How Companies Integrate AI into Operational Workflows

Rather than completely transforming business processes, AI tools are being integrated into specific steps to improve automation, analytics, and service efficiency.

Decision Support and Data-Driven Insights

In many organizations, artificial intelligence functions more as a decision support system than as a decision-maker. Particularly in data-intensive sectors, machine learning algorithms analyze operational data to provide managers with faster and more measurable insights. According to Tolga Çelik, the executive overseeing digital transformation at Arçelik, AI is being used to optimize production lines. The system analyzes historical manufacturing data to detect patterns and alert operators about machines at higher risk of failure, allowing for better maintenance scheduling and uninterrupted production.

Customer Experience and Automated Response Systems

The banking and retail sectors are among the most active users of AI-driven customer engagement tools. QNB Finansbank reports using natural language processing (NLP) to enhance voice response systems, allowing customer inquiries to be handled more efficiently while minimizing the need for human intervention. Similarly, the e-commerce platform Hepsiburada uses AI-powered chatbots for first-contact resolution in customer support. Elif Günay, who leads customer relations at the company, explains that bots handle initial queries and seamlessly transfer more complex requests to human agents.

AI in Logistics and Supply Chain Operations

Artificial intelligence is reshaping supply chain management through predictive models that help optimize inventory levels, delivery routes, and shipping priorities. At Migros, AI modules integrated into distribution centers are supported by regional consumption data to ensure product availability on shelves. According to Mehmet B. Tan, the company's head of logistics, this approach not only minimizes product waste but also helps keep distribution costs under control.

Applications in Finance and Risk Assessment

In high-volume transaction environments such as insurance and banking, AI algorithms are now indispensable for credit scoring, fraud detection, and risk modeling. At Allianz Türkiye, an internal AI system evaluates individual applications using over 30 data points, automating risk analysis and enabling rapid approvals for low-value loans. In the fintech sector, companies like Papara monitor user behavior in real time to flag potentially suspicious activity, helping to mitigate fraud and strengthen compliance frameworks.

HR Automation and Internal Process Monitoring

Several subsidiaries under Koç Holding have started using AI-assisted systems in recruitment processes. Applicant resumes are scanned and matched with job requirements through algorithmic filters. Derya Karaca, an HR coordinator at the holding, states that AI tools accelerate hiring without replacing final human decision-making. Furthermore, internal feedback analysis powered by NLP allows HR teams to extract actionable insights from employee surveys and improve communication within organizations.

As artificial intelligence becomes a standard layer within modern enterprises, its role has shifted from an external innovation to an internal strategic capability. While human input is still central to many decisions, the impact of AI on speed, accuracy, and cost-efficiency is now measurable. These developments are pushing companies to treat AI not only as a digital investment but also as a core operational asset that enhances internal data flow and long-term adaptability.