Solution · AI Policy

AI is already happening. Provide frameworks for it.

AI is already happening. Provide frameworks for it.

Your team is already using AI in the workplace, whether you have it properly organized or not. The risk lies not in the usage, but in the lack of frameworks: a shared account, customer data entering a tool unnoticed, and no visibility into what is being sent out. We write a concise, workable AI policy that lets your people work safely and smartly with the tools they already use.

Your team is already using AI in the workplace, whether you have it properly organized or not. The risk lies not in the usage, but in the lack of frameworks. We write a concise, workable AI policy that lets your people work safely and smartly with the tools they already use.

AI-policy

Frame

policy active

Prompt

allowed

Customer data

shielded

Output

checked

Within policy · safely shared

Your people are already using AI, just unstructured.

The problem, framed as a heading.

Somewhere in your company, someone is running a customer email through ChatGPT. Someone else is sharing the password for a free account the whole team uses. A third person is pasting a quote into a tool to have it summarized. It is already happening, every day, and largely out of sight.

The problem is not that they use AI — that is exactly what you want. The problem is that there are no frameworks: no one knows which data is and isn’t allowed in a tool, which accounts are being used, or what needs to be checked before anything leaves the building. Without frameworks, every use is a gamble. And that doesn’t work; it just goes underground.

Somewhere in your company, someone is running a customer email through ChatGPT. Someone else is sharing the password for a free account the whole team uses. A third person is pasting a quote into a tool to have it summarized. It is already happening, every day, and largely out of sight.

The problem is not that they use AI — that is exactly what you want. The problem is that there are no frameworks: no one knows which data is and isn’t allowed in a tool, which accounts are being used, or what needs to be checked before anything leaves the building. Without frameworks, every use is a gamble. And that doesn’t work; it just goes underground.

What’s already happening

A customer email run through ChatGPT, unnoticed

One free-account password shared across the team

Quotes pasted into tools no one is checking

Three steps, one workable policy.

From the actual situation in the workplace to frameworks your team actually uses.

Step 01

Mapping

Step one

We look at who is already working with AI, with which tools, and where the risks lie: customer data, accounts, and the quality of what is sent out. No assumptions, just the actual situation.

One line describing the first action — the input from the user.

Step 02

Define frameworks

Step one

We draw the red lines: what is allowed, what is not, and what gets double-checked. Concrete and short, in language people actually use.

One line describing the first action — the input from the user.

Step 03

Capture & bring to life

Step one

We capture it in a short, clear document. Not a thick file in a drawer, but living frameworks the workplace returns to — brought to life so people use it.

One line describing the first action — the input from the user.

What an AI policy looks like in practice.

Three risk areas from real work: accounts & access, customer data, and responsibility for output.

Example 01 · Accounts

From one shared account to secure access.

Before

The entire team worked on a single free account, logged in with a generic email address and a password that got passed around. No one had visibility into what was going on; the tool stored company data without anyone realizing it, and the free version lacked the settings to protect data. Everyone used it; no one was responsible.

Now

No more shared logins. Anyone actively working with AI gets their own business account where company data stays protected. The policy defines how you use ChatGPT for business: on which accounts, with which settings, and which contact is centrally configured so everyone works from the same basis.

Example 02 · Customer data

From “is this actually allowed?” to a single rule of thumb.

Before

The question kept coming back: can I paste customer data into an AI tool? The answer depended on who you asked. Some did it without thinking, others didn’t dare. No structure, no guidance, and therefore either too much risk or too much caution.

Now

The policy draws one clear red line around traceable data and makes it concrete with examples in two columns: this can be entered into a tool, this cannot. Plus one rule of thumb that removes any doubt — one question everyone can test for themselves.

Example 03 · Output

From isolated experiments to a shared working method.

Before

Anyone creating something with AI did so on their own. Some delivered sharp copy, others sent out something copied verbatim, full of errors and a tone that made no sense. The quality depended on who happened to be doing it.

Now

The policy states that humans stay ultimately responsible for everything that goes out: fact-checking, proofreading for tone and context, and recognizing AI writing before it’s sent. Plus practical support: prompting tips and shared working methods, so everyone starts from the same level.

Five things that are changing.

How clear frameworks change throughout your entire company.

Customer data remains secure.

Everyone knows which data is and isn’t allowed in a tool. No more shared passwords.

End to shadow usage.

No more shared accounts and hidden tools, but visibility into what is happening.

Your team dares to experiment.

Frameworks provide space; within the lines, you are free to experiment without being afraid of making a mistake.

Quality monitored.

What goes out is checked by a human, not blindly accepted.

Responsible AI use that grows.

A living policy that evolves with new tools and new usage, not a snapshot that becomes outdated.

No ban list. A concise policy that suits how you work.

Geen losse tool.
Een automatisering die past bij je proces.

We don't write a thick set of regulations full of rules that no one reads.

We help SMBs set up their work smarter with software and AI. No vendor lock-in — only what fits the way you work.

We look at how you are currently working with AI, determine the frameworks that really matter together, and summarize it in a short document that people actually use.

Permissiveness as a starting point, one testable rule of thumb as the core, and room to experiment within safe boundaries. We don't write it for you, but with you — aligned with the tools and the workplace that are already in place.

And because AI is changing rapidly, the policy is not an endpoint but a snapshot that we allow to grow with you.

It starts with discovery — sitting in, listening, mapping the process — and only then designing and building.

We do the whole chain, not just the fashionable part, and stay involved during rollout.

We help SMBs set up their work smarter with software and AI. No vendor lock-in — only what fits the way you work.

It starts with discovery — sitting in, listening, mapping the process — and only then designing and building.

This is how the process works

Mapping

Who works with what, and where the risks lie: accounts, data, output.

Define frameworks

The red lines and one testable rule of thumb, with a permissive starting point.

Establish

Short, clear frameworks — three to five pages — that adapt to new tools and practices. A living policy.

Make it live

Adapting to new tools and new practices. A living policy.

FAQ

Here are the answers to the most asked questions

What does an AI policy actually involve?

An AI policy is a short document that sets out how your team works with AI safely and smartly: which data can and can't go into a tool, which accounts are used, and who's accountable for what goes out. Three to five pages of clear guardrails people actually use.

What does an AI policy actually involve?

An AI policy is a short document that sets out how your team works with AI safely and smartly: which data can and can't go into a tool, which accounts are used, and who's accountable for what goes out. Three to five pages of clear guardrails people actually use.

What does an AI policy actually involve?

An AI policy is a short document that sets out how your team works with AI safely and smartly: which data can and can't go into a tool, which accounts are used, and who's accountable for what goes out. Three to five pages of clear guardrails people actually use.

Why not just ban the risky tools?

Why not just ban the risky tools?

Why not just ban the risky tools?

Do you write the policy for us, or with us?

Do you write the policy for us, or with us?

Do you write the policy for us, or with us?

What if our AI use changes again later?

What if our AI use changes again later?

What if our AI use changes again later?

Do you also help after the policy is in place?

Do you also help after the policy is in place?

Do you also help after the policy is in place?

Ready to let your team work safely with AI?

A half-hour conversation is enough to see where you stand and which frameworks are most urgent. We listen, ask questions, and think aloud with you.