AI Integration: Where to Start?

AI tools are everywhere now: systems that write text, summarise documents, answer questions and interpret images can be tried out in a few minutes. That ease, together with the pressure of "we need to do something too", pushes businesses to start with the technology. Yet successful integrations almost always start with a business problem.

In this post, we set out the steps you can follow to bring AI into your business processes in a realistic and safe way.

Problem first, model second

"Which AI model should we use?" is usually the wrong place to start. Better questions are:

• Which repetitive information work takes up most of our team's time?
• In which tasks do delays or errors directly affect customers?
• How do we do this work today, and how do we define a good result?

Once these questions have clear answers, choosing the right technology is much easier. Sometimes it turns out that the answer is not even AI, and that simple automation will do.

What makes a good first use case?

What to look for in a first project:

• It comes up often and takes time.
• Text- or document-heavy: Emails, requests, contracts, product information.
• The cost of a mistake is low, or a final check by a person can easily be added.
• Measurable results: For example time taken, accuracy or how quickly customers get a reply.

Example use cases

• Sorting incoming customer emails by subject and routing them to the right team
• Summarising long documents and listing the key points
• Preparing a first draft of product or service descriptions
• Searching the company's internal knowledge base in everyday language
• Pulling a to-do list out of meeting notes

Preparing your data

AI systems are only as good as the data you feed them. Before you start, be clear on the following:

• Where is the data? In email inboxes, shared folders, different programs?
• How well organised is it? Incomplete, duplicated or out-of-date records spoil the results.
• Who can access it? An AI solution should not show users information they are not authorised to see.

KVKK and data security

Most AI services run in the cloud and send your data to their own servers to process it. If personal data is involved, pay attention to the following:

• What data is being sent? Do not send personal information that is not needed; where possible, mask or anonymise it.
• Where is the data processed? Transferring data abroad creates separate obligations under KVKK, Turkey's personal data protection law.
• How does the provider use the data? Check the contract to see whether your data is used to train the model.
• Logging and auditing: It should be possible to trace which operations were carried out by AI.

Model choice and cost

Broadly speaking, there are two routes:

• Ready-made model services (API): Quick to start with and no infrastructure to worry about; you pay per use. As usage grows, so does the cost.
• Models running on your own infrastructure: You gain more control over your data, but the burden of set-up, hardware and maintenance shifts to you.

For most businesses, the sensible starting point is to run a small pilot with ready-made services and work out the cost from real usage data.

For drafting and approval workflows developed on request, explore AI Content CRM.

Designing human oversight

AI output can look convincing and still be wrong. That is why, especially for content that goes out to customers, it is important to set up a "draft – review – approve" flow: the system suggests, a person decides.

We apply the same principle to our own product, ALGO. The language courses in the app were prepared with AI and go through AI reviews; a teacher review is in progress. That is why the courses are marked "Beta", and in lessons opened with an adult account, users can report any error they spot with "Report a problem". The result: fast production, but a transparent and auditable process.

Pilot, measurement and roll-out

1. Choose a single use case and write down your measure of success at the outset.
2. Try it with a small group of users, with real business data, over a few weeks.
3. Measure and compare: Has the time taken come down? What has happened to the error rate? What do users think?
4. Decide: Roll it out, improve it or move on to another use case.

Common mistakes

• Starting with the technology: Choosing a tool before defining the problem.
• Wanting everything at once: Trying to build a large system in one go.
• Skipping human oversight: Sending output straight to customers.
• Leaving data rules until later: Not designing for KVKK and access permissions from the start.

Conclusion

When AI integration starts with the right problem and a small pilot, it delivers fast, measurable results. Preparing your data, thinking about KVKK rules from the start and making human oversight part of the process are what successful projects have in common.

If you would like to explore together where AI could add value in your business, you are welcome to write to us.