.
Implication: Output must be reviewed and validated.
2. Deterministic vs. Probabilistic Systems
Traditional software produces predictable outputs from defined logic.
AI systems produce statistically likely outputs.
Implication: AI suggestions are options, not commitments.
3. Hallucination Risk
AI may produce confident but incorrect answers.
Implication: Never treat AI output as authoritative without verification.
4. Prompt Sensitivity
Small changes in prompts can significantly alter output quality.
Implication: Teams must treat prompting as a skill.
5. Human Accountability
AI can assist.
The Scrum Team remains accountable for the Increment.
AI does not own quality.
Developers do.
Why This Matters in Scrum
Scrum is built on empiricism: transparency, inspection, and adaptation.
AI fits well inside that loop—if treated as:
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A collaborator
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A generator of options
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A speed amplifier
Not as a decision-maker.
Exercise
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As a team, define AI in one sentence.
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List three risks of using AI in your workflow.
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Identify one area in your current Sprint where AI could assist—but not replace—human judgment.
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Agree on one validation rule for AI-generated output.
Clarity first.
Tools second.