Benefits of generative AI for companies (and its limits)
What generative AI handles well inside a company, where it fails, and which controls are needed to use it without creating problems.
June 26, 2026 · Santiago Raúl Díaz · 2 min read · Updated on
Generative AI adds value in a company when it produces drafts, summarises long documentation, answers questions about your own material, and translates technical language into plain terms. Its limit is that it doesn't verify: if a fact isn't in the material it's given, it fills the gap.
Where it adds real value
- Drafting: proposals, replies, descriptions, and communications a person then reviews.
- Summarising long documentation: reports, contracts, minutes, tender documents.
- Querying your own material in natural language, when connected to company documentation.
- Classifying text that arrives unsorted: complaints, inquiries, emails.
- Translating technical language into explanations a customer understands.
The pattern is clear: it pays off on tasks where the output is text someone will review, and where the cost of an imperfect first version is low.
Where it fails
The main risk has a name: hallucination. A generative model produces plausible text, not verified text. If a fact is missing, it doesn't stop: it fills in with something that sounds right.
This isn't a niche concern: in Stanford HAI's AI Index 2026, 74% of organizations name inaccuracy as their top AI-related risk.
That's why it doesn't work alone for answering questions about your company's specific information. There you need to connect it to the real documentation through a RAG architecture, which forces it to answer from the loaded material and cite where each fact came from.
- It doesn't verify: it can confidently state something incorrect.
- It doesn't know your company unless connected to your documents.
- It doesn't replace professional judgement or legal responsibility.
- It can reproduce biases present in its training data.
The controls you need
| Criterion | Risk | Control |
|---|---|---|
| Invented answers | High | Connect to your own documentation and require source citation |
| Sensitive data exposed | High | Define what information may leave the company, and with which provider |
| Text published unreviewed | Medium | Mandatory human review before publishing or sending |
| Informal use by the team | Medium | Clear internal policy on what can be pasted into an external tool |
The last point is the most neglected. Without an internal policy, it's common for someone to paste a contract or customer data into a public tool without thinking about where that information ends up.
In Argentina there is also a legal framework. Personal Data Protection Act 25.326 classifies health-related information, among others, as sensitive data and sets specific conditions for processing it. Before pasting information into an external tool, it's worth knowing whether it falls into that category.
How to start without risk
Pick a task where the output is a draft that was going to be reviewed anyway, measure how much time it saves and at what quality, and define in writing what information may be used. With that you can decide whether to scale it to more sensitive uses.
Generative AI is useful for drafting, summarising, classifying, and querying your own material. It doesn't verify, so it needs to be connected to company documentation and to have human review before anything goes out. And it needs an internal policy on which data may be used.
Frequently asked questions
Can generative AI invent information?
Yes. It produces plausible, unverified text. To use it on your company's information you have to connect it to the real documentation and require it to cite the source of each fact.
Is it safe to upload company documents?
It depends on the provider and the configuration. Define in writing what information may leave the company, and consider deployments where data isn't used to train third-party models.
Which tasks should we start with?
Those producing a draft that was going to be reviewed anyway: proposals, replies, summaries. The cost of an error is low and the saving is easy to measure.
Sources
- AI Index Report 2026 — Economy chapter — Stanford HAI, 2026
- Act 25.326 on Personal Data Protection — consolidated text — Argentina.gob.ar, 2000