Identifying AI use cases: Strategies for digital transformation

To efficiently identify AI use cases, it helps to take a look at the various business areas. Here is an overview of the most important use cases for AI:

Artificial intelligence increases conversion rates in marketing through precise customer targeting and automates time-intensive routine tasks in sales. By linking CRM data with generative models, measurable competitive advantages are created. The most important AI use cases include:

• AI Personas & Synthetic Users: Beyond creating realistic customer profiles, AI models today enable the simulation of customer feedback. Companies use these "synthetic users" to test marketing campaigns or product ideas in advance without tying up real customer resources.
• Real-Time Hyper-Personalization: AI analyzes user behavior in milliseconds to create tailored customer journeys. This ranges from dynamic website content to automated, hyper-personalized email sequences that significantly increase customer retention and customer lifetime value (CLV).
• Scaled Content Production & Brand Governance: Modern AI workflows not only produce texts and images, but also ensure through integrated brand guidelines that all assets remain consistent. Automation enables multimodal content delivery at scale with minimal manual effort.
• Predictive Lead Scoring: By analyzing historical sales data, AI identifies the leads with the highest likelihood of closing. Sales teams can thus focus their resources on the most promising contacts, drastically increasing efficiency in the sales funnel.

In customer service, routine inquiries are resolved autonomously, while service staff are empowered to become experts through real-time intelligence. This reduces service costs while simultaneously increasing customer satisfaction. The key AI use cases are:

• GenAI Self-Service Agents: Modern AI agents leverage generative AI and RAG (Retrieval-Augmented Generation) to answer complex customer inquiries precisely based on internal knowledge databases. They guide users through transactions (e.g. returns or rebookings) and hand over seamlessly to human colleagues only when escalation is required.
• Agent Copilots & Real-Time Assistance: AI supports support teams during conversations through live transcription and context-based solution suggestions. A key lever is automated call summarizing: immediately after a conversation ends, the AI generates a structured summary for the CRM, massively reducing post-processing time.
• Intelligent Process Orchestration & Triaging: Instead of simple ticket assignment, AI analyzes the intention (intent) and sentiment of inquiries in real time. It automatically prioritizes critical cases and routes them to the most specialized agents, while standard processes such as address changes are handled completely in the dark (without manual intervention).

In the smart factory, AI is transforming traditional manufacturing into a learning ecosystem. By analyzing machine data in real time, companies are increasing their overall equipment effectiveness (OEE) while reducing energy costs and material waste. The most important AI use cases in industry are:

• Predictive Maintenance: Instead of reacting to failures, AI calculates the exact remaining service life of components (e.g., drill heads or bearings). Automated triggering of spare parts orders ensures that components are replaced at exactly the right time, eliminating unplanned downtime.
• Predictive Quality & Visual Inspection: AI systems analyze process parameters such as pressure, temperature, or welding data in real time to predict quality defects before they occur. Supplemented by computer vision, AI cameras identify the smallest surface defects or assembly errors faster and more accurately than the human eye, which significantly reduces scrap.
• Anomaly detection & condition monitoring: By monitoring vibrations and energy flows, AI detects deviations from normal operation that indicate wear or inefficient processes. This enables proactive intervention in plant control before expensive consequential damage occurs on the production line.
• Intelligent demand forecasting: In supply chain management, machine learning models forecast future demand based on historical data, market trends, and seasonal fluctuations. This enables precise production planning and inventory optimization, reducing capital tied up and shortening delivery times.

In modern logistics, AI functions as a central nervous system that synchronizes global goods flows and dissolves rigid processes. The real-time processing of massive amounts of data gives rise to the following value-adding applications:

• Dynamic Route and Tour Optimization: Algorithms calculate delivery routes by incorporating real-time traffic data, weather conditions, and vehicle capacities. For electric fleets, charging cycles and ranges are integrated in such a way that economic goals and ecological sustainability (CO2 minimization) are achieved simultaneously.
• Predictive Inventory Management & Demand Sensing: Beyond classic forecasting, AI uses "demand sensing" to predict demand peaks by analyzing unstructured data (e.g. social media trends) before they appear in the ERP system. This enables AI-supported warehouse space optimization that strategically positions fast-moving items for minimal picking distances.
• Autonomous Mobile Robots (AMR) & Vision AI: Modern logistics centers rely on AMRs that use deep learning to capture their environment and autonomously identify or transport goods. Unlike conventional systems, these units operate completely freely in space and optimize material flow through intelligent obstacle avoidance.
• Document AI for the Supply Chain: AI models extract information from bills of lading and customs documents. This automates the administrative processing of international supply chains and drastically reduces error rates as well as throughput times in the back office.

