Actionable ≠ Automation-ready
Die Grenze zwischen umsetzbaren und automatisierbaren Anforderungen bestimmt, wo menschliches Urteil enden darf und maschinelle Ausführung beginnen kann.
#requirements
#automation
#ai
Actionable Requirements Clarity Sprint
A timeboxed, LLM-augmented clarity sprint that converges to "who needs what, when".
What are Actionable Requirements?
Actionable Requirements are requirements that pass three quality gates: decidable (decisions are explicit), consistent (no contradictions), and executable (teams can build and test without guessing).
— MaZ
Actionable means: decidable, consistent, executable — so delivery (or sourcing) can start without rework loops.
- • Decisions become explicit (what's open, who decides, by when)
- • Contradictions and gaps get resolved or clearly flagged
- • "Who needs what, when" becomes testable (constraints + acceptance)
Actionable ≠ "LLM-generated specs" and ≠ "automation-ready by default". Human-owned, LLM-augmented.
How I work
I run clarity sprints for teams and freelancers who need to converge on "who needs what, when" before building or sourcing.
Human-owned, LLM-augmented: I use LLMs for synthesis and consistency checks—but every decision, every trade-off, stays with humans.
No "AI-generated specs". No automation promises. Just structured dialogue until requirements pass the actionable gate.
— MaZ
Everyone talks AI and speed — few talk input quality.
- → Meetings create opinions, not decisions.
- → Demos hide contradictions until it gets expensive.
- → Vendors estimate into fog, then rework follows.
- → AI initiatives fail on unstable, ambiguous input.
Actionable = Decidable + Consistent + Executable
A quality gate for requirements — not a methodology, not a tool.
Decidable
Decisions are explicit — not buried in chat or slides.
- ✓ Open points are recorded with owner + deadline
- ✓ Trade-offs are visible (speed vs. control, cost vs. risk)
Decision: "auto-approve?" Owner: PO. Due: Fri. Consistent
Contradictions are resolved or clearly flagged.
- ✓ Terms and roles use a shared, minimal language
- ✓ Conflicts are mapped with options, not ignored
Conflict: "fully automatic" vs "manual release required". Executable
Teams can plan, build, and test without guessing.
- ✓ Actor / trigger / outcome is clear ("who needs what, when")
- ✓ Acceptance + constraints are testable and bounded
Given X, when Y, then Z; constraints: latency ≤ 200ms. A timeboxed clarity sprint
We collect inputs, surface contradictions, turn assumptions into decisions, and converge to "who needs what, when" with constraints and testable acceptance—so delivery can start. LLMs help with synthesis and consistency checks; ownership stays with humans.
Capture → Cluster → Contradictions → Decisions → Synthesis
Human-owned, LLM-augmented.
Clarity Sprint Deliverables
What you get at the end: compact, traceable, ready for execution or handover.
Decision Log
What's open, who decides, by when — plus options and trade-offs.
Reduces "we assumed…" surprises during delivery.
Decision D-07: approve flow; owner: Lead; due: 2026-02-01 Contradiction Map
Conflicts, their impact, and paths to resolution.
Prevents late-stage rework and stakeholder churn.
C-03: security vs friction — option A/B with risks Requirement Brief
A compact, execution-focused brief (goal, scope, non-scope, acceptance).
Good enough to plan and build; small enough to read.
Goal, Users, Triggers, Constraints, Acceptance Glossary Light
A minimal shared language for roles, objects, and key terms.
Stops teams from "agreeing while meaning different things."
"Account", "User", "Session", "Approval" — definitions Notes
Reflexionen zu strukturierten Anforderungen, Eingabequalität und der Schnittstelle zwischen menschlichem Urteil und maschineller Ausführung.
Decision Logs schlagen Dokumentation
Klassische Dokumentation beschreibt Zustände. Decision Logs erklären, warum diese Zustände existieren. Das ist der Unterschied zwischen Information und Wissen.
#documentation
#decisions
#knowledge-management
Warum KI ohne Eingabequalität scheitert
Wir investieren massiv in Modell-Fähigkeiten, aber kaum in die Qualität dessen, was wir ihnen geben. Das ist ein Fehler.
#ai
#input-quality
#requirements
If you’re dealing with…
- Outsourcing: unclear scope and endless clarification loops
- AI initiatives: no reliable input to feed into workflows
- Platform work: too many voices, too little convergence
- Complex systems: contradictions show up late in delivery
- Regulated contexts: decisions and rationales must be traceable