Method
Five steps for one real workflow.
The method is a sequence. See the work, name the debt, strengthen meaning, redesign for capacity release, and measure what changed.
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Step 1
See the work
Map the real workflow, not the documented ideal. Identify where work enters, who requests it, who owns it, who decides, who approves, which systems are used, which data is trusted, where handoffs occur, where delays happen, where rework happens, where escalations happen, where people create workarounds, and where AI is already being used informally.
Questions to ask
- Where does work enter?
- Who owns the work?
- Where do delays, rework, escalations, and workarounds appear?
What good looks like: People can point to the same real workflow and name where work actually moves.
Open the tool -
Step 2
Name the debt
Classify the drag. Common categories: Decision debt. Role debt. Process debt. System debt. Data debt. Coordination debt. Incentive debt. Trust debt. Learning debt. AI-readiness debt.
Questions to ask
- What drag repeats?
- Which debt types explain the drag?
- Who pays the debt service each week?
What good looks like: The team can distinguish symptoms from the design debt causing them.
Open the tool -
Step 3
Strengthen meaning infrastructure
Identify whether the organization has governed meaning around the workflow. Ask whether key terms are defined, metrics are consistent, data sources are trusted, lineage is visible, ownership and stewardship are clear, validation rules are documented, permissions are enforced, approved AI skills/actions are defined, and humans can challenge or verify outputs.
Questions to ask
- Are key terms defined?
- Are metrics consistent?
- Can humans challenge or verify outputs?
What good looks like: The workflow has shared definitions, trusted sources, and visible ownership.
Open the tool -
Step 4
Redesign for capacity release
Prioritize interventions that reduce debt and increase usable capacity. Examples: Clarify decision rights. Remove unnecessary approvals. Redesign intake. Consolidate definitions. Improve data quality. Reduce manual reconciliation. Eliminate redundant handoffs. Create human verification loops. Improve manager enablement. Redesign roles around outcomes. Deploy AI only where context and accountability are strong enough.
Questions to ask
- Which intervention reduces the most drag?
- Where is human verification required?
- Where is AI ready, and where should it stay out?
What good looks like: The change improves the work system before it automates the workflow.
Open the tool -
Step 5
Measure capacity released
Measure whether the system improved. Do not stop at launched or adopted. Measure time saved, rework reduced, cycle time reduced, decision latency reduced, escalations reduced, manual corrections reduced, data confidence improved, employee experience improved, manager friction reduced, trust improved, and capacity redeployed to higher-value work.
Questions to ask
- What changed across cost, speed, quality, trust, resilience, and learning?
- What capacity was redeployed?
- What human impact changed?
What good looks like: The team can tell whether capacity was actually released.
Open the tool
Pilot guidance
The first use case should not try to prove everything. It should prove that the lens changes the quality of the conversation.
We thought this was a system issue. The map showed it was actually decision debt, data trust debt, and unclear handoff ownership. Before adding automation, we clarified the process, reduced rework, and created a better foundation for AI support.
Could you explain the method without looking?
See the work. Name the debt. Strengthen the meaning. Redesign for capacity release. Measure whether capacity was released.