The Techex Learning Hub is an interactive environment, created in partnership with leading associations and organisations, designed to deliver deeper, practical learning through expert-led workshops, roundtables and hands-on sessions. It gives delegates real-world insights, actionable takeaways, and meaningful opportunities to exchange ideas with peers and industry specialists.
From Prompt to Workflow
Move beyond one-off prompts and explore how generative AI can support repeatable everyday workflows. Through guided exercises, participants will break a business task into clear steps, develop and refine prompts, and assess the quality of the outputs.
The workshop will cover where human review adds value and how to identify tasks suitable for further automation.
Participants will leave with a practical workflow outline they can adapt for their own teams.
Building Your First AI Agent
What turns an AI assistant into an agent capable of taking action?
This practical workshop introduces the core components of an AI agent: instructions, tools, knowledge and controls.
Participants will work through a simple business use case, exploring how an agent selects tools, completes tasks and handles exceptions.
The session will also examine when to require human approval and how to test behaviour before introducing an agent into a live workflow.
Guardrails in Practice
Building a compelling AI demonstration is only the beginning; making it suitable for business use requires clear controls.
This workshop explores how to design and test guardrails for issues such as inappropriate responses, sensitive information and unreliable outputs.
Using practical scenarios, participants will identify risks, define acceptable behaviour and examine how safeguards affect usability. They will leave with a structured approach to testing controls and recognising where human oversight remains necessary.
Is Your Data Ready for AI?
AI ambitions often move faster than the data foundations needed to support them.
This interactive workshop helps participants assess whether their organisation’s data is suitable for a chosen AI use case.
Exercises will explore quality, ownership, access, lineage and governance, connecting each consideration to business outcomes.
Participants will develop a practical readiness assessment and prioritise the gaps they need to address before progressing from experimentation to deployment.
Physical AI in Practice
Explore how AI moves from digital outputs to actions in the physical world.
Using a guided demonstration and deployment exercise, this workshop will examine the relationship between perception, reasoning and robotic action.
Participants will consider a practical operational use case, identifying the data, simulation, integration and safety requirements involved.
The session will help attendees distinguish an engaging demonstration from a viable deployment and define the questions to answer before starting a pilot.
From AI Pilot to Product
How do you turn a promising AI pilot into a product that people use and the business can sustain?
This workshop applies product thinking to AI delivery, covering problem definition, user needs, ownership, success measures and ongoing improvement.
Participants will work through a use case to identify the assumptions that need testing and the operational requirements that pilots can overlook.
They will leave with an outline for progressing an AI initiative towards production.
Securing Enterprise AI
AI applications introduce security challenges across data, models, integrations and user interactions.
Through a guided threat-modelling exercise, participants will examine a sample enterprise AI application and identify potential weaknesses, including prompt injection, information exposure and excessive tool permissions.
The workshop will explore practical mitigations and testing priorities, helping attendees build a security checklist they can use when assessing AI applications within their own organisations.
Digital Twins and Edge AI
How can digital twins and edge AI work together to support better operational decisions?
This workshop follows a practical use case from physical asset and sensor data through to analysis and action.
Participants will map the information flows, consider which decisions belong at the edge, and identify requirements for interoperability, latency and data quality. They will leave with a use-case blueprint and a clearer understanding of the foundations needed for implementation.
From AI Idea to Evidence: Testing What Works
A convincing AI prototype can still fall short of solving a meaningful problem.
This interactive workshop challenges participants to assess an AI concept against user needs, technical feasibility and measurable business value.
Working through a structured exercise, they will identify risky assumptions, define a minimum viable product and design a test that produces useful evidence.
Participants will leave with clear criteria for deciding whether to develop, reshape or stop an idea.
Measuring What Matters: Evaluating AI Before Deployment
How do you know whether an AI application is reliable enough for real business use?
This practical workshop explores how to evaluate AI outputs against clear measures of quality, accuracy, cost and response time.
Participants will work through a sample use case to build a test set, define success criteria and compare results.
The session will also cover how to capture user feedback and monitor performance as models, data and business requirements change.
Attendees will leave with an evaluation plan they can adapt to their own AI projects.
Connecting AI to Enterprise Knowledge: A Practical RAG Workshop
Explore how retrieval-augmented generation (RAG) connects AI applications to organisational knowledge.
Participants will work through a sample use case, examining how to prepare documents, retrieve relevant information and produce answers with supporting sources.
The workshop will cover common challenges, including outdated content, poor retrieval and access permissions.
Attendees will leave with a practical blueprint for developing and testing an AI knowledge assistant.
