---
title: "A consistent AI setup for the team: AI Use Case"
description: "Shared projects, instructions and approved sources so everyone gets the same quality from ChatGPT or Claude."
url: https://lidvizion.ai/use-cases/team-ai-setup/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Leadership

# A consistent AI setup for the team

Shared projects, instructions and approved sources so everyone gets the same quality from ChatGPT or Claude.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fteam-ai-setup%2F#write) · [Configure your AI](https://lidvizion.ai/services/ai-setup/)

Where it starts

## The trigger.

The team already pays for AI, but everyone uses it differently.

How it runs today

- Prompts live in personal notes.
- Nobody knows which sources the assistant may use.
- Good results are hard to repeat.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Inventory
   List the recurring tasks people already use AI for.
2. 02 / Configure
   Set up shared projects, instructions and reference files.
3. 03 / Connect
   Give scoped access to the sources each task needs.
4. 04 / Teach
   Short, task-based walkthroughs, then a check-in.

Where people stay in control

## Approval points.

- Sensitive sources stay off until an owner approves access.
- Outputs that leave the company are reviewed.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Tasks with a shared setup.

### Time saved on the top three tasks.

### Adoption across the team.

Related

## Go deeper.

- [Configure your AI](https://lidvizion.ai/services/ai-setup/): Turn individual AI use into a repeatable way of working.
- [Fine-tuning vs. pre-trained models](https://lidvizion.ai/resources/basics/model-fine-tuning-vs-pre-trained-models/): Why most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.
- [Set up ChatGPT or Claude for a team](https://lidvizion.ai/guides/set-up-chatgpt-or-claude-for-a-team/): A four-step playbook to turn scattered individual AI use into a shared, governed setup your team actually uses.
- [Does a Small Business Need an AI Policy?](https://lidvizion.ai/answers/does-a-small-business-need-an-ai-policy/): Does a small business need an AI use policy?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [From stalled AI pilot to production](https://lidvizion.ai/use-cases/stalled-ai-pilot-to-production/): Leadership Take a pilot that impressed in a demo but never shipped, decide whether it deserves production, and get it there with owners and controls.
- [An AI tool and vendor decision you can defend](https://lidvizion.ai/use-cases/ai-tool-and-vendor-decision/): Leadership Replace scattered subscriptions and competing vendor pitches with one decision, made on your workflows, data rules and costs.
- [A technology roadmap the board can follow](https://lidvizion.ai/use-cases/technology-roadmap-for-the-board/): Leadership Turn scattered technical priorities into a sequenced roadmap with owners, costs and risks that non-technical leaders can hold you to.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)