In healthcare, AI serves as a highly precise assistance system that supports doctors in decision-making without replacing human expertise. By analyzing complex patient data, treatments become safer and more individualized. The most important applications are:

• AI-Supported Diagnostics & Computer Vision: Highly specialized algorithms analyze radiological or dermatological image data in seconds. They identify the smallest anomalies (e.g. in the early stages of carcinomas) that are difficult for the human eye to detect, thus providing an objective second opinion for early detection.
• Personalized Therapy & Precision Medicine: By evaluating genomic markers and biometric data, AI creates individually tailored treatment plans. In oncology in particular, this enables targeted therapies that are precisely matched to the patient's genetic mutation and minimize side effects.
• Accelerated Drug Discovery: AI models simulate the interaction of molecules and massively shorten the lengthy search for new medications. What previously took years in the laboratory can now be validated in a fraction of the time through generative models in virtual simulations.

In human resources (HR), AI acts as a strategic partner, reducing administrative burdens and shifting the focus back to interpersonal relationships. Data-driven analytics make talent management more objective, faster, and more personalized. Key applications include:

• Automated candidate experience & communication: Beyond simply generating text for job ads, AI optimizes all communication with applicants. Large Language Models (LLMs) adjust tone and content (e.g., switching from formal to informal address) in real time and create highly personalized outreach campaigns that significantly increase response rates in active sourcing.
• Intelligent recruiting & skill-based matching: Modern systems analyze resumes and portfolios not only for keywords, but also understand the context of experience. This enables precise matching of applicant profiles with actual role requirements and identifies internal talent for new vacancies (internal mobility) even before external advertising.
• People Analytics & Predictive Retention: Instead of purely subjective employee evaluations, AI uses aggregated metrics on project performance and feedback. Predictive models also help identify turnover risks early on (predictive retention) by identifying patterns in employee satisfaction and suggesting proactive measures to retain employees.
• HR self-service & AI agents: Intelligent agents answer complex queries about vacation entitlements, benefits, or HR policies around the clock. These "internal service agents" relieve the HR department of repetitive queries and provide employees with immediate, consistent solutions.

In the energy sector, AI is the key to managing the volatility of renewable energies and protecting critical infrastructures. The intelligent networking of producers and consumers gives rise to high-performance systems. The key applications include:

• Smart Grid & Intelligent Load Management: AI systems optimize energy flow in residential and industrial buildings by analyzing temperature data, occupancy times, and user preferences. This enables automated control of heating and cooling systems (demand response), which smooths peak loads, optimizes self-consumption, and significantly reduces energy costs.
• Predictive Grid Analytics & Monitoring: Beyond pure management, AI identifies inefficient consumption patterns and anomalies in the distribution network. Companies can thus take targeted measures to improve efficiency and anticipate potential grid overloads before they jeopardize supply security.
• Cyber Resilience for Critical Infrastructures (KRITIS): In the face of increasing threats, AI continuously monitors data traffic patterns and system diagnostics of power grids. The algorithms identify zero-day attacks in real time and respond autonomously – for example through the immediate isolation of affected segments or the automated deployment of security patches – to guarantee the stability of the national energy supply.
• Virtual Power Plants (VPP): AI coordinates decentralized energy generators such as photovoltaic systems and wind farms. Through precise forecasting of generation and demand, AI ensures optimal energy marketing and a stable grid frequency.

In IT infrastructure and software development, AI acts as a catalyst for agility. It frees experts from technical debt and enables direct, intuitive access to organizational knowledge. The key applications include:

• Automated Code Migration & Legacy Modernization: AI models efficiently translate outdated code (e.g. COBOL or monolithic Java) into modern architectures and languages. This massively reduces the risk in refactoring projects and accelerates the technical transformation of entire system landscapes.
• Conversational BI ("Chat with your Data"): Through the use of Retrieval-Augmented Generation (RAG), employees can ask complex questions directly of their data assets. AI acts as a bridge between databases and users by generating SQL queries in the background and presenting results as comprehensible business insights.
• AIOps & Self-Healing Infrastructures: AI proactively monitors IT systems and detects anomalies in load behavior or security patterns before outages occur. Automated workflows can autonomously resolve simple disruptions (self-healing), which increases availability and relieves the operations team.

In research and development (R&D), AI pushes the boundaries of what is possible by massively shortening the timespan from the initial idea to a market-ready prototype. Through the use of predictive models and generative systems, innovation becomes plannable and scalable.

• Generative Design & Topology Optimization: Instead of manual drafts, AI generates thousands of design variants based on target parameters such as material strength, weight, and cost. This enables the development of highly complex components that are often lighter and more stable than classically engineered parts and can simultaneously be manufactured more material-efficiently.
• Physics AI & Virtual Prototyping: Modern neural networks accelerate complex physical simulations (e.g. aerodynamics, thermodynamics, or material fatigue) by multiples compared to conventional solvers. This reduces dependence on costly physical test series and allows for extremely rapid iteration in product development.
• Accelerated Materials Research: By simulating molecular properties at the atomic level, AI helps to specifically develop new materials or chemical compounds with targeted properties (e.g. higher conductivity or better biodegradability), rather than relying on lengthy trial-and-error laboratory tests.