AI in practice: models, tools and responsible use
For business decision-makers: the difference between models and tools, when cloud vs local AI fits, and how to use AI responsibly from a data-protection angle.
This page is Regcytech’s own plain-language explanatory material. It is not legal advice and not a product recommendation. AI models change quickly, so specific capabilities and prices should always be checked for the current state.
What is AI — and what is it not?
Most business AI today is a so-called large language model (LLM): a system trained on huge amounts of text that generates answers based on patterns. It is a useful tool, but not “all-knowing” and not flawless.
What AI is NOT matters: it is not a guaranteed-accurate source of fact, not a bearer of legal or professional responsibility, and not a replacement for human review. Regcytech’s principle: AI-assisted work, always closed out and verified by a person.
Chatbot, LLM, agent and automation — what’s the difference?
- LLM: the language model itself, which interprets and generates text.
- Chatbot: a user interface to an LLM through which you converse with it.
- Agent: a system that plans steps and uses tools to reach a goal — with more autonomy, but also more risk.
- Automation: a predefined, repeatable process — not necessarily AI, but often combined with it.
Cloud AI vs local AI
Cloud AI runs on an external provider’s servers: quickly available and powerful, but data leaves the company. Local AI runs on your own infrastructure: more control and privacy, in exchange for more setup and more limited capability.
In practice a combination is often right: sensitive data kept local or anonymised, general tasks handled with a cloud tool. Regcytech’s principle: sensitive client data local by default, cloud AI only with anonymised or non-sensitive input.
When does which type of tool fit?
Not “which is best” but “which fits what” — by use case, stated neutrally.
| Use case | What to look for | Typically a good fit |
|---|---|---|
| General reasoning, drafting | Reliability, verifiability | Large cloud reasoning models |
| Long document work | Large context, accurate citation | Long-context models |
| Coding support | Code quality, IDE integration | Models tuned for development |
| Research / search | Fresh, cited sources | Search-integrated tools |
| Image / video | Licensing, terms of use | Dedicated media models |
| Local / private use | Data stays local, control | Local / on-prem models |
| Cost / control | Predictable cost, limits | Model chosen to fit the need |
| Business governance | Logging, access, review | Enterprise / governance-capable setup |
Use case
General reasoning, drafting
What to look for
Reliability, verifiability
Typically a good fit
Large cloud reasoning models
Use case
Long document work
What to look for
Large context, accurate citation
Typically a good fit
Long-context models
Use case
Coding support
What to look for
Code quality, IDE integration
Typically a good fit
Models tuned for development
Use case
Research / search
What to look for
Fresh, cited sources
Typically a good fit
Search-integrated tools
Use case
Image / video
What to look for
Licensing, terms of use
Typically a good fit
Dedicated media models
Use case
Local / private use
What to look for
Data stays local, control
Typically a good fit
Local / on-prem models
Use case
Cost / control
What to look for
Predictable cost, limits
Typically a good fit
Model chosen to fit the need
Use case
Business governance
What to look for
Logging, access, review
Typically a good fit
Enterprise / governance-capable setup
AI models change quickly, so specific capabilities and prices should always be checked for the current state. This table is a thinking framework, not a ranking.
What should a company watch when using AI?
- Do not upload sensitive client data, contracts or personal data into public cloud AI.
- Anonymise or mask if you use a cloud tool for a general task.
- Clarify what a given provider does with submitted data (does it train on it, how long is it stored).
- Keep sensitive work local or in a closed environment.
- Have a person review every AI output before it reaches a client or an authority.
AI governance basics
- An inventory: where and for what we use AI in the organisation.
- Classification: which uses are higher risk.
- Data-handling rules: what may go to the cloud and what may not.
- Human review: who closes out outputs, and how.
- Documentation: a short, maintained internal policy and a trace.
Typical mistakes when introducing AI in a company
- Thoughtlessly uploading sensitive data into a public tool.
- Using AI output as fact without review.
- Choosing a tool without clarifying the goal and the risk.
- No owner and no rules — everyone does something different.
- An “AI for everything” expectation, without real process and governance.
How can Regcytech help?
- AI governance and documentation readiness
- an AI use-case workshop to map realistic uses
- AI workflow setup and organising internal processes
- local AI / Mac Mini AI advisory for sensitive work
- setting up a compliance documentation workflow
What we do not provide
We work in an advisory, readiness role, and AI-assisted work is always closed out by a person:
- We are not an AI model or software vendor.
- We do not give a legal or compliance guarantee for AI use.
- We do not recommend uploading sensitive client data into public cloud AI.
- We do not guarantee the accuracy of AI outputs — every output needs human review.
Where we can help
Related guides
Let’s see where AI can help your company
A short overview maps realistic AI use cases and the data-protection boundaries — with no obligation.