AI Training Curriculum Examples
Training curriculum examples for building AI capability across organisations — from executive AI literacy to technical prompt engineering and hands-on implementation skills.
Executive AI Literacy Programme
beginnerA half-day executive education programme covering what AI can and cannot do, strategic implications, competitive dynamics, risk considerations, and how to evaluate AI opportunities — designed for C-suite and board members.
Key takeaway: Executive AI training should focus on decision-making frameworks, not technology explanations — executives need to know when to invest in AI, not how neural networks work.
Company-Wide AI Fundamentals Course
beginnerA 4-module course for all employees covering AI basics, practical applications in their work, responsible AI use, and hands-on prompt engineering exercises tailored to different departments.
Key takeaway: Department-specific examples (marketing prompts for marketers, finance prompts for finance) drive 3x more adoption than generic AI training.
Prompt Engineering Workshop Series
intermediateA 3-session workshop series that progresses from basic prompt writing through advanced techniques (chain-of-thought, few-shot, system prompts) to building prompt libraries for team use.
Key takeaway: Prompt engineering workshops that include building a team prompt library as a deliverable produce lasting value beyond the training itself.
AI for Product Managers Curriculum
intermediateA programme for product managers covering how to identify AI product opportunities, write AI feature specifications, manage AI development projects, and set realistic expectations for AI capabilities.
Key takeaway: Product managers who understand AI constraints (data requirements, accuracy limitations, development timelines) make better AI product decisions than those who treat AI as magic.
Technical AI Implementation Bootcamp
advancedAn intensive 5-day programme for developers covering LLM API integration, RAG implementation, prompt engineering for production systems, evaluation methods, and deployment best practices.
Key takeaway: Hands-on implementation bootcamps where participants build a real application produce better skill transfer than lecture-based technical training.
AI Champions Programme
intermediateA programme to train AI champions within each department — employees who become go-to resources for AI questions, share best practices, identify new use cases, and bridge between central AI teams and business units.
Key takeaway: Distributed AI champions scale AI adoption far more effectively than centralised AI teams — they translate AI capabilities into department-specific value.
Patterns
Key patterns to follow
- Role-specific training with department-relevant examples drives significantly more adoption than generic courses
- Hands-on exercises with real work tasks produce better skill transfer than theoretical instruction
- Distributed AI champions programmes scale AI knowledge more effectively than centralised training
- Progressive skill-building (fundamentals then advanced) prevents overwhelming learners
FAQ
Frequently asked questions
Most employees need 2-4 hours of AI fundamentals plus 2-4 hours of role-specific training. Technical teams need additional days of hands-on implementation training. Ongoing learning through communities of practice and regular sessions is more effective than one-off training.
Basic AI literacy should be encouraged strongly for all employees, with mandatory training for teams directly using AI tools. Make it practical and relevant to their work rather than forcing abstract technology education.
Measure: AI tool adoption rates before and after training, number of AI use cases identified by trained teams, time saved on tasks where AI is applied, and employee confidence scores in using AI (pre/post survey). Track at 30, 60, and 90 days post-training.
Buy foundational content (AI basics, general prompt engineering) and build company-specific content (your tools, your data, your use cases). The company-specific elements are what make training actionable and are worth the investment to develop internally.
AI capabilities change rapidly. Review and update training content quarterly. Add new tools and use cases as they emerge. Supplement formal training with monthly 'AI updates' sessions that cover new developments and share internal success stories.
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