Introduction
The programme “Customer Service Quality Management Using Artificial Intelligence Technology” develops professional practice in Quality. It connects the subject with workplace challenges by examining existing evidence and practices and choosing methods appropriate to the organization and its available resources.
The learning progresses from customer experience development and complaint handling to data analysis and decision support, with practical work on artificial intelligence applications and controls, quality management and process control, procedure analysis and work simplification. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Design a service journey and complaint response based on customer needs and follow-up evidence in an application related to “Customer Service Quality Management Using Artificial Intelligence Technology”.
- Design an AI use case with verification criteria and human review.
- Apply a quality approach connecting beneficiary requirements with process control and improvement.
- Redesign a procedure to reduce delays and rework while maintaining controls.
- Turn available data into documented analysis that supports a specific professional decision.
Learning areas
1. Customer Experience Development and Complaint Handling
- Define customer groups, expectations, and touchpoints, and map an actual journey including waiting, effort, and information exchange.
- Analyze the voice of the customer, complaints, and feedback, classifying recurring causes and linking them to the process, product, or communication channel.
- Set response, resolution, and escalation standards that consider the problem's impact, staff authority, and message clarity for the customer.
- Simulate a difficult service situation, applying listening, empathy, and fact verification, and offer a feasible solution or alternative.
- Build indicators for resolution time, complaint recurrence, and experience quality, and use results to address root causes and monitor impact. The application examines a case related to “Customer Service Quality Management Using Artificial Intelligence Technology”.
2. Artificial Intelligence Applications and Controls
- Identify a task that AI can support, and separate inputs and human decisions from outputs produced by the tool.
- Prepare suitable test data and examples while respecting confidentiality, privacy, and permissions, and use de-identified training exercise data.
- Write clear instructions covering the task, context, constraints, and output format, and compare multiple formulations using a fixed set of examples.
- Check outputs for errors, bias, and unsupported information, and identify cases that require specialist review.
- Assess the feasibility of use in terms of quality, time, cost, and risk, and document controls for approval, monitoring, and stopping when failures occur.
3. Quality Management and Process Control
- Define beneficiary requirements and product or service acceptance criteria, and translate them into testable process controls.
- Map process interactions, identifying risks, resources, records, and quality control responsibilities at handover points.
- Use cause and data analysis tools to understand nonconformities, and distinguish immediate correction from corrective action.
- Prepare a verification and internal review plan using objective evidence, referring to the current applicable edition of the relevant standard during implementation.
- Evaluate improvement results in terms of defects, time, rework, and beneficiary experience, and document knowledge that should be shared more widely.
4. Procedure Analysis and Work Simplification
- Define the procedure's start, end, owner, inputs, and outputs, and collect data on processing time, waiting, and rework.
- Map the actual workflow between roles, identifying approvals, handovers, bottlenecks, and duplicate data entry.
- Analyze the value added by each step and the controls it provides, and propose combining, removing, or running steps in parallel with reasons for the choice.
- Document the improved procedure with clear authorities, forms, timelines, and standards, and review its effects on beneficiaries and delivery teams.
- Test the procedure on a limited case, measure time, errors, and output quality, and document adjustments and the rollout plan.
5. Data Analysis and Decision Support
- Frame an analytical question linked to a specific decision, and define the unit of measurement, time period, and data needed to answer it.
- Check data quality for completeness, accuracy, consistency, and duplication, and document cleaning steps and limitations on reliance.
- Select appropriate comparisons, indicators, and charts, and distinguish statistical correlation from causal explanations of results.
- Interpret results in the work context, test assumptions and unusual values, and present alternatives and their associated uncertainty.
- Prepare an analytical report containing the finding, recommendation, data source and date, review owner, and next follow-up step.
Practical outputs
- A customer journey map, complaint response guide, and service quality cards.
- A use case brief and a test template for the quality of AI outputs.
- A process map, nonconformity register, and quality improvement plan.
- An improved procedure map, service profile, and acceptance criteria for implementation.
- A dashboard and decision memo explaining the data, assumptions, and alternatives.
Who should attend
- Practitioners, specialists and supervisors whose work relates to the programme subject.
- Managers and teams working in quality, according to their practical responsibilities.
- Professionals responsible for applying the outputs of “Customer Service Quality Management Using Artificial Intelligence Technology” within their organizations.
Prerequisites
- Working familiarity with the programme field and the ability to discuss a professional case or challenge.
- Confirm the suitability of your experience and any specialist prerequisites with the Union before participation.
Conclusion
The programme “Customer Service Quality Management Using Artificial Intelligence Technology” brings together concepts, case review and a practical application. Participants prepare steps that can be adapted to their responsibilities and organizational resources, with an indicator to follow application and review results after returning to work.
Participation arrangements and any special requests are specified in the confirmation issued by the Union.