Generative AI in companies: drafts, classification and controls

You have tried a generational AI tool to write an email, summarize a document, or classify information. This guide focuses on those generated outputs: how to define instructions, allowed data, formatting and revision before using them in a company.

The decision is not to use generative AI everywhere. It is to identify what specific task you can attend, what information you can use, and which person should validate the result. If you also need to connect tools, execute actions, and manage exceptions, see the AI automation within business processes guide.

What is being tried with generative AI

Generation AI can produce a first draft based on instructions and information provided to it. In a company, its usefulness is usually in tasks where there is a repeatable pattern, but the result needs to be adapted to each case: preparing an initial response, summarizing texts, extracting data from documents or proposing a classification.

The aim should not be to ‘put AI’ in a process, but to improve concrete action without losing traceability or human judgement.

For example, in a hypothetical scenario, a business receives requests from multiple channels. The opportunity is not just to write answers faster: it can be to collect the basic data, identify what is missing, and prepare a draft for a person to review before sending it.

Separate these three layers clarifies the decision:

-OBjective: what should change in daily work. -Scope: which task, inputs, outputs and exceptions will be included. -Solution: how the generative AI will connect to the tools you already use.

common mistakes when applying generative AI

1. Start with the tool and not the problem

Symptom: you open a generational AI tool and test ideas without knowing what task it should solve or how to check if it brings value.

Cause: confuses the ability to generate text or analyse information with a complete solution for the business.

Consequence: the test can produce striking results, but it doesn't fit into a workflow.You end up adding manual steps, doubling information, or creating a task that nobody assumes in a stable way.

Correct: describes the problem with an operational phrase first. For example: "I need to prepare a summary of the requests received and detect the missing data before assigning them." Then, define who starts the process, what information enters, what result should come out and who uses it.

2. Ask for vague results with vague instructions

Symptom: the result changes a lot between one request and another, includes irrelevant information or uses a tone that does not correspond.

Cause: the instruction does not specify the objective, context, output format or task limits.

Consequence: the person reviewing must redo much of the work.Generative AI becomes a source of inconsistent drafts, not a real help.

Correct: turns the request into a clear template. It indicates what to do, with what data, for whom, in what format and what to avoid. If the result will be a response for a client, it also defines which fields to review before sending it.

3. Treat a generated response as a verified response

Symptom: is copied and sent without reviewing data, context or conclusions.

Cause: the tool is assigned a responsibility that belongs to a person: decide, confirm or approve.

Result: may circulate incorrect, incomplete or inappropriate information for the case. The problem is greater when content affects customers, commercial conditions, internal decisions or sensitive documentation.

Correct: defines which outputs can be used as drafts and which require mandatory validation. Human revision should be part of the flow, especially when the result communicates commitments, interprets relevant information, or triggers an external action.

4. Enter information without deciding what data can be used

Symptom: for a more accurate response, complete texts, documents or records are pasted without a previous rule.

Cause: gives priority to the convenience of testing over the control of information entering the system.

Result: you lose visibility about what has been shared, for what purpose and who can repeat that use. In addition, the process becomes difficult to review or maintain.

Correct: before connecting data, identify what information the task really needs. Minimize input to what is needed, remove data that does not contribute to the result and set who authorizes the use of each source. If there are doubts about obligations applicable to your data, it is appropriate to validate them with the relevant specialized profile.

5. Automate a process that is still confusing

Symptom: two people perform the same task differently, exceptions appear constantly or no one knows what should happen when data is missing.

Cause: is intended to automate before ordering manual process.

Consequence: the generative AI reproduces ambiguities and multiplies the cases that require intervention. Instead of simplifying the operation, it moves the disorder to another tool.

Correct: draws the current path: trigger, input data, steps, responsible, result and exceptions. You don't need to document every detail from the start, but you do need to agree what should happen in the usual cases and what cases should leave the automatic circuit.

6. Do not define what happens when AI cannot solve the task

Symptom: the flow stops, generates insufficient output or no one detects missing information.

Cause: the design only looks like the ideal case: complete data and a usable response.

Consequence: silent incidents accumulate or incomplete work is derived to clients and internal equipment.

Correct: sets alternative outputs.For example: mark the case for review, request missing data, register an incidence or send the task to a responsible person. A reliable process does not need to appear to solve everything; it needs to recognize when it should stop.

7. Measuring the novelty, but not the functioning of the process

Symptom: the initiative is valued for the apparent quality of some examples, without checking whether it improves the work you wanted to solve.

Cause: A simple acceptance criteria have not been defined before it is implemented.

Result: is difficult to decide whether to maintain, adjust, or discard automation. Conversations are based on impressions rather than on expected flow behaviour.

Correct: agrees how you will check the result. You can check whether the flow collects the defined data, if it leads incomplete cases to the right person, and if the output adopts the agreed format. The criterion should describe an action and a verifiable result.

How to prevent these errors before automating

It starts with a limited and repeatable case. You don't need to address all customer service, all documentary management or all internal communications at once.

A well-concluded first version may include:

1. a specific task;
2. an identified data source;
3. a defined formatted result;
4. a person responsible for reviewing exceptions;
5. a rule to stop or scale cases that do not meet the conditions.

Avoid presenting automation as an automatic replacement of criteria. Generation AI may assist in preparation, classification or writing, but business decisions and validations required continue to require clear accountability.

Checklist for your use case

Before incorporating generative AI into a process, review these questions:

• Have I described the concrete task I want to improve?
• Do I know who uses the result, and what will he do with it afterwards?
• Have I separated the objective from the technical solution?
• Have I defined what data the task needs and which data should not be used?
• Does the instruction indicate context, format and limits of the result?
• Is it clear what content is a draft, and what requires human validation?
• Have I foreseen what happens to incomplete data, errors or exceptional cases?
• Is there a person responsible for reviewing the flow and its changes?
• Can I verify, on concrete criteria, that the process does what was agreed?
• Have I left out the first version of the functions that are not yet necessary?

Next step: turn an idea into a controlled flow

If you already have a repetitive task in mind, the next step is to review your actual route before choosing tools or connections. So you can decide which part to automate, what information to circulate, and where to keep a human review.

If you want to raise that use case with clear scope, responsibilities and exceptions, you can explain your automation project with AI.