Single source of definitions
Formal definitions paired with plain-language translations. The vocabulary should make work easier to discuss, not harder to enter.
A practical discipline for increasing the usable capacity of a work system by reducing design debt, strengthening meaning infrastructure, and measuring whether capacity was truly released.
Plain language: A way to help organizations get more useful work out of the system without simply asking people to work harder.
The usable capacity of a work system to produce valuable outcomes at acceptable levels of cost, speed, quality, trust, resilience, and learning.
Plain language: How much useful work the organization can actually produce, not just how many people or tools it has.
The theoretical capacity represented by people, hours, budget, systems, tools, or technology.
Plain language: What looks available on paper.
The portion of available capacity that can actually convert into valuable outcomes after accounting for friction, rework, ambiguity, delay, and trust issues.
Plain language: What survives contact with reality.
The loss of productive capacity through rework, delays, escalations, manual reconciliation, unclear ownership, mistrusted data, duplicated effort, or poorly designed workflows.
Plain language: Where effort disappears without producing enough value.
The measurable improvement in usable capacity after design debt is reduced.
Plain language: The capacity you get back when the system works better.
The accumulated drag created by past decisions, non-decisions, workarounds, structures, systems, incentives, handoffs, approvals, and habits that now make work slower, harder, more expensive, less trusted, or less adaptable.
Plain language: The hidden cost of how the organization has been allowed to operate over time.
The recurring effort spent compensating for bad design: chasing approvals, reconciling reports, explaining exceptions, correcting errors, escalating routine issues, maintaining shadow trackers, or sitting in meetings because ownership is unclear.
Plain language: The tax people pay every day because the work system is harder than it needs to be.
A practical map of how work actually moves through people, systems, decisions, handoffs, data, approvals, exceptions, and informal workarounds.
Plain language: A map of the real work, not the ideal process.
A structured inventory of the organizational design debt affecting a workflow or operating area.
Plain language: A backlog of the drag we need to reduce.
The governed layer of shared meaning that defines business terms, metrics, data sources, metadata, lineage, permissions, validation rules, approved AI skills, and trusted context.
Plain language: The system that makes sure humans, dashboards, workflows, and AI mean the same thing when they use the same words.
The drag caused by unclear, inconsistent, duplicated, or ungoverned definitions, metrics, rules, labels, data relationships, or business meanings.
Plain language: When the organization cannot agree on what its own words and numbers mean.
The design gaps that make a workflow unsafe, unreliable, or low-value for AI: unclear ownership, weak data quality, missing validation, poor documentation, lack of human review, ambiguous policy, inconsistent definitions.
Plain language: The reasons AI would make the mess faster instead of making the work better.
A decision screen that determines whether AI should assist, summarize, route, recommend, automate, monitor, or stay out of a workflow.
Plain language: A way to decide whether AI belongs in this work yet.
A designed point where a qualified human reviews, challenges, approves, or overrides an AI-generated output.
Plain language: Where human judgment stays meaningfully in the work.
An assessment of whether a capacity or AI intervention preserves human dignity, trust, agency, fairness, accountability, and transparency.
Plain language: A check on whether the change improves work for people or simply extracts more from them.
The percentage of effort that converts into useful outcomes after accounting for friction, rework, delay, ambiguity, trust gaps, and design debt.
Plain language: How much value comes out compared with how much effort goes in.
The process by which people, tools, data, decisions, systems, and effort become valuable outcomes.
Plain language: How effort becomes results.
The mechanisms that allow people to believe, use, challenge, and improve the outputs of systems, data, processes, and AI.
Plain language: The reasons people trust the system enough to use it.
The invisible labor managers perform to compensate for unclear roles, broken processes, fragmented systems, poor communication, or weak decision rights.
Plain language: The extra coordination managers do because the system is not designed well enough.
An unofficial workaround used because the formal process is too slow, confusing, incomplete, or mistrusted.
Plain language: The spreadsheet, side chat, or backchannel people use because the official way does not work.
The full set of people, roles, tools, data, decisions, processes, incentives, relationships, norms, and feedback loops that produce an outcome.
Plain language: The whole collection of things that make work happen.