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Playbooks by Scenario

This page provides lightweight scenario playbooks for applying the CDS Software Delivery Profile. Each playbook describes:

  • when to use it
  • what to emphasize in Meaning Discovery, Intent Refinement, and Commitment Formalization
  • the minimum outputs to produce
  • common failure signals to watch for

These are not “full methodologies.” They are repeatable patterns for shaping commitments that survive software delivery reality.

RFI/RFP initiation

Use when

  • a client requests a proposal, estimate, or plan
  • scope is unclear or solution is assumed
  • procurement expects fixed language early

Emphasize

Meaning Discovery

  • client-side stakes and decision rights (who truly owns outcomes)
  • constraint discovery early (security, compliance, data residency)
  • dependency landscape (access, environments, internal teams)

Intent Refinement

  • outcome and success signals (avoid feature-only proposals)
  • explicit assumptions (what the proposal depends on)
  • feasibility probes (what must be validated post-award)
  • tradeoffs (speed vs certainty, fixed scope vs learning)

Commitment Formalization

  • commitment envelope “procurement mode” (translate into SoW/RFP terms)
  • change protocol (how discovery changes scope without conflict)
  • dependency obligations (client access, SMEs, approvals)

Minimum outputs

  • Meaning Handshake (v0–v1)
  • Intent Package with explicit assumptions + probes
  • Commitment Envelope with change protocol + dependency obligations

Failure signals

  • client cannot name decision owner or acceptance owner
  • constraints are “unknown until after award”
  • proposal is forced into fixed scope without an explicit uncertainty strategy

Legacy modernization / migration

Use when

  • “modernization” is the headline but meaning is unclear
  • there is production risk and hidden constraints
  • dependencies and legacy knowledge are fragmented

Emphasize

Meaning Discovery

  • operational pain and failure modes (incidents, costs, stability)
  • domain semantics and “why the system exists”
  • constraint ambush prevention (security, data, compliance)

Intent Refinement

  • reversibility classification (what becomes irreversible)
  • feasibility probes (data profiling, migration rehearsal, cutover simulation)
  • explicit “do not regress” needs (availability, support load, compliance)
  • success signals that reflect reality (lead time, change failure rate, incident rate, cost)

Commitment Formalization

  • release/cutover posture (phased, canary, parallel run)
  • operational ownership (who runs it, support model)
  • change protocol tuned for uncertainty (learning gates)

Minimum outputs

  • Meaning Handshake with operational signals + dependency landscape
  • Intent Package with NFRs + reversibility + probes
  • Commitment Envelope with cutover stance + revisit triggers

Failure signals

  • modernization defined as tech replacement with no operational outcome
  • no plan for data realities and cutover risk
  • operations/support absent from commitment

New product build (discovery → build)

Use when

  • a new product or major capability is being created
  • value assumptions are high and evidence is needed early
  • scope is likely to evolve based on learning

Emphasize

Meaning Discovery

  • user/customer needs and progress (JTBD lens is useful here)
  • stakeholder stakes (who funds, who benefits, who can block)
  • evidence baseline (what signals exist today)

Intent Refinement

  • define success signals early (activation, retention, task success)
  • learning plan as first-class (experiments, prototypes, user tests)
  • scope boundaries that protect learning (avoid premature full build)
  • tradeoffs: speed-to-learn vs completeness

Commitment Formalization

  • commitments framed as learning milestones + value evidence cadence
  • change protocol that supports iteration without political escalation
  • acceptance based on evidence, not feature completeness

Minimum outputs

  • Meaning Handshake with user needs + stakeholder stakes
  • Intent Package with clear signals + learning plan
  • Commitment Envelope with evidence cadence + decision forum

Failure signals

  • pressure to commit to full scope before learning
  • “MVP” defined as a small scope, but with no evidence plan
  • acceptance defined as “features delivered”

Team augmentation engagement

Use when

  • client requests people/capacity rather than outcomes
  • client leadership may be weak or fragmented
  • delivery depends on client-side access, priorities, and decisions

Emphasize

Meaning Discovery

  • client intent clarity: what outcomes they actually need (even if they say “resources”)
  • dependency reality: access, environments, onboarding, approvals
  • decision vacuum detection: who prioritizes and accepts work

Intent Refinement

  • boundaries: what augmented team owns vs does not own
  • success signals: flow metrics + client satisfaction + value evidence
  • constraints: client process constraints (ticket queues, CAB, security gates)
  • tradeoffs: utilization vs effectiveness, speed vs coordination

Commitment Formalization

  • engagement contract (cadence, escalation, how priorities are set)
  • explicit dependency obligations (client must provide access, decisions, SMEs)
  • definition of “ready” for work intake (to prevent idle time and churn)

Minimum outputs

  • Meaning Handshake focused on leadership, access, and bottlenecks
  • Intent Package with boundaries + decision rights
  • Commitment Envelope with engagement contract + intake readiness rules

Failure signals

  • “We’ll give you tasks” without decision forum or backlog ownership
  • access/onboarding lead times are unknown
  • team becomes idle due to client bottlenecks

Rescue / reset engagement

Use when

  • execution has started but outcomes are unclear
  • scope is thrashing and trust is low
  • hidden constraints and dependencies have already surfaced painfully

Emphasize

Meaning Discovery

  • reality reset: what is actually true right now
  • stakeholder alignment: what each party believes is happening
  • evidence audit: what signals show drift (value drift, delivery drift, engagement drift)

Intent Refinement

  • shrink to decision-grade intent: clarify outcomes, boundaries, constraints, tradeoffs
  • identify what assumptions were false
  • define a learning plan to unblock confidence quickly
  • re-establish decision rights

Commitment Formalization

  • renegotiate commitment envelope (especially change protocol and governance)
  • define immediate stabilization commitments (stop-the-bleeding)
  • explicit revisit triggers and escalation paths

Minimum outputs

  • Meaning Handshake updated from current reality
  • Intent Package that narrows scope and redefines evidence
  • New Commitment Envelope (renewed commitment, not a patch)

Failure signals

  • “Just execute harder” without re-entering meaning/intent
  • blame cycles prevent reality statement
  • governance exists but nobody can decide

How to pick the right playbook

Choose the playbook based on the dominant risk:

  • Procurement compression risk → RFI/RFP initiation
  • Production/legacy risk → modernization/migration
  • Value uncertainty risk → new product build
  • Decision vacuum risk → team augmentation
  • Trust + drift risk → rescue/reset

Re-entry reminder

If a scenario gets stuck, don’t “push through.” Re-enter the correct stage:

  • meaning conflict or missing stakeholders → Meaning Discovery
  • unclear signals/boundaries/tradeoffs → Intent Refinement
  • unclear accountability/change control → Commitment Formalization