The market data is where you practise it.
Scrum was designed for problems where the requirements aren't fully known upfront — where you learn by doing, deliver in short cycles, and improve based on what you find. That is exactly the environment AI tools operate in today.
AI handles the repeatable execution — fetching data, running analysis, calling models, logging outputs. Scrum handles the continuous improvement — sprint goals, daily cadence, retrospectives, Definition of Done. Neither is sufficient without the other.
A team that uses AI without Scrum produces good outputs inconsistently. A team that uses Scrum without AI works hard but slowly. A team that uses both — with a disciplined retrospective each sprint — builds a compounding advantage. Each sprint the automation gets slightly better, the process gets slightly tighter, and the output gets slightly more accurate. Over eight sprints, that compounds into something significant.
By Sprint 6, the best teams have a GitHub Actions workflow that fires automatically after the US market closes on Friday (Saturday morning SGT), collects all instrument data, runs agent scripts, calls LLMs via API, and commits the output — without anyone touching a keyboard. That didn't happen because they used smarter AI. It happened because every retrospective produced one concrete improvement to the automation. Scrum compounded what AI started.
These are the principles your team should be improving on every sprint. Each one has a project-specific meaning.
| Scrum Event | In This Project | AI Angle |
|---|---|---|
| Sprint Planning Monday · in class |
R1 writes sprint goal. R2 assigns roles. DoD checklist written. DECISION.md committed. | The sprint goal defines what the AI pipeline must produce by Sunday. Writing it forces clarity about what "automated" actually means for this team this week. |
| Daily Scrum 15 min · every day |
Three questions. Blockers surfaced immediately. R2 chases by Wednesday. | Pipeline failures, API key issues, GitHub Actions errors — these are Wednesday blockers, not Sunday surprises. The daily standup is what catches them early. |
| Sprint Execution Mon–Sat |
Agents commit independently (R3–R6). LLM API calls run (R8). Automated fetch fires Saturday morning SGT after Friday US market close (R9). | The best teams have AI doing the execution. Humans do the judgment — Wild Card (R7), calibration (R10), pipeline monitoring (R9). The sprint cadence keeps this ordered. |
| Sprint Review Monday presentation |
Every role has a speaking point. Show GitHub Actions log live. Demo the pipeline running — or explain the failure honestly. | The Sprint Review is where the AI system is evaluated against reality. Did the automated fetch run? Did the LLM comparison table generate correctly? Did the prediction reflect what the agents produced? |
| Retrospective After Sprint Review |
Separate from Sprint Review. Every member speaks. One concrete change for next sprint. Committed to repo. | The retrospective question for an AI team is specific: did the automation run without failure? Did the output improve? What checklist item prevents the failure next sprint? These are engineering questions — and they compound. |
Every role in this project practises a real skill. The Scrum framework is what makes those individual skills add up to a functioning system.
One per class session. These are the ideas that compound.