AI has moved quickly from experimentation into everyday work. In many tech businesses, it is already helping teams write, research, code, take meeting notes, respond to customers and improve internal workflows. In others, it is starting to shape the product itself.
The challenge is that AI use can spread faster than the legal setup around it. A team member tests a tool, finds it useful, and it becomes part of how work gets done. Before long, sensitive information, customer data or product work may be moving through systems the company has not reviewed closely enough.
Founders do not need to slow everything down with a heavy AI governance programme. They just need enough structure to know which tools are being used, what information is going into them, and where the business needs clearer rules.
Once founders start looking properly, they usually find that AI is being used in more than one way across the business.
Some use is relatively contained. It helps the team work faster, draft more easily, summarise information or think through a problem. These uses still need rules, especially where confidential information or customer data is involved, but they are usually easier to manage when the output stays inside the business.
The risk changes when AI becomes part of the product customers rely on. At that point, the company needs to understand how the feature works, what information it uses, how outputs are checked and what could happen if something is wrong.
The point is to avoid treating every AI use in the same way. Once founders understand where AI sits in the business, it becomes easier to decide what needs simple guidance and what needs closer control.
Consumer AI tools are easy to access, which is part of the appeal. The risk is that employees may use them with information the company would not want shared outside the business.
This often happens in ordinary working moments. Someone may paste in a customer query to draft a better response, upload code to troubleshoot a bug, or use AI to refine a confidential investor update. The employee is usually not thinking about legal risk or trying to create a problem. They are trying to get work done faster.
The difficulty is that the business may not know what happens to the information once it is entered into the tool. It may be stored, reviewed, retained, used to improve the model, or processed under terms that were never intended for business use.
That matters if the company has promised to protect confidential information in customer contracts, employment contracts, non-disclosure agreements or investor materials. Internal AI use needs to match those promises.
Most employees will not know where the legal line is unless the business explains it. An AI acceptable use policy gives the team practical guidance. It should make clear which tools are approved, what information must stay out of AI tools, when outputs need to be checked and when a more sensitive use needs approval.
The tone of the policy matters. If it feels unrealistic, people may ignore it. If it reflects how the team actually works, it can support better AI use without making the tools feel off-limits.
For a growing business, the aim is to move away from guesswork. People should know when AI is useful, when to pause, and who to ask before using it in a more sensitive context.
Employees often paste customer queries, code or investor updates into consumer AI tools to work faster. Once that information is in the tool, you may have no control over whether it is stored, reviewed or used to train the model.
AI use needs extra care when customer information is involved. A support team might want to summarise a customer issue, a product team might test a feature using real examples, or the product itself may process information as part of the service.
Those uses may be legitimate, but they need to match what the company has promised in its privacy notices, customer contracts and data protection documents. The business should be clear on what information is being used, why it is needed, where it goes and whether customers have been told enough about the processing.
This matters even more where the company acts as a processor for its customers. The contract may limit how customer data can be used, and some customers will expect clear wording that their data will not be used to train or improve AI systems unless they have agreed to it.
One of the first questions to answer is whether customer data is used to train or improve models. If it is not, that should be clear in the contract, product documentation and internal guidance. If it is, the company needs to think carefully about consent, contractual permission, transparency and data minimisation.
Founders should also understand how their AI providers handle prompts and outputs. Enterprise settings may offer stronger controls over training use, retention and access. Consumer settings may not give the same comfort.
Enterprise customers increasingly expect explicit wording that their data will not be used to train or improve AI systems without their agreement. If your contract is silent on this, it can stall procurement while the customer asks you to prove it.
An AI acceptable use policy is one of the simplest ways to bring structure to AI use inside the business. It does not need to read like a regulatory manual. It needs to help people make better decisions in everyday work.
The team should know which tools are approved, what information must stay out of them and who to ask before using AI in a more sensitive context.
The real value is clarity. People should not have to guess whether they can paste something into a tool, rely on an output, or start using AI in a new part of the business.
The guidance should reflect the way AI is already being used. Sales teams, developers and product teams will not all use AI in the same way, so the rules should speak to the work people are actually doing.
The policy does not need to predict every future use. It should give the team enough direction for current use, with a simple process for anything new, sensitive or higher risk.
AI tools can produce useful work quickly, but the output still needs human judgement before the business relies on it. That applies to internal work and customer-facing products. An AI output may be inaccurate, incomplete, outdated or simply wrong for the context. In some cases, it may also reflect bias or sound more certain than it should.
Some uses of AI need a more careful review process that matches the use. This is especially true where AI supports decisions about people, money, health, employment, access to services or legal rights. It also matters in regulated sectors, or where customers are likely to rely on the output when making important decisions.
The business should be clear about where human review is mandatory, who is responsible for it and whether a record should be kept. The more important the decision, the clearer the oversight should be.
If AI is part of the product, the customer contract should reflect how it works in practice. Customers need to understand what the AI feature does, how it should be used, where its limits are, and when human review may still be needed.
This matters most where customers may rely on AI outputs in their own business. The company should avoid promises that go further than the product can realistically support, especially around accuracy, automation, compliance, security or decision-making.
Clear terms help customers understand what they are buying, how the AI should be used and where responsibility sits.
The same care should apply to marketing. Claims about AI features should be accurate, clear and easy to support.
Customers often form expectations before the contract is signed. If the marketing promises one thing and the contract is much more cautious, the company can create confusion and risk.
Clear, measured positioning is usually stronger than overclaiming. It makes sales conversations easier and helps customers trust the product.
A good AI policy starts with understanding how the business is already using AI.
Start with a simple internal check. Look at which AI tools teams are using, what they are using them for, and whether any of those uses involve customer data, confidential information, source code or customer-facing outputs.
That exercise will usually show where the legal setup needs to catch up. It may be approved tools, clearer data rules, human review, customer terms or supplier checks.
It does not need to be complicated. The important thing is to make sure AI use is visible and managed, rather than growing quietly in parts of the business no one has properly reviewed.