Functional & Business Track
Agentic Expertise for AI-Driven Business
Design, assess and govern AI agents using your own business challenges as the starting point. Gain field-tested insight into what drives value, what fails in real organisations, and how to maintain control without unnecessary technical noise.
Course overview
- This one-day programme targets business and functional professionals who need to frame, assess and steer agentic AI initiatives without unnecessary technical depth.
- Every example, discussion and workshop is adapted live to the functions in the room, the organisation’s context and its current level of AI maturity.
- Use cases are never imposed: they are co-developed from the real challenges participants bring so outcomes stay realistic, governed and value-focused.
Learning objectives
By the end of the day, participants will be able to:
- Clearly articulate what an AI agent is — and what it is not.
- Translate their own operational or managerial challenges into agentic AI use cases.
- Assess business value, feasibility and risk in their organisational context.
- Recognise recurring failure patterns seen in real deployments.
- Define governance, accountability and human oversight models.
- Build a pragmatic, business-aligned agentic AI roadmap.
Who should attend?
This programme is ideal for:
- Functional leaders (HR, Finance, Operations, Quality, Support, functional IT).
- Digital transformation and change managers.
- Functional product owners and innovation leads.
- Continuous improvement managers and executive sponsors.
- Leadership teams overseeing AI-enabled programmes.
No technical background is required.
Prerequisites
Participants should have a solid understanding of their own business processes, decision-making structures and constraints. Prior AI or data science experience is not necessary.
Course content
Module 1 – Understanding agentic AI through real business situations
Foundational concepts
- Automation vs generative AI vs agentic AI.
- What AI agents can realistically decide, execute and adapt.
- Limitations observed in operational environments.
Participant alignment
- Clarifying expectations and priority challenges.
- Positioning AI agents within existing processes and decision flows.
Module 2 – Business use cases tailored to participants
Generic patterns (adapted live)
- Operational prioritisation.
- Management decision support.
- Cross-team coordination.
- Workflow, request or ticket management.
- Multi-source analysis and executive synthesis.
Each use case is reframed in real time using participant examples.
Workshop: translate participants’ challenges into structured agentic AI use cases.
Module 3 – Field experience: what works, what fails, and why
Experience-led analysis
- Why certain use cases deliver sustained value.
- Why others fail despite strong intent.
- Common gaps between design assumptions and reality.
Recurring pitfalls
- Choosing the wrong problem to automate.
- Building agents on unstable or poorly defined processes.
- Granting excessive autonomy too early.
- Missing human ownership and accountability.
- Tracking activity KPIs instead of business outcomes.
Module 4 – Governance, control and human oversight
Practical governance framework
- Human-in-the-loop models.
- Setting safe autonomy boundaries.
- Decision traceability and auditability.
- Alert thresholds, escalation paths and rollback mechanisms.
Contextual adaptation
- Tailoring governance models to organisational constraints.
- Risk assessment aligned with participants’ environments.
Module 5 – Building a personalised agentic AI roadmap
Methodology
- Selecting priority use cases.
- Assessing value, risk and organisational readiness.
- Progressive deployment paths (assisted → semi-autonomous).
- Defining clear, business-oriented success criteria.
Deliverable: a custom, actionable agentic AI roadmap aligned with each participant’s context and constraints.
Key benefits
- Use cases grounded in real operational challenges.
- A realistic, non-hyped view of agentic AI.
- Reduced risk of failed or stalled initiatives.
- Decision frameworks for knowing when to proceed — and when not to.
- Practical tools for governance and accountability.
Teaching methods
- Real-world case studies (anonymised).
- Analysis of successes and failures from the field.
- Participant-driven workshops.
- Reusable decision and governance frameworks.
- Peer discussion and guided reflection.
Assessment & certification
Continuous assessment through workshops and case analysis, validation of the personalised roadmap and a certificate of completion issued at the end of the programme.