Until a few years ago, most business uses of AI were about “detection” or “prediction”: is this email spam or not, how much will we sell next month. Generative AI goes a step further and creates new content: a draft letter, an advertising image or a script. That ability has created many opportunities, and new risks as well.
A simple definition
Generative AI refers to models that have been trained on very large amounts of data and can produce new output similar to that kind of data. The user usually states the request in a text instruction, or “prompt,” and the model builds the result. The key point is that the model does not look up answers in a database of correct responses; it generates a likely output based on the patterns it has learned. That is why the output can be fluent and convincing, yet wrong.
Types of output
- Text: chatbots such as ChatGPT, Claude or Gemini for writing, summarizing, translating and answering questions.
- Images: creating or editing images from a text description, for early ad concepts or social media content.
- Audio and video: text-to-speech and creating short audio or video clips.
- Code: suggesting and writing code, scripts and formulas; IT teams use this every day.
Some tools support several types of input and output together; for example, they take a photo of an invoice, then extract and summarize its text.
What can it do for a company?
- Drafting content: business letters, email text, product descriptions or social media posts; editing and final approval stay with a person.
- Summarizing: summaries of meeting minutes, long reports or email threads.
- Helping IT staff: explaining error messages, suggesting PowerShell or Bash scripts, and helping write documentation.
- First-line customer responses: answering frequently asked questions, provided complex cases are handed to a person.
- Brainstorming: lists of ideas for a campaign, headlines or the structure of a presentation.
The best use of generative AI is “a quick draft plus human review,” not a full replacement for human decisions and judgment.
Where you should not rely on it
- Precise, critical information: financial figures, technical specifications, contract clauses, or anything where an error is costly must be checked against the original source.
- Sensitive decisions about people: such as hiring or evaluating employees, without close human oversight.
- Running code directly in production: a script written by a model should first be checked in a test environment.
Risks to manage
- Data leakage: text you enter into a public service may be stored or reviewed under that service’s terms. Do not enter customer information, passwords or confidential documents unless you have reviewed the terms and privacy settings.
- Content ownership: the legal status of generated content and of training data is neither uniform nor settled everywhere; be cautious with commercial use.
- Impersonation and deception: the same technology is used to create convincing phishing emails and fake audio or images; employees should confirm unusual requests, such as an urgent payment, through another channel.
- Service access and stability: access to some foreign services from Iran may be limited, and their features and terms also change; check this before making your processes depend on them.
Checklist
- Put in writing which uses of generative AI are allowed and which are not for employees.
- Specify which information must never be entered into public tools.
- Make human review mandatory for every important output.
- Run generated code and scripts only after testing them in a separate environment.
- Make employees aware of phishing and fake audio and images.
If you want to use these tools more securely, ITPlus724 engineers can strengthen your company’s network security, access control, business email and data backup.