Business managers today are under pressure from two directions: on one side they hear “if you don’t adopt AI, you’ll fall behind,” and on the other they worry about data security, cost and how employees will react. The right decision usually lies somewhere in between: targeted use, with eyes open to the risks.
Real benefits
- Saving time on repetitive work: drafting letters and emails, summarizing reports, categorizing requests and extracting information from documents.
- Faster response: first-line answers to frequent customer questions, even outside working hours.
- Data analysis: finding patterns in sales, inventory or customer behavior data that are not easy to spot by eye.
- Help for IT and security teams: detecting unusual behavior in the network and email, and helping read logs and write scripts.
- More capability for small teams: a company without a full-time content writer or data analyst can handle part of that work with the help of tools.
Drawbacks and risks
- Errors that look confident: output can seem completely correct and still be wrong; without review, the error reaches a customer or a management decision.
- Data security and confidentiality: entering customer information or internal documents into public services can lead to data leaks.
- Dependence on an outside service: prices, features and terms of services change, and access to some of them from Iran may be limited or unstable.
- Data quality: if the company’s data is incomplete, scattered or messy, the AI’s results will be poor as well.
- Impact on employees: fear of being replaced, or the opposite, over-reliance and loss of skills, can harm the work culture if it is not managed.
- Shadow tools: employees who install or use various tools without the IT department’s knowledge take control of data away from the company.
Count the hidden costs too
The cost of AI is not just a service subscription. Also account for the time spent training employees, reviewing output, preparing and cleaning data, integrating with existing software and, if you run it in-house, hardware, power and maintenance. Sometimes, after doing this math, a simple automation without AI turns out to be the better option.
Before asking “What should we do with AI?”, ask “Which specific problem is costing us a lot of time or money?” and then see whether AI is a good fit for it.
A realistic start
- Pick one or two low-risk uses: such as drafting emails or summarizing non-confidential documents.
- Define a success measure: for example, the time spent on a specific task, before and after.
- Write a usage policy: what data is allowed, which tools are approved and who approves the output.
- Run a limited pilot: with a small team for a few weeks, then measure the results.
- Decide: expand, adjust or drop it; all three outcomes are acceptable.
The role of the IT department
The IT department should be involved in decisions from the start: maintain a list of approved tools, control access through company accounts and two-factor authentication, classify sensitive data, and keep backups of the data used in projects.
Checklist
- You have defined a specific, measurable problem before choosing a tool.
- You have a written AI usage policy for employees.
- Confidential information is classified, and entering it into public tools is prohibited.
- Human review is assigned for every important output.
- You have weighed the hidden costs and the dependence on outside services.
- You evaluate the pilot’s results against criteria set in advance.
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