AI-based process automation offers companies a wide range of opportunities to optimize their operations, reduce costs, and develop innovative solutions. In this article, we present practical use cases and provide a checklist to help you successfully launch your automation project.
What is AI-Based Process Automation?
Process automation refers to the automation of recurring business processes and tasks within a company. This can range from simple tasks like data processing to complex decision-making processes across various departments. Artificial intelligence, particularly machine learning and advanced algorithms, enables companies to elevate this automation to a new level.
Artificial intelligence can recognize patterns in large datasets, make predictions, and make decisions—often faster and more accurately than any human employee.
Opportunities and Benefits of AI-Based Process Automation
Although automation and artificial intelligence are still viewed by some as complicated, too complex, or untrustworthy, they should not be overlooked due to their numerous benefits.
The possibilities of AI, in conjunction with human skills, are almost limitless. With careful planning, companies in any industry can benefit!
Efficiency Improvement
Imagine if your employees could focus on creative and strategic tasks instead of spending hours on repetitive tasks. AI-based process automation allows exactly that—a significant increase in overall efficiency within your company.
Cost Savings
Error-prone manual tasks cost companies not only money but also time. By automating such tasks with AI, you can not only reduce costs but also increase accuracy and speed.
Enhanced Decision-Making
With AI, companies can analyze data in real-time and make informed decisions. This enables data-driven management and a quicker response to market changes.
Driving Innovation
AI-based process automation supports companies in developing new products and services. For example, personalized customer interactions or predictive maintenance systems can be optimized and improved.
Scalability
One of AI’s great strengths is its ability to grow with the company. From small pilot projects to scaling at the enterprise level, AI can be flexibly adjusted to meet growing demands.
Use Cases: How Our Clients Use AI-Based Process Automation
1. Case Study: AI-Based Automation of Inventory Management at a Machine Tool Manufacturer
To optimize its inventory management processes and avoid unnecessary costs, a machine tool manufacturer uses AI-powered systems. This reduces monthly inventory costs for outdated and written-off parts.
- Data Collection and Inventory Monitoring
The system continuously collects data on inventory, including quantities, condition information, and depreciation status of parts. Sensors and IoT devices support real-time data collection.
- Data Analysis and Forecasting
AI algorithms analyze this data to detect patterns and trends in inventory. AI forecasts which parts will soon be depreciated and should no longer be used.
- Automatic Inventory Optimization
Based on the forecasts, the system automatically generates recommendations for inventory management. Parts that are depreciated or have high storage costs are identified and suggested for disposal.
- Notifications and Action Recommendations
The system sends notifications to inventory management with detailed action recommendations. This includes prioritizing the disposal of depreciated parts and optimizing storage space.
- Report Creation and Analysis
Automatically generated reports provide management with a clear overview of inventory status and savings achieved. These reports support data-driven decisions and strategic planning.
By implementing this AI-based process automation, the machine tool manufacturer can significantly enhance the efficiency of its inventory management, remove outdated and written-off parts from inventory in a timely manner, and save approximately EUR 500,000 in inventory costs monthly.
2. Case Study: Personalized Customer Interaction in E-Commerce
One of our clients significantly improves the shopping experience of their online store visitors through process automation. This strengthens customer loyalty and increases sales by providing tailored offers.
- Data Collection and Profiling:
The system collects data on customer behavior on the website, such as visited pages, purchased products, search queries, and time spent. This data is used to create detailed customer profiles.
- Analysis and Segmentation:
AI algorithms analyze the collected data to identify patterns and preferences. Based on these insights, customers are automatically segmented into groups with similar interests or purchasing behavior.
- Personalized Recommendations:
Using customer profiles, the system automatically generates personalized product suggestions and offers. These recommendations are displayed in real-time on the website or sent via email to influence customer purchasing decisions.
- Automatic Adjustment and Optimization:
The system continuously learns from customer behavior and adjusts recommendations accordingly. Through A/B testing and feedback loops, it optimizes personalization to maximize conversion rates and sales.
3. Case Study:: AI-Based Automation and Digitalization in Insurance
An Austrian insurer for sports goods automated a previously complex, manual process that included consulting, reviewing insurance applications, communication, contract and document creation, billing, claims processing, and additional insurance.
- Introduction of a Legally Compliant Online Platform
The new platform allows customers to conduct online consultations and perform the “wish-need test” easily and securely. This platform is prominently placed and advertised on all comparison portals.
- API Integration
Various interfaces (APIs) are integrated to connect systems for review, billing, CRM, follow-up, and marketing. This ensures seamless data flows and improves process accuracy.
- Automation of Contract and Document Creation
The platform automatically generates contracts and documents based on entered customer data, resulting in faster and error-free creation.
- Automation of Billing, Dunning, Collection, Claims Processing, and Additional Insurance
Billing processes, dunning, and collections are efficiently managed by automated systems, reducing manual effort and speeding up payment receipts. Expiring contracts are also detected to offer additional insurance in a timely manner.
- Implementation of a Reporting and Dashboard System
A comprehensive reporting and dashboard system provides real-time insights into key performance indicators, supports data-driven decisions, and optimizes management.
The process from advertising message to final contract has been automated by 90% and mapped within a CRM system. Employees review and manage orders via drag-and-drop in KANBAN interfaces. User history is chronologically stored, SMART processes efficiently manage contracts and groups. The system is continuously developed and currently expanded with AI modules to further optimize it.
How Does AI-Based Process Automation Work
The key to successfully implementing AI-based process automation lies in a systematic approach. From data collection and model development to integration and monitoring: every step is crucial for the project’s success.
Data Collection and Preparation
Companies identify and structure relevant data sources needed for process automation. This can include a variety of data, from structured data in corporate systems to unstructured data such as texts and images.
Model Development and Training
Custom AI models are developed and trained to support specific business processes. Machine learning algorithms are used to learn from collected data and enable predictive analytics.
Integration and Implementation
AI solutions are seamlessly integrated into existing IT infrastructure and business processes. A careful implementation phase and thorough testing ensure that the solution functions correctly and achieves the desired results.
Monitoring and Optimization
After implementation, the performance and results of AI systems are continuously monitored. Through continuous feedback and optimization loops, models are improved to further maximize accuracy and efficiency.
AI-Based Process Automation: Driving Innovation and Enhancing Efficiency!
AI-based process automation offers companies tremendous potential to improve their business processes and enhance competitiveness. By adopting a strategic approach and leveraging AI effectively, companies can achieve operational efficiency, drive innovation, and strengthen customer relationships.
To learn more about how you can successfully implement AI-based process automation in your company, please contact us. Our team is ready to assist you with consulting and implementation.
Let’s explore the opportunities AI offers for your business together.
Checklist
for Implementing AI-Based Process Automation
1. Goal Setting: Clearly define the goals of process automation with AI. What do you want to achieve? For example, reducing costs, increasing efficiency, improving customer experience.
2. Data Collection: Identify necessary data sources and collect relevant data for automation.
3. Technology Selection: Choose an AI technology or platform that meets your requirements.
4. Development and Testing Develop prototypes of the AI solution and conduct tests to ensure its functionality.
5. Implementation: Introduce the AI solution on a limited scale and monitor performance.
6. Scaling: If successful, scale the solution to other areas or processes.
7. Monitoring and Optimization: Continuously monitor performance and optimize the solution as needed.