AI in business processes: where it really delivers value

1. AI cannot make up for a disorganised process

Artificial intelligence can process large volumes of data quickly, but it cannot decide on its own which way of working is the right one. If employees handle identical cases differently, if categories are unclear or the data is out of date, the results AI produces will be inconsistent too.

Imagine a company that wants to classify incoming requests automatically. If the same type of request is used across several categories and it is not clear who handles it, the model has no reliable pattern to follow.

That is why the sensible order is this: first an agreed process, then well-structured data and a digital workflow, and only then AI where it can eliminate specific manual tasks.

2. Four questions before implementation

Before choosing a use case, answer four questions:

• Does the task repeat often enough?
• Is there a correct or acceptable result that can be verified?
• What are the consequences of an error and who will review the result?
• How much time and cost does the task incur today?

If there are no clear answers to these questions, it is too early to choose a technology. The process and the expected result need to be defined more precisely first.

3. Use cases where AI often delivers a quick return

Reading documents

The data arrives in a scanned document or a PDF file and an employee retypes it into a form by hand. AI can read the data and prepare a proposed entry, which a person then checks. The saving can be measured in time per document and in the number of transcription errors.

Classifying and routing requests

AI can suggest the category, the priority or the person responsible. This approach works well when the categories are clearly defined and when the final decision is confirmed by the responsible person.

Searching internal sources

An employee asks a question in everyday words and the system finds the answer in policies, instructions and other approved sources. It is important that the answer cites its source and lets the user verify it.

Preparing drafts, summaries and translations

AI can prepare the first draft of an announcement, summarise a longer document or produce a translation. Because a person reviews the final text, the risk is usually manageable, and the saving shows up wherever there is a larger volume of repetitive work.

4. Where particular caution is needed

AI should not make independent decisions that have serious consequences for an individual, a client or the business. In such cases it can prepare information or a proposal, but the decision must remain with the responsible person.

Implementation is also often not worthwhile for very infrequent tasks, or where the content changes often but nobody maintains it. The model may work technically, but it will return out-of-date or unreliable answers.

5. Human review is part of a good solution

Reviewing an AI-generated proposal does not eliminate the time saving. The difference between creating content from scratch and checking a proposal that has already been prepared can be considerable. At the same time it significantly reduces the risk of an incorrect result reaching the end user.

The system must make clear what AI proposed, who checked the proposal and what the final decision was. That keeps the process traceable and makes it possible to improve it.

6. How to measure success

Define the metrics before implementation. Most often we track processing time, the proportion of AI suggestions users accept without changes, the number of errors and the actual use of the solution.

If amendments are frequent or users do not use the proposals, the data, the instructions or the chosen use case need improving. Even that result is useful, as it shows where the solution is not yet ready for wider use.

7. A BizIT example

For our client Svetkom we added a feature to the application for selling damaged vehicles that reads the vehicle details from a photograph of the vehicle registration document and prepares them for entry into the form. An employee checks and confirms the data. That means less manual retyping and less scope for typing errors, while the final decision remains with the user.