Instructor-led service
AI foundations and workplace productivity workshop
Give your team a practical starting point with AI. Work through tool choice, clear instructions and result checking, then apply the method to a research, document, spreadsheet or meeting task.
Audience
Employer teams that are new to AI or want a more consistent way to use it in everyday work.
Starting point
- Everyday computer skills; no coding required.
- Access to the AI tool agreed for the workshop.
- A suitable practice task and non-sensitive examples that can be used in training.
Expected work product
Each exercise produces an AI-assisted work product, a checking checklist and instructions for repeating the task.
- A task brief with an audience and quality standard.
- A reviewed draft, analysis or action log using agreed practice information.
- A repeatable set of instructions and checks.
- A short plan for applying the method and measuring its usefulness.
Delivery format
Instructor-led workshop with demonstrations, guided practice and feedback. The task can focus on one function or bring a team together around a shared workflow.
Sample workshop agenda
- Task — Choose a recurring task and record the starting process and quality standard.
A task brief and starting measure. - Demo — See how to choose a tool, give clear instructions and check an AI-assisted result.
A worked example and checking routine. - Practise — Create or improve a work product using approved practice information.
A draft document, research summary, spreadsheet analysis or meeting action log. - Test — Check facts, sources, figures and decisions; revise the result where needed.
A checked work product and review notes. - Apply — Write down the steps and approval points for using the method at work.
A reusable task guide. - Measure — Compare quality, effort and usefulness with the starting task.
A practical follow-through plan.
Check the work
Complete a task from a short brief, explain the instructions used and show the checks applied before the output is used.
Tool and platform fit
The quotation names the tool or platform and required access after suitability and delivery capability have been confirmed.
Request a team workshop quoteFor training providers
For training providers: an agent-building module
Bring specialist agent instruction into your existing programme, with a defined learner task, guided build and assessment evidence.
Explore the agent-training module →Beginner · Practitioner · Advanced
Choose a learning route
Start with foundations, develop practical workflows or discuss a deeper engineering or specialist brief. Each route states the learner starting point.
Beginner
AI Foundations and Effective Prompting
Build a reliable way to choose an AI tool, give it a useful task and judge the result.
Audience: People starting with AI, or using it inconsistently, across professional and operational roles.
Prerequisites: Everyday computer skills. No coding required. Access to an agreed AI tool for practical exercises.
- Choose a suitable tool for a task and recognise when AI is a poor fit.
- Write instructions with purpose, context, constraints and a clear output format.
Practitioner
AI for Workplace Productivity
Use AI to support everyday work while keeping the checks that make the output useful.
Audience: Knowledge workers and teams working with research, documents, spreadsheets and meetings.
Prerequisites: Basic prompting skills or completion of the foundation area. Familiarity with the work task and agreed access to the relevant tools.
- Create a research or document workflow with sources and review points.
- Use AI to help with spreadsheet analysis and meeting follow-through, checking figures and decisions.
Practitioner
Build and Manage AI Agents
Configure an agent around a defined job, with clear information, permissions and limits.
Audience: Confident AI users, operations specialists and team leads who want to configure and supervise agents.
Prerequisites: Comfort using AI tools and a clearly defined example workflow. Coding requirements depend on the agreed platform.
- Define the agent’s task, information sources, tool access and human approval points.
- Configure a bounded workflow and test ordinary, missing-information and failure cases.
Advanced
Advanced Agent Engineering
Develop and evaluate agent workflows that connect to software and information sources.
Audience: Software engineers, technical architects and practitioners already building agents.
Prerequisites: Programming experience, familiarity with APIs and version control, and an understanding of basic agent workflows. Environment and access requirements agreed before delivery.
- Connect APIs and Model Context Protocol (MCP) tools with explicit access boundaries.
- Design retrieval and orchestration around a defined workflow and failure model.
Practitioner
AI for Business Functions
Apply AI to a specific function’s work, decisions and quality standards.
Audience: Teams in sales, marketing, bids, customer service, finance or operations.
Prerequisites: Knowledge of the chosen function and its review requirements. Basic AI skills and approved examples that can be used in training.
- Select a useful task and separate draft generation from decisions that need human judgement.
- Practise a function-specific workflow, such as a bid outline, campaign brief, service response or reporting analysis.
Beginner
AI Leadership and Governance
Make clearer decisions about where to use AI, how to introduce it and how to keep people accountable.
Audience: Leaders, managers, programme owners and people responsible for risk, adoption or oversight.
Prerequisites: No coding required. Bring a business process or adoption question and knowledge of the organisation’s decision responsibilities.
- Compare opportunities by usefulness, feasibility, risk and evidence needed.
- Define ownership, human oversight, permissions and escalation before scaling a use case.
Practitioner
Train-the-Trainer and Curriculum Services
Help tutors and training teams teach practical AI with clear exercises, checks and current materials.
Audience: Established training providers, internal learning teams and tutors developing AI provision.
Prerequisites: An agreed learner audience, course brief and delivery responsibilities. Tutors need the subject knowledge required for the modules they will teach.
- Develop or refresh learning outcomes, demonstrations, exercises and assessment criteria.
- Practise delivery and feedback using realistic learner questions and common failure cases.
Practical learning
Task → Demo → Practise → Test → Apply → Measure
Start with a useful task, practise the method, test the result and plan how to apply and measure it at work.
- Understand the task and define a useful result.
- Demonstrate the method and its limits.
- Practise with safe examples.
- Test the output and explain the checks.
- Apply the method to approved work.
- Measure quality, effort and usefulness.
Practical perspective
Engineering experience and AI authorship
AI Venture X was founded by Richard Russell, AI engineer and author of The Consequence Machine: AI Agents, Superintelligence and the Fight to Stay in Control. His work informs the focus on permissions, testing and human oversight.
About Richard Russell →Our approach
Independent training, relevant to your work
We develop our own learning outlines and exercises, informed by current vendor learning pathways. Official vendor courses, examinations and qualifications are separate.
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