Overview
Ai adoption is happening faster than many organisations can create policies around it. This interactive program helps consulting engineering firms move from informal individual AI use toward responsible, secure, and governed organisational adoption. Through risk classification, policy development, and vendor assessment, participants will build an initial AI governance framework that balances technological innovation with professional accountability, privacy protection, and risk management. Why Should an Individual Attend?
-
Establish Clear Acceptable Use Rules: Determine exactly which AI platforms employees may use and what client or organisational information can be safely entered.
-
Protect Privacy and Intellectual Property: Learn how to safeguard confidential project data, client information, and proprietary IP from unauthorised exposure or data leakage.
-
Ensure Professional Accountability: Define clear standards for human oversight, verification, and professional sign-off when AI tools contribute to technical deliverables.
-
Drive Responsible AI Adoption: Transition your organisation from uncontrolled, high-risk experimentation to a structured, secure, and value-driven AI operating model.
Outcomes
On completion of this course, the delegate will be able to:
-
Understand Organisational AI Risks: Identify and evaluate major strategic, legal, operational, and technical risks associated with AI deployment.
-
Classify AI Use Cases: Categorize organisational AI applications into risk tiers (e.g., approved, restricted, prohibited).
-
Establish Core AI Principles: Formulate appropriate usage guidelines and acceptable use policies tailored to consulting engineering.
-
Manage Data and Confidentiality: Protect client privacy, proprietary information, and regulatory data compliance (including POPIA).
-
Define Oversight Requirements: Establish robust human-in-the-loop verification processes and professional sign-off standards.
-
Evaluate AI Tools and Vendors: Assess third-party AI software and platforms for security, compliance, and suitability prior to adoption.
-
Develop a Governance Framework: Build a practical 90-day governance implementation roadmap and risk assessment matrix for your firm.
Program Outline
-
Foundations of AI in Engineering & Risk Classification
-
AI in the professional engineering environment: Current landscape and adoption drivers
-
AI risk classification: Categorising tools, platforms, and automated workflows
-
Identifying confidentiality, privacy, and data protection risks
-
Legal, Professional & Operational Control
-
Intellectual property rights, ownership, and professional responsibility
-
Human oversight requirements and mandatory professional sign-off protocols
-
Drafting AI policies: Acceptable use guidelines and operational boundaries
-
Implementation, Vendor Assessment & Governance Strategy
-
Tool and vendor assessment: Evaluating security, privacy, and technical standards
-
Managing client expectations, transparency, and contractual disclosures
-
Developing a 90-day AI governance implementation roadmap for your firm
Who Should Attend?
-
Directors and Senior Executives
-
Engineering Managers and Technical Leads
-
IT, Cyber, and Risk Management Professionals
-
Human Resources and Training Leaders
-
Quality, Compliance, and Project Leaders
|
