Artificial Intelligence 101
Audience:
Executives, senior leaders, owners and managers responsible for strategy, risk, technology investment or oversight. No technical background required.
Duration:
0.5 Day
Delivery:
Virtual (1 × half day) or in-person (1 × half day). Delivered live-virtual across Canada and the US, and in-person in Ottawa–Gatineau (National Capital Region) and Toronto. Custom on-site delivery is available for private-sector, public-sector and not-for-profit teams, with examples tailored to your industry.
Next public sessions (live-virtual, 1 half-days, $450/person): Nov 17, 2026 · Winter 2027 TBD — book on our training page.
Course description:
Every organization now faces two AI risks: the risk of adopting it badly, and the risk of not adopting it, for example while competitors and customers move on or while you fail to take advantage of operational improve. This course gives senior leaders a clear, practical grounding in what AI is, how its core methods work, and what generative and agentic AI can and cannot do. It then equips them to weigh both sides of that decision for their own products, services and operations, looking at value, cost, security, privacy, ethics, workforce and continuity. Concepts are explained through simple, behind-the-scenes examples and live interactive tools. There is no math and no code. Participants leave with a structured way to assess AI opportunities and risks, and a first draft of that assessment for their own organization.
Course overview:
Why AI, why now – How AI has evolved, what has changed in the last few years, and how it is reshaping productivity, innovation and competitiveness across industries
Drivers and the rules of the road – The market, economic and customer pressures pushing adoption, and the legal, regulatory and standards landscape that governs its use, with examples such as Canada’s national AI strategy, privacy law, algorithmic impact assessments and international frameworks
Navigating the technology landscape – AI, machine learning, data mining and data science on a single map, explained through simple, behind-the-scenes examples:
Artificial Intelligence approaches (e.g., Rule-Based, Predictive & Decision, Perceptual, Generative)
Machine Learning and Data Mining approaches (e.g., Supervised & Unsupervised Learning, Pattern and Association Discovery)
Why not every “AI” problem needs AI: the role of statistics, simulation and modelling
Generative AI and large language models – What tools like ChatGPT, Claude, Gemini and Copilot actually do, why they sometimes produce confident errors, and how enterprise tools differ from consumer ones
Agentic AI – How autonomous, goal-driven systems plan and act, where human oversight belongs, and how to tell genuine capability from marketing (“agent-washing”)
AI in products, services and operations – Where AI shows up in what you offer customers versus how you run the business, and how the risks, customer-trust questions and value case differ between the two
Assessing the risk of adopting, and of not adopting – A structured, look at AI risk, covering:
Security, privacy, ethics and bias
Data sovereignty and unsanctioned “shadow AI” use
Vendor dependence and business continuity
Workforce impact and competitive position
Governing and leading adoption – Key elements of an AI use policy and technology plan, building the business case and measuring outcomes, and bringing staff along through phased, well-managed change. Ends with a short exercise in which participants assess one real AI opportunity for their own organization
By the end of this course, participants will be able to:
Explain what Artificial Intelligence is and how its major analytical, generative and agentic methods operate
Describe how AI has evolved and why accelerating capabilities matter for productivity, innovation and competitiveness
Identify the strategic drivers pushing AI adoption and the laws, policies and standards that govern its use
Distinguish between key AI, machine learning, data mining and data science approaches, and recognize when a simpler method is the better fit
Understand what generative AI and large language models can and cannot reliably do
Understand the fundamentals of agentic AI and the opportunities and risks autonomous, goal-driven systems introduce
Assess the risks of adopting, and of not adopting, AI in the organization’s products, services and operations, using a structured risk framework
Recognize the security, privacy, ethical, continuity and data-sovereignty considerations that belong in an AI risk assessment and mitigation plan
Outline the key elements of an AI use policy and technology plan suitable for leadership approval
Frame an AI investment as a business case with expected costs, benefits and measurable outcomes
Plan for the people side of adoption, including staff engagement, training and phased transition
Interactive Data Tools for This Course
These tools are free to use anytime — browse them before the course as a preview, work through them live during a session, or come back to them afterward as a refresher.