Value-Adding
Data & AI Use Cases
When it comes to driving valuable innovation and increasing a company’s competitiveness in the global market, ‘Data & AI First’ has become the core pillar of the world’s best-performing companies.
Essentially, applying the right Data Analytics & AI Use Cases is core for solving business problems and driving growth opportunities.
Data & AI Use Case Library
Discover value-adding data and AI applications in the following and inspiring examples from various business areas in our Use Case Library.
What is a Use Case for Data & AI?
A “use case” is a targeted application of data and AI methods to solve a specific problem that leads to one or more measurable and value-adding results.
The outcomes of a use case can range from enhancing an existing process or product to developing entirely new types of services. A Data & AI Use Case may function as a standalone application or form part of a larger system, such as machine-learning-based object recognition in autonomous vehicles. However, it can also be business analytics reporting, for example as a dashboard, or the use of generative AI (GenAI).
These use cases typically progress through several life cycle phases, starting from the initial idea and culminating in an operationally implemented solution.
In this context, terms like ‘Data or AI product’ and ‘AI solution’ are often used interchangeably.
How is a Use Case structured?
A use case is therefore a brief and specific description of a challenge that is to be solved with the help of data and AI in order to achieve new added value.
Ideally, this is recorded in a standardized template or a so-called Use Case Card.
The most important points are:
- Description of the problem/challenge
- Objective
- Benefit / Value drivers
- Description of the process or product in which the use case is to be applied
- How the result will be used
- Core target group
This can be further determined by company-specific meta data.
You can find more examples here and here
From a technical perspective, the core of the data and AI application is fromed by an algorithmic model, e.g. a machine learning model, and is surrounded by other software components such as data input and output, system logging and monitoring.
Documenting these technical components of a specific use case in a model card as well as the used and produced data of a use case in data cards is a helpful extension of a use case, especially with regard to AI governance issues.
Why are Use Cases important for the AI strategy?
Use cases are essential for translating an AI strategy into actionable implementation, as they convert data and AI technologies into concrete applications. They act as a bridge between strategic objectives and operational execution by clearly defining which resources – from skilled personnel to organizational structures and technological systems – are required and how they must work together effectively.
Furthermore, use cases highlight where and how a company can achieve measurable value through the use of data and AI. They provide insights into how products, services, or processes can be improved, thereby helping to unlock the full potential of the AI strategy.
Potential Use Cases span the entire value chain.
Examples range from text, audio, and image generation in marketing via generative AI to business forecasting in controlling, personalized product recommendations in web shops or predictive maintenance in the production line … and many more.
Using data ANALYTICS & AI along the value chain
Data & AI serve as drivers for the redesign of corporate values. Their potential applications are diverse and influence all aspects of a company’s value creation.
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| Automated Invoice Matching | Fraud Detection In Financial Transactions | Cash Flow Forecasting |
| AI helps you streamline invoice processing by automatically matching invoices with purchase orders and receipts, flagging discrepancies for review. This reduces manual errors, accelerates payment cycles, and improves efficiency. | AI supports you by monitoring financial transactions for unusual patterns, such as duplicate payments or unauthorized activities. By detecting fraud early, you can protect your business from financial losses and reputation damage. | AI assists you in predicting cash flow trends by analyzing historical and real-time financial data. This allows for better budgeting, liquidity management, and strategic financial planning. |
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| Automated Applicant Screening | Employee Retention Analysis | Personalized Employee Learning Paths |
| AI helps you streamline recruitment by analyzing resumes, matching qualifications with job requirements, and ranking candidates. This accelerates the hiring process and ensures you find the best fit for your team. | AI supports you by analyzing employee engagement, performance trends, and feedback to identify individuals who may be at risk of leaving. This enables you to take proactive steps to improve satisfaction and retention. | AI assists you in developing tailored training programs for employees by analyzing their skills, career goals, and development needs. This fosters professional growth and boosts job satisfaction. |
| –> Further info | –> Further info | –> Further info |
| Automated IT Incident Management | Predictive System Monitoring | IT Help Desk Support |
| AI helps you automate the identification and resolution of IT incidents by analyzing logs and alerts in real-time. AI-powered systems can prioritize critical issues, suggest solutions, and even resolve problems autonomously, reducing downtime and improving system reliability. | AI supports you by monitoring system performance and predicting potential failures or bottlenecks before they occur. By analyzing historical data and usage patterns, AI enables proactive maintenance, ensuring seamless operations. | AI assists you in improving IT support by using intelligent chatbots and virtual agents to handle common IT requests, such as password resets or software installations. This reduces the workload on IT teams and accelerates issue resolution for end-users. |
| Supplier Risk Analysis | Demand Forcasting | Automated Contract Review |
| AI helps you assess supplier performance by analyzing their financial health, compliance records, delivery reliability, and geopolitical risks. With predictive analytics, you can proactively identify vulnerabilities in your supply chain, ensuring stability and reducing potential disruptions before they occur. | AI supports you by predicting future demand using historical data, market trends, seasonality, and external factors such as economic conditions. By improving accuracy in forecasting, you can optimize procurement schedules, avoid overstocking, and reduce waste. | AI assists you by automating the tedious process of contract analysis. It scans legal documents for potential risks, missing clauses, or non-compliance issues, highlighting areas that need attention. This speeds up contract negotiations, reduces legal risks, and ensures compliance with procurement standards. |
