Artificial Intelligence for Businesses: Where to Start

Artificial intelligence is no longer a topic reserved for large corporations. Small and medium-sized businesses are talking about it too. Many business owners want to benefit from the technology but are not sure where to begin. Artificial intelligence for businesses is not a magic solution. It is a tool that saves time when it is used in the right place and with the right preparation. In this article we suggest a step-by-step starting path for small and medium-sized enterprises and point out common mistakes and the limits that should be kept in mind.

Process first, artificial intelligence second

Artificial intelligence speeds up or simplifies a process that already exists. If there is no defined process, there is nothing to speed up. In a business where orders are written on paper, stock levels live in one person's head and customer records are spread across different files, an artificial intelligence tool has no data to work with.

The first step is therefore not artificial intelligence but digitalisation. The way work is done should be written down, and records should be kept in one place in an orderly manner. Artificial intelligence is of no use in a process that has not been digitalised, and it may even add to the existing confusion. Artificial intelligence and digital transformation are not alternatives to each other. One follows from the other. It should first be clear which work is done in which order and by whom, and only then should you ask which step of that work needs help.

Putting your data in order

The output of an artificial intelligence tool depends on the quality of the data it is given. A tool working with incomplete, inconsistent or outdated data produces results that are just as incomplete and misleading. The same customer recorded under several different names, or product codes written differently in every file, are typical examples. Records that exist only on paper or as scanned images are not in a form that tools can use easily either.

Putting data in order does not require a large project. A few questions are enough to get started:

• Which information is kept where, and who is responsible for it?
• Is the same information stored in more than one place, and which copy is current?
• Do records follow a common format and naming convention?
• Can past records be reached when they are needed?

As the answers become clear, a solid foundation emerges, not only for artificial intelligence but for day-to-day operations as well.

Choosing the right use case

There is no single answer to the question of how to use AI in a business, but the areas suited to a first step are fairly clear. Good candidates are tasks that are repeated often, cause limited harm when something goes wrong and produce results that a person can check easily. Common AI applications in businesses fall under the following headings:

• Repetitive work: Drafting similar e-mails, summarising long texts, classifying records.
• Document processing: Extracting information from documents such as invoices, order forms or contracts and transferring it to the relevant field.
• Answering customer questions: Drafting answers to frequently asked questions from the company's own knowledge source.
• Reporting: Summarising well-kept data and flagging notable changes.

By contrast, decisions that directly affect money, health, safety or legal obligations are not a suitable starting point. In these areas artificial intelligence should offer suggestions at most, and the decision should stay with a person. When choosing, it is better to start from where the business loses the most time than from what the technology happens to be capable of.

Starting with a small pilot

Instead of trying to change the whole business at once, it is healthier to begin with one task, one team and a limited period. A pilot shows how the tool behaves under real conditions and catches unexpected problems while they are still small. If it fails, the loss is limited. If it succeeds, it provides a concrete example for a wider rollout.

The scope of the pilot should be defined in writing from the start. It should be clear which task is targeted, who will use the tool, which data will be given to it and who will check the results. Keeping the old method available during the pilot is also a sensible precaution. Work does not stop if the tool fails to perform as expected, and the results of the two methods can be compared.

Measuring success

When a trial is not measured, judging whether it worked comes down to impressions. The current situation should be noted before the pilot begins. Simple indicators are enough, such as how long a task takes, how often mistakes occur and how quickly customers receive a reply.

At the end of the pilot, the same indicators are reviewed again. The time spent checking and correcting the output has to be counted alongside the time saved. If the tool produces quickly but every output is rewritten from scratch, there is no real gain. The experience of employees is a measure too. A tool that nobody uses adds nothing to the business, however good it looks on paper.

Involving employees

Artificial intelligence initiatives can fail for human reasons as well as technical ones. An employee who fears losing their job, or who feels the tool has been imposed on them, will not want to use it. The purpose should therefore be explained clearly from the outset. The goal is to reduce repetitive and tiring work, not the number of people.

The person who knows a job best is the one who does it every day. Employees can tell you which step takes time and where mistakes happen. Asking for their views when choosing the pilot area and evaluating the results leads to a better choice and makes the tool easier to adopt. Short training and written rules of use also reduce uncertainty. These rules should state clearly which information may be entered into the tool and who checks the output.

Data privacy and security

Information entered into an artificial intelligence tool may be sent to a system outside the business. Before customer details, price lists, contracts or employees' personal data are entered, it should be known how the tool stores that data and what it uses it for. Obligations arising from personal data protection legislation continue to apply when artificial intelligence is used. The terms of the tool should therefore be read, and where there is any doubt, sample or anonymised data should be used instead of real data.

The second issue is the reliability of the output. Large language models can produce information that is fluent but wrong, or even entirely made up. A reply to a customer, a quotation or a report should not be sent without being read by a person. Responsibility for the output lies with the business that uses the tool, not with the tool.

Off-the-shelf tool or custom solution?

General-purpose tools are a suitable first step for many businesses, because they are quick to start with and usually require less preparation. They can be used for writing, summarising and translating without any additional development. Their limitation is that they are not directly connected to the company's own data and workflow.

A custom solution comes into question when the job requires working with the company's own records, exchanging data with existing software or following a specific approval flow. A custom solution takes more effort, but in return it offers more control over where the data sits and who can access what. In most cases the sensible route is to gain experience with a ready-made tool and to consider a custom solution once the need has become clear. Whichever route is chosen, it is worth remembering that tools change over time and that it helps not to depend on a single provider.

Start small, move forward on solid ground

For small and medium-sized enterprises, the journey into artificial intelligence begins with order rather than technology. Digitalise your processes, tidy up your data, choose a single use case, try it with a small pilot, measure the result and involve your employees. Following this sequence helps avoid unnecessary spending and makes the real contribution of the tool visible. Carrying what you learn at each step into the next one, rather than rushing, tends to give more lasting results.

If you would like to move your processes into a digital environment or develop a solution tailored to your business, you can take a look at BYK Yazılım's custom software service and request a quote by telling us about your needs.