Generative AI: what can it do for your business?

Understand what the model actually does

A language model breaks data into units called tokens and learns patterns while it is trained. When asked a question, it produces a series of tokens compatible with the instructions and the context. This explains his ability to reformulate, summarize or write, but also his tendency to produce a convincing answer even when information is missing. We must therefore not confuse quality of writing with factual accuracy. A professional application should organize this check instead of just asking the model to never make a mistake.

Distinguish generation, search and classic automation

The three approaches complement each other. A classic rule calculates a discount deterministically. A search finds an existing invoice. The generation explains this invoice with words adapted to the customer. Replacing the calculation with a free response would introduce unnecessary uncertainty. Conversely, asking a list of conditions to include every possible wording in an email quickly becomes difficult. Proper slicing involves using the model to interpret and write, research to provide facts, and business code to apply decisions that require an accurate outcome.

Identify uses that create measurable value

A good first target brings together repetition, accessible data and a verifiable result. Summarizing reports, classifying sales requests or preparing a support response often meets these criteria. Evaluate each idea on four axes: time consumed today, cost of an error, frequency of need and ease of control. A spectacular but rarely used feature may be less valuable than a reliable summary integrated into an advisor's daily screen. The comparison should cover the entire process, including data preparation and proofreading time.

Detailed example: a customer support assistant

Let's imagine an advisor who receives a conversation of twenty messages. The application offers a summary including the initial problem, the checks already carried out and the missing information. She then researches the procedures to which he has access and prepares a draft. The advisor sees the passages used, corrects the text then sends the response himself. The system maintains the link with the ticket and the corrections made. This path reduces screen changes while maintaining clear human responsibility. It does not transform a generated text into official truth simply because it is well formulated.

Build actionable instructions

A useful guideline indicates the role, objective, available data and expected format. To extract a request, specify for example the need, deadline and budget fields, explicitly authorizing an absent value. Add examples where the customer does not give a date: the model should leave the field empty rather than infer an emergency. When a JSON is expected, validate its structure on the server side. Output conforming to the diagram may still be fundamentally wrong: structure and correctness are two different checks, which must be tested separately.

Choosing between context, RAG and model adaptation

If the task uses some recent information, conveying it in context may be sufficient. When the document base is large or frequently modified, a RAG mechanism searches for useful passages before generation. An adaptation of the model can help for specialized behavior or a recurring format, but it does not replace an up-to-date fact base. Before making the architecture more complex, build a small set of examples and measure what a well-designed instruction already allows. The most sophisticated solution is not automatically the most reliable or the cheapest.

Protect data and permissions

Start by inventorying the information sent to the supplier: internal documents, personal data, identifiers or technical secrets. Reduce the data to what is strictly necessary and check the retention and use settings applicable to the chosen service. Control access before building the context, not after the response. A user should never obtain a summary of a file that they do not have the right to open. The logs must facilitate diagnosis without systematically copying all sensitive conversations. Also define who can view these logs and for how long.

Evaluate with a representative set of cases

Prepare requests that are frequent, ambiguous, incomplete and intentionally misleading. Associate each case with an observable expectation: correct fields, relevant citation, lack of invention or refusal of a prohibited action. A trades person must be involved in the scoring, as a technically valid answer may be unusable in actual work. Track accuracy, response time and cost separately. Maintain reference cases to compare new guidelines and model changes. Without this basis, a change that improves a few demonstrations can quietly degrade the most important requests.

Control costs and deadlines

The cost depends in particular on the volume of context, the size of the responses, the model and the number of calls necessary. Measure the cost per successful task rather than the price of a single call. A system that starts over four times or wastes time rereading may be less profitable than a seemingly more expensive solution. Limit unnecessary contexts, reuse appropriate results, and set ceilings. Also plan a maximum time limit and a backup route: the advisor must be able to continue working if the supplier is unavailable.

Moving from a prototype to a usable product

A prototype demonstrates a possibility; a product must manage errors, permissions, monitoring and user feedback. Deploy to a limited area first and document what the assistant can do. Clearly display the content to be checked and avoid hiding a failure behind a generic response. Measure the time actually saved after several weeks of use. Tuplify Studio can link this functionality to a CRM, portal or mobile application, starting with a complete journey whose acceptance criteria are defined with the people who will use it.

Practical workshop: prepare an extraction assistant

Take a small set of anonymized sales emails and define five fields: company, requirement, deadline, budget and questions to clarify. For each message, manually write the expected response. Include a missing budget, an ambiguous date, multiple requests and a message with no real plan. Then ask the model to produce the fields without filling in the missing information. Validate the formats with code and compare the background with your reference. Classify errors: invented information, forgotten data or misinterpretation. Modify only one part of the instruction at a time and retry all cases. This exercise makes progress observable and quickly shows whether AI is really improving a task or whether a classic rule is enough.

Frequently asked questions: private model, accuracy and liability

Should you host the model yourself? Not systematically. Compare data sensitivity, supplier conditions, operating skills, cost and measured quality. Internal hosting does not automatically make the answers accurate or the application secure.

Can we get zero errors? Instead, we must define tolerable errors, controls and situations where a person takes control. A high-consequence task requires an appropriate level of verification.

Is AI replacing business software? It can enrich the interface and certain treatments. Permissions, exact calculations, transactions and tracking obligations remain the responsibilities of the application. The model should not decide on an authorization or amount on its own just because it can produce a convincing explanation.