The AI setting every company should review before using it

Generative AI is already part of everyday work in many organizations. It is used to summarize documents, prepare meetings, draft emails, analyze information, generate ideas, and accelerate tasks that used to take hours. The problem is that, in numerous instances, adoption has moved faster than the reflection on how these tools are being used.

An AI tool may look simple: you open ChatGPT, Copilot, Gemini or Claude, write an instruction, and get a response. But in a professional context, the important question is not only what AI can do. It is what information we are sharing with it, from which account, under which settings, and with which usage criteria.

And this is where one of the least reviewed issues appears: privacy and data-use settings.

The challenge is not using AI, but using it properly

In many companies, AI has entered through the side door. Not necessarily through a major transformation program, but through professionals who found a useful tool and started using it to work better. The intention is usually positive: save time, improve a presentation, summarize a report, or prepare a proposal more efficiently.

But this spontaneous adoption carries a clear risk. When each person uses a different tool, with personal accounts, unknown settings, and no shared criteria, the organization loses visibility over something fundamental: what information is being entered into external systems.

That does not mean companies should stop using AI. It means they need to professionalize its use.

Productivity with AI is not achieved simply by giving people access to tools. It is achieved when people know what they can do, what they should avoid, how they should review outputs, and what information they can share in each case.

Shadow AI as a symptom, not the cause

Shadow AI describes the use of artificial intelligence tools outside the channels approved by the organization. It is the modern version of “I found my own workaround,” but with generative models connected to sensitive information.

An employee may paste a commercial proposal into a personal account to improve the writing. Another may upload a meeting transcript to get a summary. Someone may ask an AI tool to analyze a contract, reorganize a strategic document, or draft a response to a client.

On the surface, these are reasonable tasks. In fact, many of them are excellent AI use cases. The issue is not the task itself, but the context: what data is being shared, which version of the tool is being used, which settings are active, and whether the person understands the implications.

AI needs context to be useful. But in business, context is often the most sensitive asset: client names, figures, internal decisions, strategies, contracts, personal data, commercial proposals, or information that is not yet public.

That is why the conversation about AI in organizations cannot stop at “which tool is best.” It needs to move towards “what conditions do we need to use it safely.”

The setting almost nobody checks

Many AI platforms provide controls to limit whether conversations, files, or interactions can be used to improve their models. The problem is that these options are not always in the same place, do not work in the same way across platforms, and may vary depending on the type of account.

Using a free personal account is not the same as using a professional, team, enterprise, or corporate environment such as Microsoft 365 or Google Workspace. The interface may look very similar, but the privacy, retention, and data-processing conditions can be very different.

In ChatGPT, for example, there is a setting called “Improve the model for everyone” that allows users to turn off the use of conversations for model training or improvement. In Gemini, activity and privacy management require particularly careful review, especially when personal Google accounts are used. In Claude, the “Help Improve Claude” control allows users to manage data contribution to training in certain plans. In Copilot, the difference between a personal account and Microsoft 365 Copilot with enterprise protection is especially relevant.

The practical conclusion is simple: before using AI with professional information, it is worth reviewing each tool’s privacy settings and understanding what type of account is being used.

Not out of fear. Out of professional hygiene.

The account matters as much as the tool

One of the most common misunderstandings is assuming that “using Copilot”, “using Gemini” or “using ChatGPT” always means the same thing. It does not.

In artificial intelligence, the tool is only one part of the equation. The account, the plan, the working environment, and the applicable terms are just as important. An organization may have strong guarantees in an enterprise environment and, at the same time, remain exposed if employees use personal accounts to handle professional tasks.

This distinction is critical in any adoption process.

It is not enough to say, “We can use AI.” Companies need to define which tools can be used, with which accounts, for which tasks, with what types of information, and under what human review process.

Otherwise, the organization creates a fairly common paradox: it encourages productivity while leaving security in the hands of individual intuition. And individual intuition, when it comes to privacy and compliance, is a rather creative policy. Not always in a useful way.

Settings help, but they do not replace judgement

Turning off the use of data for training is a good practice, but it does not solve every risk on its own. It is an important piece, not a complete strategy.

A company may have the right settings and still face problems if its teams enter sensitive information without anonymizing it, fail to distinguish between public and confidential data, accept AI outputs without verification, or use unapproved tools for critical tasks.

AI security does not depend only on the provider. It also depends on usage habits.

That is why training plays a central role. Not as a generic session to “learn prompts,” but as a process of practical literacy: understanding how these tools work, where they fail, what data should be protected, how to review outputs, how to document decisions, and how to integrate AI into real processes without losing control.

AI does not remove professional responsibility. It moves it to new layers: judgement, supervision, traceability, and diligence.

What every organization should review

Any company that wants to adopt AI productively should review at least five areas.

The first is the tool map. Which platforms are actually being used, both officially and informally? Many organizations believe they are just starting to use AI when, in reality, their teams have been using it on their own for months.

The second is the type of account. A personal account is not the same as a corporate account with enterprise guarantees. This difference should be clearly explained to every team, because many poor practices are born simply from lack of knowledge.

The third is privacy settings. Each platform has its own menus, options, and conditions. Reviewing them should be part of the basic adoption process, just like configuring permissions in a CRM or defining access levels in an analytics tool.

The fourth is information classification. Employees need to know what they can enter into an AI tool, what they should anonymize, and what they should never share. Without this guidance, each person improvises their own criteria.

The fifth is human review. AI can accelerate a lot of work, but it should not become a black box producing texts, analyses, or decisions without supervision. In professional contexts, the final output remains the responsibility of the person using it and the organization that publishes, sends, or applies it.

From tool to habit

True AI adoption does not happen when a company buys licenses. It happens when its teams incorporate new work habits.

Preparing meetings better. Summarizing information without losing nuance. Analyzing documents with judgement. Writing faster without delegating responsibility for the message. Automating repetitive tasks without turning the organization into a more efficient error factory.

To get there, technology is necessary, but not sufficient.

People need to be trained so they understand what they are doing. Clear usage frameworks are needed. Settings must be reviewed. Risks must be explained without drama and opportunities without selling magic. Ultimately, AI needs to become a professional competence, not a collection of tricks.

Trust is also configured

Trust in AI does not appear by default. It is built.

It is built when an organization knows which tools it uses. When it distinguishes between personal and corporate accounts. When its teams understand what data they can share. When relevant settings are reviewed. When outputs are validated before being added to a document, campaign, proposal, or decision.

Productivity matters. Speed matters too. But neither compensates for losing control over information.

Safe AI adoption often begins in a rather unglamorous place: a settings menu almost nobody checks. But it does not end there. The real change happens when that review becomes a culture of use, shared judgement and training applied to real work.

Because the relevant question for companies is no longer whether they will use AI.

The question is whether they are preparing their teams to use it well.