| Automated Literature Analysis | Predictive Design Optimization | Experiment Automation |
| AI helps you analyze and summarize vast collections of scientific literature, technical documents, and patents. By extracting key insights and identifying trends, it enables researchers to bypass the time-consuming manual review process. This ensures your team stays updated on the latest developments and can focus on creating innovative solutions faster. | AI supports you by running simulations to test and predict how product designs will perform under various conditions, such as stress, temperature, or usage patterns. This reduces reliance on physical prototypes and accelerates the iteration process, leading to more cost-effective and efficient product development cycles. | AI assists you in automating laboratory experiments, using machine learning and robotics to optimize conditions like temperature, pressure, or chemical compositions in real-time. This significantly speeds up processes such as drug discovery, material testing, or engineering experiments, reducing both time and costs. |
| Route Optimization | Inventory Optimization | Supply Chain Risk Prediction |
| AI helps you calculate the most efficient delivery routes by taking into account real-time traffic data, weather conditions, fuel costs, and delivery schedules. This reduces transportation costs, improves on-time delivery rates, and minimizes the environmental impact of logistics operations. | AI supports you in maintaining the right inventory levels by predicting demand fluctuations and identifying patterns in sales data. This helps you prevent stock shortages or excess inventory, leading to improved operational efficiency and cost savings. | AI assists you by analyzing global data sources, such as weather reports, political events, and economic trends, to predict potential disruptions in your supply chain. This enables you to take proactive measures, such as diversifying suppliers or rerouting shipments, to minimize risks. |
| Predictive Maintenance | Quality Control Automation | Energy Optimization |
| AI helps you monitor machinery and equipment in real-time, analyzing sensor data to predict potential failures before they occur. By recommending maintenance only when needed, AI minimizes unplanned downtime, extends the lifespan of machinery, and reduces operational costs. | AI supports you by using advanced computer vision systems to inspect products on production lines for defects or inconsistencies. This ensures that only high-quality products move forward, reducing waste, improving efficiency, and boosting customer satisfaction. | AI assists you in monitoring and optimizing energy usage throughout your manufacturing operations. By analyzing consumption patterns, it suggests ways to reduce energy waste, such as rescheduling processes during off-peak hours or optimizing energy-intensive operations. |
| Dynamic Pricing Models | Market Segmentation | Campaign Performance Prediction |
| AI helps you adjust your pricing strategies in real-time by analyzing factors like demand, competitor pricing, and market conditions. This enables you to remain competitive, maximize profits, and respond quickly to changes in the market. | AI supports you in identifying distinct customer segments by analyzing demographics, purchasing habits, and behavior patterns. This allows you to design more focused marketing strategies, ensuring your campaigns resonate with the right audience. | AI assists you in forecasting the success of marketing initiatives by analyzing past campaign data, customer responses, and external factors. With this information, you can refine strategies, allocate budgets effectively, and maximize ROI. |
| Customer Support Automation | Voice of the Customer Analytics | AI-Powered Virtual Agnets |
| AI supports you by automating customer service interactions through intelligent chatbots and virtual assistants. These tools handle routine queries, provide accurate information instantly, and escalate complex issues to human agents, enhancing efficiency and customer satisfaction. | AI helps you understand customer sentiment and needs by analyzing feedback from reviews, surveys, and social media posts. These insights enable you to address pain points, improve your offerings, and stay ahead of competitors. | AI enables virtual agents to handle routine customer inquiries instantly, such as order tracking or basic troubleshooting. This reduces response times, enhances customer satisfaction, and frees human agents to focus on complex issues. |
Find more Data & AI Use Cases
Would you like to discover more potential AI solutions along the value chain? Then take a look at our Data & AI Use Case Library – An interactive portfolio of more than 200 use cases from various business functions and industries and is therefore a great source of inspiration to discover new opportunities for the use of AI in organizations.
Benefits of Data & AI for organizations
For companies, Data & AI solutions deliver benefits on two levels by streamlining internal operations and processes while propelling the creation of new and innovative offerings.
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Task Automation
AI enables the automation of repetitive and time-intensive tasks that were previously performed manually by humans. This increases efficiency, reduces errors, and allows employees to focus on more strategic and value-adding activities.
Example: A customer service company uses AI-powered chatbots to automatically handle frequently asked questions, such as inquiries about orders, returns, or general support issues. This relieves support staff, enabling them to address more complex customer problems.
Related questions:
- Does the application take over the manual task completely or only partially?
- How much time is spent on this task on average and how much does this time costs?
Improved Resource Consumption
Data & AI enables companies to use their resources more efficiently by identifying patterns, optimizing processes, and minimizing the consumption of time, energy, or materials. This leads to cost reductions and more sustainable operations.
Example: A logistics company uses AI to optimize delivery routes in real-time based on traffic data and weather conditions. This reduces fuel consumption and delivery times, lowering operational costs and environmental impact.
