5 steps to AI-supported service process optimization

Unplanned downtime costs the industry €42.5 billion a year and can amount to over €425,000 per hour. With AI, companies can cut these costs, reduce downtime by up to 15% and cut maintenance costs by 30%. But how do you get started?

Here are the 5 steps to implementing AI:

Analysis and objectives: Review existing processes, identify weaknesses and define clear KPIs.
Set up data systems: Install IoT sensors, centralize data and ensure data quality.
Develop AI models: Select tools, train and continuously update systems.
Integration into existing systems: Connect AI solutions with existing software and train employees.
Monitor results: Check KPIs regularly, carry out updates and use feedback.

Advantages at a glance:

Up to 25 % higher overall equipment effectiveness (OEE)
75% time saving in ticket processing
90 % customer satisfaction through self-service

By taking these steps, companies create the basis for more efficient and cost-effective service processes.

Data-driven process analyses and AI in production

Data & AI in Industry 4.0 – How are data-driven process analyses and artificial intelligence revolutionizing industry? Tobias Herwig talks to Benjamin Aunkofer about optimizing modern production processes through the smart use of data.

Step 1: Analyze and set goals

In order to optimize service processes in mechanical engineering, it is important to analyze the current situation and identify automation options.

Check current workflows

The review of existing processes forms the basis for successful AI integration. We proceed as follows:

Analysis step	Methodology	Expected result
Process recording	Creation of flowcharts	Clear presentation of the current situation
Vulnerability analysis	Analysis of throughput times and bottlenecks	Identification of optimization possibilities
Stakeholder survey	Interviews with service technicians	Practice-oriented suggestions for improvement

These steps help to identify the relevant areas for the use of AI.

Identify AI areas of application

The selection of suitable areas of application for AI is based on which processes can be automated. A practical example: KLM Royal Dutch Airlines uses AI-supported chatbots to automatically process frequent customer inquiries such as baggage information or flight changes ^[1].

“AI will completely transform the experience that customers get with their company.” ^[1]

Delta Airlines also shows how AI can create added value. With the help of AI-supported data analysis for reservations and pricing strategies, an increase in value of up to 2% was achieved ^[1].

In order to achieve such success, it is important to define clear goals and key figures.

Define clear performance indicators

Measurable success metrics are essential to evaluate the effectiveness of AI integration. Both technical and business aspects should be taken into account ^[2].

Metric category	Example key figures	Measuring cycle
Model performance	Accuracy, execution time	Daily
Business success	Customer lifetime value, acquisition costs	Monthly
Operative	Throughput times, degree of automation	Weekly

Interesting: Only 12% of companies manage to achieve a real competitive advantage through AI ^[2]. This shows how important careful planning and continuous monitoring of the defined key figures is.

Step 2: Set up data systems

Implementing IoT sensors

IoT sensors play a central role in the collection of machine data. The choice of the right sensors depends on the specific requirements of the machines:

Sensor type	Area of application	Measuring range
Temperature sensors	Monitoring of critical components	-40°C to +125°C
Vibration sensors	Early detection of wear	0.1-100 Hz
Pressure sensors	Monitoring hydraulic systems	0-400 bar

Some production companies report that vibration sensors are particularly helpful in detecting wear at an early stage. This enables preventive maintenance work and reduces unplanned downtime ^[4]. Once the sensors have been installed, the recorded data is collated in a central data center.

Set up data center

The collected machine data must be processed in a structured manner in a central data center. The following points should be taken into account:

Define data structure
A standardized data structure is crucial to enable meaningful analyses. Standardized data formats reduce inconsistencies and facilitate processing ^[5].
Implement security architecture
To protect sensitive operating data, a security architecture with a DMZ (demilitarized zone) should be set up ^[8].

Ensure data quality

The quality of the data has a significant impact on the success of AI applications. Studies show that poor data quality can lead to efficiency losses of 15-25% in operational processes ^[7].

Quality criterion	Measures	Inspection interval
Completeness	Automatic validation	Daily
Accuracy	Plausibility checks	Weekly
Actuality	Real-time monitoring	Continuous

Automated checks help to detect anomalies at an early stage and ensure consistent data formats. This creates a reliable basis for well-founded decisions.

“Continuous monitoring and improvement are essential for sustaining data quality. It allows organizations to proactively identify and address data quality issues before they impact business operations.” – Robert Wilson, Data Quality Analyst at 456 Enterprises ^[6]

"By integrating AI, Agentforce and automation into your service processes, you can not only increase efficiency, but also improve customer satisfaction. Unlike Salesforce's Einstein AI, Agentforce AI agents can not only assist, but also act proactively. To do this, they use relevant data and follow specific tasks that are precisely tailored to their defined roles." - logicline.com/agentforce

Step 3: Build AI models

Select AI tools

Select tools that can make your service processes more efficient. Analyze your needs precisely to find the right solution. The following criteria will help you do this:

Selection criterion	Meaning	Checkpoints
Integration	Must be compatible with your existing systems	API interfaces, supported data formats
Scalability	Should be able to grow with your requirements	Server capacities, license models
Support	Technical support must be available	Response times, quality of documentation
Security	Data protection and encryption are essential	GDPR compliance, security certificates

Clearly define which tasks are to be automated. This allows you to select tools that offer real benefits.

Training AI systems

Training AI systems requires high-quality data and a structured approach ^[9].

Important steps for successful training:

Data preparation
Prepare the training data carefully. Remove errors, standardize formats and mark relevant data points.
Model validation
Divide the data into 60% training data, 20% validation data and 20% test data to ensure accuracy.
Performance monitoring
Monitor model performance continuously using key figures such as accuracy and precision.

These trained models serve as the basis for future developments.

