AI Copilot

What is an AI copilot?

An AI copilot works alongside a person inside a specific tool or workflow. It sees the context of what the user is doing, such as the open email, the customer record, the spreadsheet or the code file, and offers help: a draft reply, a summary of history, an answer pulled from internal documents or a suggested next step. The user accepts, edits or ignores it.

Under the hood, a copilot combines a large language model with access to the relevant context and, often, company knowledge retrieved at the moment of the request. Some can also take small actions on request, like updating a field or creating a task.

Copilots appear in sales (call summaries and follow-up drafts in the CRM), support (suggested answers for agents), finance (explaining variances), operations (reading and comparing documents) and engineering (code suggestions). Alongside the copilots bundled into commercial software, many companies build internal copilots around their own processes and data.

How is an AI copilot different from an AI agent?

The main difference is who drives. A copilot assists a person who remains in charge of the task: it suggests, and the person decides. An AI agent is given a goal and works through multiple steps on its own, choosing tools and actions along the way, with people reviewing at set points or afterwards.

That difference changes the risk profile. Because a copilot's output passes through a person before it takes effect, mistakes are usually caught, which makes copilots a sensible first step for teams new to AI. Agents can handle more volume without supervision but need stronger guardrails and monitoring. Many products blend both: a copilot that, when the user asks, hands a well-defined task to an agent and reports back.

When does a custom AI copilot make sense?

Copilots built into email, office suites or CRMs are a good default for general tasks. A custom copilot is worth considering when the work depends on context those tools cannot see:

• Answers need data from several systems at once, such as CRM, finance and support.
• The task follows company-specific rules, templates or terminology.
• Sensitive data must stay within controlled systems and existing permissions.
• The workflow is frequent and specific enough that small time savings on each use add up.

If none of these apply, a built-in copilot is usually cheaper and faster to adopt. Many teams start there and build a custom one once they know exactly which gaps they need to close.

How do you get people to use an AI copilot?

Adoption depends on fitting the copilot into existing habits. Put it where the work already happens, inside the CRM or ticketing screen rather than in a separate tab, and make its output easy to use with one action: insert, edit or discard. Start with one or two tasks people find tedious and where quality is easy to judge, such as meeting summaries or first-draft replies.

Trust grows when the copilot shows its sources and stays within its competence. A few confidently wrong answers early on can put a team off for months, so ground answers in company data through retrieval-augmented generation and make a plain answer that it could not find something acceptable. Explain what the copilot is for and what it is not, and collect feedback inside the tool so problems surface quickly.

How do you measure the value of an AI copilot?

Pick measures tied to the task the copilot supports. For drafting, look at the time from opening a case to sending a reply, and how much users edit the drafts. For research or analysis, measure time to an answer and how often people still need to ask a colleague. Usage is a signal in itself: how many people use it each week and how often suggestions are accepted show whether it is genuinely helpful.

Quality matters as much as speed. Spot-check outputs for accuracy, track errors that reached customers or records, and gather feedback on specific suggestions. Compare against a baseline measured before rollout, otherwise improvements are hard to show. If acceptance stays low, the copilot is aimed at the wrong task or lacks the right context.

How does Vizio AI build AI copilots?

Internal copilots are one of the AI-powered internal workflows our DX Studio builds, alongside research, reporting and decision support tools. We build them around the tools a team already uses and connect them through API and systems integrations to the CRM, finance, marketing, product or support data they need, so suggestions reflect the company's own context and permissions.

We support copilots after launch, adjusting them as people use them and as processes change. The practical first step is mapping one workflow, finding the repetitive reading, searching or drafting in it, and checking which data a copilot would need to help.