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 the companies surveyed had yet to show tangible value from AI. We break the Turkish picture down by sector and size in AI adoption in Türkiye, collect the common reasons projects fail to deliver in why AI projects fail, and summarise the gains measured in studies in AI productivity impact research.
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. If you want concrete ideas, browse our 30 AI use cases in business by department and sector, and choose between candidates with an AI use case prioritisation matrix. 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.
At the decision and governance stage, first test whether the task really needs AI with AI vs rule-based automation, and read vendor offers against our checklist for evaluating AI project proposals. As use spreads, a written company AI acceptable use policy sets the rules, and companies selling into the EU should read our guide to the EU AI Act. As tools spread, human–AI collaboration at work explains how roles, approval steps and job descriptions change.
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
- By department: AI in sales to cut admin and follow-up work, AI in B2B marketing for content and campaigns, AI in finance and accounting for invoices, reconciliation and close, AI in human resources for staff questions and personnel files, AI in procurement for supplier and order follow-up, AI management reporting for commentary on the numbers, and AIOps for IT operations to cut alert noise.
- Everyday office work: AI email reply drafting, AI proposal writing and AI meeting notes and summaries for decisions and action lists.
- Production and maintenance: AI in manufacturing: examples across the factory, AI production scheduling for realistic schedule inputs, and an AI assistant for maintenance technicians that answers from manuals.
- By sector: AI in hospital operations, AI for accounting firms, AI in agriculture, AI in local government and AI in hotels and hospitality.
- Company knowledge and infrastructure: AI enterprise search for meaning-based search across documents, the vector database underneath search and RAG, AI agents that act across several systems, and the prompt injection attacks that target such assistants.
- Customers and communication: customer service chatbots, agent-assist AI for customer service that works alongside the agent, and call centre speech analytics.
- Turkish language and speech: the Turkish natural language processing behind chatbots and analytics, WhatsApp business chatbots that answer in the channel customers already use, and Turkish speech to text for meeting and call recordings.
- Warehouse and operations: speech recognition on the shop floor, with hands kept free, in voice picking in the warehouse, and other uses from warehouse to dispatch in AI in logistics and warehousing.
- Health and safety: AI PPE detection.
- Documents and inbox: AI document analysis, AI email classification for routing requests in shared mailboxes, and AI contract review for flagging risky clauses.
- Forecasting and planning: after demand forecasting, retail inventory optimisation for store-level orders and transfers, and electricity load forecasting for hourly load from meter data, and AI dynamic pricing for setting prices from demand and stock data.
- Training data and vision in production: data labelling for AI, on preparing examples before training a custom model, and AI fabric defect detection, computer vision applied to textiles; for monitoring and retraining a model once it is live, see MLOps and the model lifecycle.
- Energy and retail analytics: electricity theft detection, anomaly detection applied to the grid, and retail people counting for measuring store conversion.