EN
    Search
    Contact us
    Language
    Search
    Enterprise Resource Planning
    Proalpha AI Platform
    Financial Management
    Human Capital Management
    Spend Management
    Digital Shopfloor
    Customer Experience
    Business Application Suite
    Mechanical and Plant Engineering
    Automotive Suppliers
    Electronics & High Tech
    Medical Technology
    Metal Processing
    Wholesale
    Aerospace & Defence
    Plastics Industry
    Production
    Procurement
    Sales
    Finance
    Materials Management
    Quality Management
    Service
    my Proalpha
    Academy
    Consulting
    Product Documentation
    Management
    Success Story
    Group Companies
    Locations
    Press Release
    Become Partner
    Careers
    Contact
    AI in Customer Service

    AI Agents for Better Customer Service, More Efficient Operations, and Faster Decision-Making

    Service organizations in medium-sized industrial companies are under pressure: rising customer expectations, a shortage of skilled workers, complex machinery, and high demands on response times, spare parts availability, and first-time fix rates. For service decision-makers, therefore, the goal is not only to process individual service cases more quickly, but also to manage the entire service process in a way that is more transparent, predictable, and cost-effective.

    Proalpha AI integrates service cases, machine information, spare parts data, documentation, and historical service records into an intelligent knowledge base. AI agents help service teams identify critical issues more quickly, set priorities based on data, and prepare for service calls in a targeted manner – all directly integrated into the business processes of medium-sized industrial companies.

    The Benefits of AI in Technical Service
    IL-data-in-real-time

    Faster Access to Relevant Service Knowledge

    Service cases, machine histories, spare parts data, and documentation are available in a central knowledge base to support informed decision-making.

    IL-Browser-Based-Instruments-Browser-Based-Instruments

    Better Preparation for Service Calls

    Technicians, scheduling staff, and service managers can see all the relevant information about the machine, the customer, replacement parts, and previous malfunctions at a glance.

    IL - Transparent Reporting

    Greater Transparency Regarding Open Service Requests

    Critical cases, priorities, processing statuses, and service levels can be tracked at any time.

    IL-Industry-Services

    More Efficient Service Processes in Everyday Industrial Operations

    Recurring tasks are streamlined, administrative burdens are reduced, and service teams are specifically relieved of some of their workload.

    AI Agents Drive Service Processes

    Proalpha AI supports service organizations throughout the entire service process – from capturing and prioritizing open service cases to preparing for service calls and analyzing service histories. The following examples illustrate how AI agents accelerate industrial service processes, make knowledge accessible, and support service managers in making operational and strategic decisions.

    Process Open Service Cases Faster

    Proalpha AI provides transparency into open service cases, identifies urgent requests, and helps service teams prioritize them based on criticality, deadline, customer, and machine status. This reduces response times while allowing service managers to maintain a constant overview of workload, escalations, and processing progress.

    Typical questions from day-to-day service operations that Proalpha AI answers:

    • Which service cases are open and overdue?
    • Which cases require immediate attention?
    • Create a new service request for Customer X with the following issue.
    • Update the status of a service case after the technician’s visit.

    Your Benefits

    • Identify critical service cases faster and avoid escalations
    • Set priorities based on data – by customer, criticality, SLA, and machine status
    • Automate administrative service processes
    • Reduce response times and processing effort
    Proalpha AI Platform

    Proalpha AI Platform: The Foundation for Intelligent Service Processes

    With the Proalpha AI Platform, AI agents access service knowledge, machine information, spare parts data, and historical service transactions. They identify connections between customers, equipment, service cases, and business processes, thereby providing well-founded answers, transparent recommendations, and concrete suggestions for action in industrial service.

    The Proalpha AI Platform integrates data, processes, and knowledge from ERP, service, spare parts management, and other business areas. In doing so, it lays the foundation for generative AI in industrial SMEs: reliable data, process-oriented answers, and AI agents that effectively support service organizations in their day-to-day operations.

    Service is just one of many areas of application. Discover how Proalpha AI supports employees along the entire value chain – from analysis and decision-making to the automation of recurring tasks in industrial companies.

    Proalpha AI Overview
    Pa-WP-industrial-ai-for-smes-1920x1080

    Whitepaper: Industrial AI in Mid-Sized Companies

    How can small and medium-sized enterprises not only test AI but also use it effectively over the long term? This whitepaper highlights the challenges that arise when scaling up, explains why integrated data and processes are crucial, and shows you how to get started with industrial AI.

    FAQ – Frequently Asked Questions About AI Agents in Customer Service

    • Proalpha AI agents help service teams prioritize open service cases, retrieve relevant information, and prepare technician visits. To do this, Proalpha AI links service cases with customer, machine, and spare parts data, as well as technical documentation. Service management, scheduling, and technical customer service can use this information to process inquiries and prepare for on-site visits.

    • In supplier risk management, AI agents highlight critical supplier dependencies, delayed deliveries, and recurring schedule deviations. For example, AI in supplier management shows which parts pose a high concentration risk, which production orders are affected by delayed orders, and which alternative suppliers are viable options.

    • Proalpha AI agents help prioritize tasks based on criticality, deadlines, customer information, service-level agreements, and machine status. They highlight open and overdue cases and help service managers identify which cases require immediate attention.

    • Proalpha AI integrates machine information, technical documentation, spare parts data, and previous service records. The information that AI agents can use for a specific case depends on the connected data sources and the stored data. Error codes can be taken into account provided they are included in the available information and are available for the specific use case.

    • For example, service teams can check which service calls have been made for a particular machine, what issues are known for a specific machine type, and which parts have already been installed. The AI agents make the stored service knowledge accessible for the current task. Documented solutions can thus be incorporated into the preparation for the service call, provided they are included in the available service histories.

    • Proalpha AI agents aggregate information about customers, machines, previous service calls, and replacement parts. For example, they help identify the replacement parts needed for a scheduled service call and track components that have already been installed. This supports scheduling and technical staff in their preparations. Whether current inventory levels can be checked or technicians can be automatically assigned depends on the connected systems and the configured functions.

    • Use cases include creating a service request, updating a service case status, and removing an incorrectly created service call item. The actions an AI agent is actually permitted to perform must be defined through the respective configuration, permissions, and approvals. The scope of autonomous service case processing therefore depends on the specific process that has been set up.

    • The escalation of a service case to employees should be subject to defined limits, such as missing information, required approvals, or decisions that fall outside the scope of permitted actions. Escalation rules and logging functions for traceable steps must be defined for each service process.