Most AI glossaries define words for people who already understand them. These are the definitions we actually use with clients — written for the person who has to make a decision, not pass a quiz.
AI that runs a process rather than answering a question. A chatbot responds to what you ask. An agentic system decides what to do next based on what just happened, carries out multiple steps, and reports back or hands off when it needs a decision from you.
An AI configured to carry out a multi-step task on its own, including on a schedule, and to report back or hand off when it needs a decision.
An AI configured for repeated work in one domain. It still does one thing: respond. The difference between an assistant and an agent is whether it decides what to do next, or only what to say next.
A saved, reusable instruction set that captures how a specific person or role does a specific job, so the same quality of output is repeatable rather than dependent on how well someone phrases a request that day. A skill built once runs every week after that.
Structuring your organization's information so AI can actually use it. Most AI disappointment is a context problem, not a model problem: the tool is capable, but it cannot reach the data it would need to be useful.
A deliberate checkpoint where a person reviews or approves before an AI-driven process continues. Where these checkpoints belong is a governance decision, not a technical one.
The rules your organization sets for who may use which AI tools, with what data, under what review. Governance is what makes AI use defensible rather than accidental.
AI tools used by employees without organizational approval or oversight, typically because no sanctioned option exists for the work they are trying to do. Shadow AI is a symptom of a missing option, not a discipline problem.
A written statement of acceptable AI use. Necessary and not sufficient: a written policy, even a well-trained one, does not by itself change what people do at a keyboard.
The group inside an organization who go deepest on AI and become the people their colleagues ask first. Chosen for willingness to learn on behalf of their department, not for existing enthusiasm about the technology.
A survey of everyone taking part in an AI program, run before anything is scheduled, measuring which tools are genuinely in use, how often, and how people feel about AI. It produces a written Baseline Report to leadership. How someone feels about AI predicts whether they adopt it better than anything else we measure, and most programs never ask.
SkillSpout's five-stage framework for organizational AI adoption. Stage 1, Foundational AI, builds literacy and safe-use habits. Stage 2, Applied AI, puts the AI already in your tools to work. Stage 3, Embedded AI, has each department build assistants for its own workflows. Stage 4, Agentic AI, moves teams from assistants to AI coworkers. Stage 5, Cultural AI, makes governance and adoption self-sustaining. Organizations move one stage at a time, in order.
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