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.
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.
- 01 / Inventory
List the recurring tasks people already use AI for.
- 02 / Configure
Set up shared projects, instructions and reference files.
- 03 / Connect
Give scoped access to the sources each task needs.
- 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 AITurn individual AI use into a repeatable way of working.Fine-tuning vs. pre-trained modelsWhy most teams should start with a capable pre-trained model, and when fine-tuning earns its cost.Set up ChatGPT or Claude for a teamA 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?Does a small business need an AI use policy?
More use cases
Other places to start.
Leadership
From stalled AI pilot to production
Take a pilot that impressed in a demo but never shipped, decide whether it deserves production, and get it there with owners and controls.
See the workflow LeadershipAn AI tool and vendor decision you can defend
Replace scattered subscriptions and competing vendor pitches with one decision, made on your workflows, data rules and costs.
See the workflow LeadershipA technology roadmap the board can follow
Turn scattered technical priorities into a sequenced roadmap with owners, costs and risks that non-technical leaders can hold you to.
See the workflowAn 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.
---
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/)