Update AI models

Automated updates play a central role. Teams often use tools such as MLflow or Kubeflow to optimize this process ^[10].

Update type	Frequency	Trigger
Routine update	Monthly	Planned maintenance
Performance update	As required	Deviations in key performance indicators
Emergency update	Immediately	Critical problems or errors

A clearly structured, versioned process for updates ensures that the models are continuously improved.

Step 4: Connect AI with existing systems

Once the AI models have been developed and fine-tuned, the next step is to integrate them into existing systems. This process requires a well thought-out approach in order to achieve maximum efficiency.

Connect business software

The connection of AI with existing systems should be structured. According to studies, companies can reduce their operating costs by up to 25% by integrating AI ^[12].

Integration aspect	Requirements	Solution approach
Data exchange	Real-time capable interfaces	API gateway architecture
Security	GDPR compliance	Encrypted data transmission
Scalability	Flexible resource customization	Cloud-based infrastructure
Legacy systems	Compatibility	Wrapper technology

Secure data exchange forms the basis for integrating AI service tools into existing systems.

Introduce AI service tools

AI tools should be introduced gradually in order to minimize risks and measure results precisely:

Launch and integrate pilot project
Start with a clearly defined pilot project. After successful testing, this should be transferred to regular operation using modern integration methods. An example: The University of Virginia developed an AI system that optimizes production processes in real time and intervenes automatically in the event of problems ^[11].
Establish quality assurance
Use monitoring mechanisms to continuously check the performance of the AI. Companies using AI-supported ERP systems report efficiency gains of between 30% and 40% ^[11].

For AI integration to be successful, employees must also be familiarized with the new technologies.

Train employees to use AI

After the technical implementation, it is crucial that the team understands the new tools and uses them effectively. According to a TalentLMS study from 2024, 67% of employees would like more training in AI ^[13].

An effective training program should include the following:

Convey basic knowledge: Explain the basic principles of AI and address ethical aspects.
Practice-oriented learning: Integrate real work scenarios into the training courses.
Regular training: Organize updates and workshops to keep knowledge up to date.

Tailor the training specifically to the requirements of the respective departments. The responsible use of AI tools and the development of efficient methods, for example for creating prompts, are particularly important.

Step 5: Monitor and optimize results

After the successful introduction and use of AI solutions, it is important to continuously monitor their performance. According to studies, 80% of the best-performing service organizations use AI regularly ^[14]. This monitoring ensures that all previous steps of the implementation process remain meaningfully linked.

Define key performance indicators

Clear and measurable key performance indicators (KPIs) are crucial to objectively evaluate the performance of AI. Here are some important KPI categories:

KPI category	Example metrics	Description
Model quality	Coherence, accuracy, security	Evaluates the accuracy and reliability of AI results
System quality	Availability, error rate, latency time	Measures the technical performance of the system
Business processes	Processing time, customer satisfaction	Shows the effects on operational processes
Adoption	Usage rate, session duration	Provides insights into acceptance within the company
Goodwill	Productivity gains, cost savings	Quantifies the financial benefit

These key figures should be defined and regularly reviewed both before and during implementation ^[3]. They form the basis for a well-founded evaluation of system performance.

Review of the AI results

Various control mechanisms are required to effectively monitor the performance of AI systems:

Real-time monitoring: Use automated tools to record performance metrics in real time and report deviations immediately. Pay attention to data quality in order to detect and rectify errors at an early stage ^[16].
Anomaly detection: Implement statistical methods to identify unusual patterns or deviations ^[16].

Carry out regular updates

Regular updates and checks are essential to ensure the long-term efficiency and security of AI solutions:

Model updates: Monitor the performance of AI models and perform retraining as needed to maintain their accuracy ^[15].
Safety audits: Schedule regular safety audits to ensure compliance with regulations ^[15].
Feedback loops: systematically collect and analyze user feedback to identify possible improvements ^[16].

All changes and adjustments should be documented to ensure transparency and traceability.

Conclusion

Advantages of AI integration

The integration of AI technologies results in lower costs and a more efficient way of working. According to studies, AI-supported service solutions can enable savings of up to 8 billion euros ^[17]. One example: a food manufacturer was able to reduce its operating costs by 13 million euros through AI-supported data analysis ^[17].

Measurable improvements at a glance:

Range	Improvement	Effects
Incident management	Up to 50 % shorter solution times	Faster problem solving
Ticket processing	75 % time saving	More efficient use of resources
Self-service	90 % customer acceptance	Higher customer satisfaction

These advantages illustrate how AI creates concrete added value. But what does the future hold?

A look into the future of AI

The ITSM market is expected to grow to over 22.1 billion euros by 2028 ^[20]. This makes it clear that the importance of AI will continue to grow.

“Rigid IT conditions are slowing down the use of new AI innovations that will be made available in short evolutionary cycles in the future, which is why IT needs flexible development conditions.”

Siegfried Riedel, CEO of the ITSM Group ^[19]

To remain competitive, companies should prioritize the following steps:

Use cloud-based service management solutions: By 2025, around 95% of all digital workloads will run on cloud-native platforms ^[20].
Expand self-service technologies: The market for this could grow to around 92 billion euros by 2030 ^[20].
Modernize security strategies: Introducing AI-powered security solutions and zero trust models ^[20].

The combination of human expertise and AI is the key. Companies that focus on AI at an early stage and integrate it sensibly create a stable foundation for long-term success and competitiveness ^[18].

Rethinking service processes: greater efficiency with IoT, AI and self-service

Find out how logicline’s extensions for Salesforce – especially for manufacturers of plant and machinery – can revolutionize your service processes with IoT, AI and self-service solutions. Find out more now and fully exploit service potential!

18.03.2025

5 steps to AI-supported service process optimization