Glossary

Clear definitions for the terms we use. No jargon without explanation.

Workflow Stability

The condition where work reliably moves from initiation to completion without hidden rework, manual overrides, or ambiguity about ownership. A workflow is stable when ownership is explicit, handoffs are clear, exceptions are anticipated, and sequencing is intentional.

Example: Stability is a prerequisite for optimization, automation, or AI. Without it, improvements amplify existing problems instead of solving them.

Workstream

How real work moves through your organization — the handoffs, decisions, and data that connect one step to the next.

Example: "The client onboarding workstream" includes everything from signed contract to first project kickoff — across sales, ops, and account management.

Source of Truth

The authoritative system where data lives. Not personal spreadsheets or email threads — one place everyone trusts.

Example: When multiple systems have "the answer," no one knows which is right. A source of truth eliminates that confusion.

Operational Stability

Workflows that run reliably without heroism. The system works even when your best people are on vacation.

Example: Opposite: "We need Sarah to handle that because she's the only one who knows how it works."

Automation Readiness

The degree to which a workflow can be automated without increasing risk. A workflow is automation-ready only when decisions are deterministic or well-bounded, ownership is enforced, exception handling is explicit, and failure is visible and recoverable.

Example: Automating an unstable workflow amplifies failure instead of removing it. Most 'automation failures' are actually stability failures.

Workflow Failure Mode

A predictable way a workflow breaks under pressure, scale, or change. Common failure modes include silent exceptions, rework loops, manual overrides, ambiguous approvals, and knowledge-based ownership.

Example: Failure modes are structural, not individual mistakes. If the same type of error keeps happening with different people, it's a failure mode.

Handoff

The moment work moves from one person, team, or system to another. Most workflow problems happen at handoffs.

Example: Common failure: "I thought you were handling that" — unclear ownership at the handoff point.

Ownership Gap

A condition where responsibility for a step exists socially but not structurally. Ownership gaps occur when multiple people assume someone else owns the step, escalation paths are unclear, or accountability exists only after failure.

Example: Sales says ops owns it. Ops says sales owns it. The customer waits. Ownership gaps are one of the primary causes of automation breakdown.

Exception Handling

The explicit definition of what happens when a workflow does not follow the happy path. Effective exception handling includes clear detection, defined ownership, allowed actions, and recovery paths.

Example: Ignoring exceptions is a leading indicator of future failure. In real operations, exceptions are often more common than the 'happy path.'

Human Fallback

A designed mechanism that allows humans to safely intervene when automation or AI encounters uncertainty. Human fallback is not a workaround — it is a required safety layer in any production-grade system.

Example: When the AI isn't 95% confident, it hands off to a human instead of guessing. Critical decisions always require a final human "Ok."

Fragile Automation

Automation that technically functions but depends on manual correction, tribal knowledge, or informal workarounds. Fragile automation often survives until volume, change, or staff turnover exposes its weaknesses.

Example: "We have 47 Zaps and no one knows what half of them do." That's automation debt from fragile automation.

Automation Debt

Accumulated complexity from automations built quickly without proper design. Works today, breaks mysteriously tomorrow.

Example: Every quick fix that doesn't get documented becomes automation debt. Eventually, the fear of breaking things prevents any improvement.

AI Workflow Risk

The risk introduced when probabilistic systems (AI) are applied to workflows that are not structurally stable. AI increases speed, opacity, and the impact of errors. Without stability, AI makes failures harder to detect and recover from.

Example: AI doesn't fix broken workflows — it accelerates whatever structure already exists, including the broken parts.

SOP Drift

When documented procedures no longer match how work actually gets done. The SOP says one thing; the team does another.

Example: Process changes happen, but no one updates the documentation. Eventually, the SOP becomes fiction.

Loud Failure

When automation breaks, it should alert humans immediately — not fail silently and pollute downstream data.

Example: It's better to stop and alert than to proceed with bad data. Silent failure is the enemy of trust.

Tribal Knowledge

Information that exists only in people's heads, never written down. A liability disguised as expertise.

Example: When that person leaves, the knowledge leaves with them. Documentation is the antidote.

Diagnostic vs Optimization Work

Diagnostic work establishes what is real. Optimization work changes how things operate. These are different activities with different purposes.

Example: Optimization without diagnosis often improves the wrong thing. That's why we start with assessment before recommending changes.

Workflow Stability Assessment

A fixed-scope diagnostic using audit-style methodology to identify where your workflows are breaking, who owns what, and what to fix first. It is not an audit, and it is not implementation.

Example: Deliverables: Current state workflow map, stability risk assessment, prioritized remediation plan, ownership matrix.

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