AI in Business: A Guide to Use Cases, Data Security and ROI

Artificial Intelligence

Where should a business start with AI? Our guides on RAG, enterprise LLMs, data security, OCR, computer vision, demand forecasting and ROI, in one place.

AI is no longer only a concern for big tech. Systems that read invoices, spot defective parts on the line, forecast demand or answer questions from company documents are now being deployed by mid-sized businesses too. In Türkiye, though, adoption is still early: TurkStat's Artificial Intelligence Statistics 2025 show that the share of enterprises with 10 or more employees using any AI technology rose from 2.7% in 2021 to 7.5% in 2025. And using AI is not the same as getting value from it. In BCG's Where's the Value in AI? survey of 1,000 executives in 59 countries, 74% of companies had yet to show tangible value from AI.

This hub gathers our business AI articles into a reading path from first steps to decision. Begin with where to start with AI in business and how to choose a first project. Straight after that, read how to measure AI project ROI before work begins, and what the risks are when staff paste company data into ChatGPT.

Going deeper, the use cases take centre stage. For documents and knowledge, read about RAG and enterprise LLM assistants, how to keep those assistants reliable by reducing AI hallucinations, and OCR invoice processing for finance teams. In production and operations, the key articles are computer vision quality inspection, AI demand forecasting and anomaly detection. When general-purpose models are not enough, custom AI model training explains when and how to train your own.

For delivery, see our pages on AI integration, enterprise LLM assistants, computer vision and document OCR. If you want to see AI in an everyday workflow, the Smart360 family offers message summaries and invoice attachment extraction in SmartMail, and lets you ask questions of uploaded documents in SmartFiles. Before you start, pick a task that repeats often, is hard to reduce to fixed rules, and for which you already hold the data.

Reading list by use case

Customers and communication: customer service chatbots and call centre speech analytics.
Health and safety: AI PPE detection.
Documents: AI document analysis.

Start here

AI in Business: Where to Start and How to Choose Your First Project

Start with AI in business on one repetitive task, measured before and after. A seven-step framework for choosing, testing and scaling your first AI pilot.

Read the guide

All guides in this topic (2)

Artificial Intelligence

Retrieval Augmented Generation for the Enterprise: How an LLM Assistant Answers from Your Own Documents

How retrieval augmented generation lets an enterprise LLM assistant answer from your own documents, cite sources and respect permissions, in seven steps.

24 September 2026 · 9 min read

Questions we hear most often

Frequently Asked Questions

Which AI project should we start with?

Your first project should be a frequent, measurable task for which you already have data: invoice reading, document classification, demand forecasting or visual quality inspection, for example. Keep the scope narrow, write down the success criteria at the outset, and judge the result side by side against the method you use today before scaling up.

Is it safe to put company data into tools like ChatGPT?

Not without rules. Staff sharing customer, personnel or financial data through personal accounts creates data protection and trade secret risks. You need a usage policy that states which data may be shared with which tools, business accounts, and where necessary an enterprise assistant that keeps the data within the organisation's control.

What is RAG and why does it matter?

RAG (retrieval-augmented generation) means the AI first retrieves the relevant passages from your own documents and then bases its answer on them. The assistant answers according to your procedures, contracts and product documentation rather than general knowledge, can cite its sources, and is noticeably less likely to invent an answer.

How do you measure the ROI of an AI project?

Measure the current state before the project starts: how many minutes a task takes, the error rate, missed sales or scrap, for instance. Take the same measurement after go-live and compare the difference with development, infrastructure and maintenance costs. A project that starts without a measurable target cannot demonstrate a return later.

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