Related questions:
- How much can be saved per event through data-driven improved resource consumption?
- Which resources are most consumed in our processes, and what are the associated costs?
Increased Speed of Operations
AI enhances the speed of business operations by streamlining workflows, automating processes, and enabling faster decision-making. This allows companies to reduce time-to-market, improve responsiveness, and achieve operational efficiency.
Example: A manufacturing company implements AI-driven predictive maintenance systems that detect potential equipment failures before they occur. This reduces downtime, ensuring faster production cycles and consistent output.
Related questions:
- Does the application speed up the process (same output in a shorter time frame) or increase operational efficiency (higher output in the same time frame)?
- How much could be saved through the increased speed per event?
Better Informed Decision Making
AI enhances the ability to gather, analyze, and interpret data, providing better insights and supporting informed decision-making. By delivering more accurate and timely information, AI helps businesses reduce uncertainty, make strategic decisions faster, and improve overall performance.
Example: A manufacturing company implements an AI-powered production monitoring system that consolidates real-time data from machines across the factory floor. The system identifies inefficiencies and predicts potential bottlenecks in production. With these insights, managers can proactively adjust workflows and maintenance schedules, reducing downtime and increasing operational efficiency.
Related questions:
- Does the application gather and evaluate data so that information on specific topics is better than before, and does this result in a better decision-making process?
- Do these improved decision-making processes lead to increased profitability?
- How can the enhanced access to information be leveraged to gain a competitive advantage or unlock new opportunities?
Risk Reduction
AI helps businesses reduce operational risks by identifying potential issues, predicting outcomes, and enhancing monitoring capabilities. By proactively addressing risks, companies can avoid costly disruptions, improve compliance, and safeguard their operations and reputation.
Example: A financial institution uses AI to detect fraudulent transactions in real-time by analyzing patterns and anomalies in large datasets. This prevents financial losses and ensures regulatory compliance, reducing overall operational risks.
Related questions:
- Can operational risk be reduced by the use of an AI application?
- How can this risk reduction be translated into processes that save costs or add profits?
- Which specific areas (e.g., compliance, fraud detection, safety) can benefit the most from AI-driven risk mitigation?
New products or services
AI enables businesses to innovate and create entirely new products or services by leveraging advanced technologies such as machine learning, natural language processing, and computer vision. These innovations open up new revenue streams, attract customers, and position companies as leaders in their industries.
Example:
A healthcare company develops an AI-powered virtual health assistant that offers personalized health recommendations, tracks patient progress, and provides reminders for medication. This service not only enhances patient care but also creates a new subscription-based revenue model, driving additional profits and customer loyalty.
Related questions:
- Which new products or services can be developed using AI to create additional profit streams?
- How can these innovations differentiate the company from competitors and meet unmet customer needs?
- What is the projected market demand and potential profitability of these new offerings?
New business models
AI enables the creation of innovative business models by transforming traditional value chains, unlocking new revenue streams, and facilitating unique customer experiences. AI-driven business models often capitalize on data insights, automation, or predictive capabilities to deliver unprecedented value.
Example:
A transportation company develops an AI-powered ride-sharing platform that dynamically adjusts pricing based on real-time demand and predicts optimal driver routes to minimize idle time. This not only improves service efficiency but also creates a new, scalable business model where both the company and drivers share revenue based on platform activity.
Related questions:
- Can we create a new business model based on an AI application?
- How does the proposed model differ from traditional approaches, and what unique value does it offer?
- What are the scalability and profitability potential of this new business model?
Improved customer retention
AI enhances customer retention by analyzing customer behavior, identifying patterns that predict churn, and delivering personalized experiences. AI-powered tools enable businesses to proactively address customer needs, resolve issues, and build stronger relationships, leading to increased loyalty and lifetime value.
Example:
A subscription-based streaming service uses AI to analyze user preferences and viewing habits. Based on this data, the platform offers tailored content recommendations and sends timely reminders about new releases aligned with individual tastes. This personalization reduces the likelihood of cancellations and encourages customers to stay subscribed, translating into higher recurring revenue.
Related questions:
- Can more customers be convinced to return to the product or service, or prevented from leaving as customers?
- How can improved customer retention be translated into additional profit?
- What specific AI-driven initiatives (e.g., personalized offers, proactive engagement) can strengthen customer loyalty?
Improvements of existing products or services (increased customer satisfaction)
AI enhances existing products or services by providing smarter, faster, and more tailored solutions. These improvements lead to increased customer satisfaction, stronger brand loyalty, and a competitive edge in the market.
Example:
A telecommunications company enhances its customer support services by implementing an AI-powered virtual assistant. This assistant handles common inquiries, such as troubleshooting connectivity issues or billing questions, through natural language processing. For complex problems, it seamlessly escalates cases to human agents with a detailed summary of the issue. This reduces resolution time, improves the overall customer experience, and increases satisfaction by providing faster, more efficient support.
Related questions:
- How can AI be used to improve existing products or services to better meet customer needs?
- Do these improvements lead to increased customer satisfaction and higher retention rates?
- How can the enhanced product or service be leveraged to gain market share or increase pricing power?