Introduction
The programme “Quality Management through Artificial Intelligence Applications” 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 artificial intelligence applications and controls to risk assessment, controls, and response, with practical work on quality management and process control, procedure analysis and work simplification, data analysis and decision support. Participants prepare outputs to support workplace application and review the quality and results of implementation.
Learning objectives
- Design an AI use case with verification criteria and human review in an application related to “Quality Management through Artificial Intelligence Applications”.
- 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.
- Prepare a risk register connecting causes, consequences, controls, and treatment responsibilities.
Learning areas
1. 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. The application examines a case related to “Quality Management through Artificial Intelligence Applications”.
2. 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.
3. 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.
4. 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.
5. Risk Assessment, Controls, and Response
- Define the work context, objectives, and assessment boundaries, and distinguish the risk source, potential event, and effects on results.
- Gather risks from processes, data, past incidents, and interviews with responsible staff, and document causes and existing controls.
- Estimate likelihood and impact using stated criteria, and review control effectiveness to determine residual risk levels.
- Select a response proportionate to the risks, resources, and authority, and specify the owner, deadline, and early warning indicator.
- Test a scenario involving changing circumstances, update the risk register, and present material risks and required decisions to the appropriate authority.
Practical outputs
- 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.
- A risk register, control matrix, and prioritized treatment plan.
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 “Quality Management through Artificial Intelligence Applications” 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 “Quality Management through Artificial Intelligence Applications” 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.