AI Governance and Quality Control for Administrative Professionals

Course Category : Quality

An advanced programme enabling administrative professionals to govern AI use, control output quality, manage associated risks, and support reliable, accountable administrative decision-making.
Duration: 10 Days
Level: Advanced

Introduction

The growing integration of artificial intelligence into administrative functions requires organisations to establish clear mechanisms for accountability, quality assurance, and responsible oversight. Effective adoption depends not only on access to AI capabilities but also on the ability to validate outputs, identify errors and bias, protect information, and define appropriate administrative responsibilities.
This programme examines AI governance from an administrative perspective, focusing on policies, controls, risk management, output quality, human oversight, documentation, and compliance. It develops the competencies required to support reliable and accountable AI-enabled administrative processes..

Targeted Audience

Administrative and Department Managers
Section Heads and Administrative Supervisors
Corporate Governance Managers and Specialists
Quality and Organisational Excellence Professionals
Administrative Policy and Procedure Officers
Risk and Compliance Professionals
Digital Transformation Officers supporting administrative functions
Administrative professionals involved in using or overseeing AI solutions

Targeted Skills

Understanding AI governance principles in administrative environments
Developing institutional AI policies and controls
Defining roles, responsibilities, and accountability
Assessing risks associated with AI-generated outputs
Controlling output quality, accuracy, and reliability
Identifying bias, inconsistency, and errors
Applying effective human oversight and review
Managing privacy and information confidentiality
Documenting AI use and related decisions
Developing monitoring and continuous improvement indicators

Expected Outcomes

Explain fundamental AI governance principles and their administrative applications.
Define responsibilities and authorities associated with organisational AI use.
Develop appropriate controls for responsible AI use in administrative processes.
Classify and assess risks associated with AI-supported outputs and decisions.
Establish criteria for reviewing output accuracy, completeness, consistency, and relevance.
Recognise indicators of bias, error, and unreliable information.
Determine appropriate levels of human oversight according to risk.
Support privacy, confidentiality, and compliance requirements in AI use.
Develop documentation, traceability, and accountability requirements.
Support continuous improvement in AI governance and quality management.

Training Topics Index

Definition and institutional importance of AI governance
Relationship between governance, ethics, quality, and accountability
Characteristics of AI use in administrative functions
Risks arising from uncontrolled AI adoption
Principles of trust, fairness, transparency, and responsibility

Institutional models for AI oversight
Defining process, decision, and output ownership
Allocation of responsibilities across management, technology, and governance
Accountability for AI-supported decisions
Escalation, review, and approval mechanisms

Components of institutional AI-use policies
Acceptable, restricted, and prohibited uses
Administrative authority and approval levels
Requirements for handling sensitive information
Integrating AI controls into existing policies and procedures

Identifying risk sources in administrative AI use
Classifying risks according to impact and likelihood
Risks of excessive reliance on automated outputs
Bias, unfairness, and inaccurate information risks
Preventive and corrective controls and risk monitoring

Defining quality for AI-supported outputs
Accuracy, completeness, consistency, and relevance criteria
Verification of information and underlying assumptions
Acceptance, rejection, and escalation criteria
Documentation of quality review results

Sources of bias in data and outputs
Distinguishing error, bias, and inconsistency
Assessing fairness in administrative contexts
Impact of bias on decisions and stakeholders
Administrative controls for reducing bias risks

Importance of transparency in administrative AI use
Limitations of explaining automated outputs and decisions
Purpose and principles of human oversight
Determining cases requiring human review or approval
Documenting review, intervention, and final decisions

Data protection principles in AI use
Information classification before AI processing
Risks of unintended information disclosure
Access, authorisation, and data protection requirements
Aligning governance with compliance and internal policies

Documenting organisational AI use cases
Decision, review, and approval records
Traceability and accountability requirements
Output quality and risk indicators
Periodic review and compliance auditing

Assessing AI governance maturity
Analysing quality, risk, and incident results
Updating policies and controls as requirements evolve
Strengthening a culture of responsible AI use
Developing an integrated administrative governance and quality-control framework

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

AI Governance and Quality Control for Administrative Professionals