Some uses of artificial intelligence in Poland offices are obvious: perhaps your developers use GitHub Copilot to generate code. But so many other applications of AI are harder to spot. You might not realize that AI is already built into the software you use for hiring. If you use fraud detection software to flag suspicious transactions, it might rely on machine learning models behind the scenes.
The problem? You can’t govern AI properly if you don’t know where and how you’re using it.
Defining AI
There is no single working definition of AI across most organizations. Because of that inconsistency, a system that may be using machine learning might be described internally as ‘analytics.’ One of your vendors might call a product ‘automation’ even though an AI model is making decisions behind the scenes.
And then there’s AI that escapes governance altogether, which is much less tidy than just employees secretly using ChatGPT at home. Your organization could already be using ‘approved’ software that you thought was purely rules-based but actually relies on AI. A standard software inventory will not always reveal that distinction.
How to get a clear view of your AI use
For organizations adopting artificial intelligence in Poland, it’s important to first identify where AI is already being used before deciding how to govern it.
1. Find where AI might be hiding
Don’t just look for products that carry an obvious AI label. Check whether the software your teams already use includes machine learning or AI-driven features. Review vendor documentation to see whether AI features have been added to systems you already use.
2. Build an AI inventory
Create a record of:
- the systems that use AI
- what they are used for
- which business processes depend on them
Include third-party tools as well as internally developed systems.
3. Categorize your AI tools into risk tiers.
Your exact categories will depend on how your organization uses AI, but as a general guide, you could group systems according to the consequences if they fail or make the wrong decision:
High risk:
This is for AI that auto-executes high-stakes actions, such as AI that.
- touches sensitive business/customer data
- makes financial decisions
- influences hiring
- changes security settings or takes security actions
Medium risk: This could be AI used internally (not public-facing) to summarize meeting notes or analyze non-confidential operational data. It can also include AI used to draft code that a developer reviews before it reaches production.
Low risk: This is AI used for general brainstorming or basic web research.
4. Put controls that match each risk tier in place.
For high-risk AI, require human-in-the-loop review before consequential actions are taken.
For medium-risk, set up data-loss prevention guardrails so that sensitive data can’t leak. You might also need to run monthly spot-checks on output accuracy.
And for low-risk AI use, consider publishing acceptable-use guidelines.
We can help
If your business is adopting artificial intelligence in Poland, CA can help you identify where AI is already being used and assess the risks around it. Talk to our team.
