Glossary

DesignOps glossary

The vocabulary of design operations, defined plainly: how design organizations plan capacity, govern their design systems, run reviews, and test ideas with synthetic users.

DesignOps (design operations)

The orchestration and optimization of people, processes, and craft to amplify design’s value and impact at scale, in Nielsen Norman Group’s definition. It spans how design teams work together (structure, rituals, hiring, and growth), how they get work done (process, tools, design systems, and prioritization), and how their work creates impact (measuring and sharing design’s value). Design decisions stay with designers and design leaders.

DesignOps agent

An AI agent that prepares design operations work for a human owner to approve: staffing proposals, design-system fixes, tool-adoption follow-ups, and review evidence. Sentient is Sentia’s DesignOps agent. Leaders keep priorities, approvals, and people decisions.

ResearchOps (research operations)

The function that runs the systems around research: study intake, participant recruitment, consent, tooling, and the research repository. Researchers keep ownership of methodology and interpretation.

Design capacity planning

Comparing the design demand of planned initiatives with the designers available to staff them, so coverage gaps are found before delivery slips. The output is usually a staffing or reallocation proposal for the managers who own the commitment.

Design coverage

How much designer time each product team or initiative actually has against what it needs. Coverage often erodes without a decision, for example when one of two designers on an initiative is pulled onto a launch.

Related: Design capacity planning

Design system

The shared components, design tokens, patterns, and guidance that product teams use to build consistent interfaces. A design system is maintained as a product, with its own owners, roadmap, and contribution process.

Design tokens

Named values for design decisions such as color, spacing, and typography, stored once and referenced by both design files and code, so a change to the token updates every place that uses it.

Related: Design system, Design drift

Design system governance

The rules and decision rights for changing a design system: how teams request or contribute components, who approves changes, how departures from the standard are handled, and how deprecated parts are retired.

Design system adoption

The extent to which product teams actually use the shared components and tokens instead of local copies. Adoption is usually tracked per team or per file, and low adoption is a signal to investigate rather than a verdict on the team.

Design drift

Gradual divergence between shipped work and the shared design system, such as detached components, hard-coded values in place of tokens, or near-duplicate components. Drift creates repair work at handoff and inconsistency for users.

Design handoff readiness

Whether a design is complete enough for engineering to build without rework: states, edge cases, accessibility requirements, content, and acceptance criteria are specified, and design and engineering owners agree it is ready.

Related: Design review

Design review

A recurring decision point where a named approver accepts, rejects, or redirects design work. Unlike design critique, which improves the work, a design review should end with a decision and a recorded owner.

Design critique

A structured session where designers give feedback on work in progress against its goals. Critique improves the work; it is not the place where approval decisions are made.

Related: Design review

Design tool adoption

How fully teams use the design and AI tools the organization pays for. Tracking it shows unused licenses that can be reassigned and teams that need onboarding support.

Contribution record

An evidence-based account of what a designer contributed over a period: the projects, decisions, research, and collaboration behind shipped work, with links to the source. Managers use it to prepare performance reviews; the assessment stays with the manager.

Design impact

The measurable effect of design work on product and business outcomes. Credible measurement names a baseline, a target, a measurement source, and an owner, and treats a single change as contribution rather than proof of causation.

Synthetic users

AI agents that simulate members of a target audience so teams can test designs, flows, and messages before recruiting real participants. Their value depends on grounding: agents built from a team’s own interviews and behavioral data are more representative than generic personas.

Synthetic user research

Research that uses synthetic users to answer early product questions, such as where people may struggle in a flow or how segments react to a design. It gives directional evidence and sharper questions for studies with real people; it complements them rather than replacing them.

AI usability testing

Usability testing in which AI agents attempt tasks on a prototype or live website and report where they hesitate, fail, or misread the interface. It screens a flow quickly before a moderated or unmoderated study with real participants.