A focused glossary for buyers, operators, and technical teams evaluating workflow automation, AI-enabled execution, and decision support.
Many AI and automation conversations break down because different teams use the same words to mean different things. This glossary establishes how Enacment uses key terms so proposals, discovery work, operating models, and delivery decisions stay aligned.
Definitions are written to be commercially useful, not academic.
Each term is framed around execution, accountability, and business outcomes.
AI is treated as one capability layer inside a broader workflow system.
Start with the execution layer, then move into AI operating models and intelligence capabilities.
These terms define the operational layer that structures work before AI is introduced as a capability.
A workflow system is the end-to-end structure that governs how work enters, moves, gets approved, and gets completed across people, rules, and software.
Workflow automation is the use of software logic to execute repeatable steps inside a workflow, such as routing, notifications, validations, updates, or escalations.
These terms describe different levels of AI participation inside work, from guided support to bounded action.
An AI assistant helps a user generate, summarize, classify, or retrieve information in response to a prompt or request.
A copilot is an AI capability embedded inside an existing workflow or tool that provides context-aware recommendations while a human continues driving the task.
An AI agent is a software component that can interpret goals, use tools, take bounded actions, and adapt across multiple steps within defined operational constraints.
These terms describe the analysis, document understanding, and control layers that make execution more reliable and more useful.
Document intelligence converts unstructured files such as PDFs, invoices, forms, contracts, or reports into usable data, classifications, and workflow-ready context.
AI governance is the policy, oversight, monitoring, and accountability framework that controls how AI systems are approved, used, measured, and corrected.
Location intelligence uses geographic, operational, and contextual data to improve decisions related to territory design, routing, site selection, coverage, and service execution.
Decision intelligence combines data, models, business rules, and human judgment to improve how recurring decisions are made, explained, and measured.
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