AI Body of Knowledge (AIBoK) is an independent GenAI learning system and training organisation for practical AI capability at work. We help professionals, teams, and organisations build practical AI capability; real skills, patterns, and workflows they can apply to real work.
AI Body of Knowledge is a practical body of knowledge, learning system, and training offer for people who need to use GenAI well at work. We help people build usable AI skills, repeatable workflows, and sound judgement for work; not theory, not hype, and not a slide deck with a quiz at the end.
The learning system breaks GenAI down into usable skills, repeatable patterns, practical workflows, and guardrails. It helps participants move from basic awareness to practitioner-level use and, where needed, strategic or governance-level capability.
Sessions can include reusable prompts, templates, diagrams, examples, and practical tools that participants can adapt after the training. The aim is not to memorise AI terminology. It is to know where to start, how to work safely, and how to build capability that compounds.
The body of knowledge is being developed as a reusable reference for practical AI capability, not as a closed slide deck or one-off course. Training, workshops, and advisory support help organisations apply that reference to their own tools, teams, risks, and workflows.
It is shaped by practical training delivery, research, field examples, and input from a small network of practitioners across AI, technology, cybersecurity, governance, and business operations.
We are not a tool reseller, a generic online course library, or tied to a single AI platform. We may work with vendors where it helps organisations build capability, but we do not recommend a tool because of a hidden commercial incentive or conflicted vendor relationship. Our focus is on building capability that transfers to the work people do every day.
Every training session starts with a short scoping conversation. We discuss the team, the context, and the goals before recommending anything. Module 1 is the usual starting point for most buyers. See all training options.
Teams across Australia and globally are already experimenting with AI tools. The evidence is clear: casual experimentation does not automatically become reliable business capability.
Individuals find useful shortcuts. A few people develop sharp instincts. Most of the organisation stays at the curiosity stage; occasional use, scattered results, no shared method for what good looks like.
AI Body of Knowledge exists to help teams move from scattered use to practical, repeatable, safer AI capability. Not by adding more awareness content. By building a shared foundation that people can actually use on Monday morning.
Our training is designed for mixed business and technical audiences. We regularly work with:
We work with for-profit organisations, not-for-profits, and government agencies. Sessions are shaped around the actual participants; their roles, their tools, and their goals; not a fixed curriculum for a generic audience.
AI Body of Knowledge was founded by Matt Tati and Si Pham, supported by a small practitioner network that helps shape the body of knowledge through real delivery, research, and field examples.
Co-founder & Principal Learning Architect
Matt brings experience in practical AI capability development, product leadership, and project management. His background spans travel technology, media, and IoT, including time as Former Chair of IoT Alliance Australia's Innovation Workstream. His focus is on making AI adoption practical and sustainable for real organisations, not just technically possible.
Co-founder & Principal GenAI Strategist
Si is a GenAI solution architect and ex-SIGINT practitioner with deep experience in enterprise solution architecture, cybersecurity, and national security and government-sector work. He brings a rigorous, operationally grounded perspective to AI capability and governance; one shaped by environments where the cost of getting it wrong is high.
AI Body of Knowledge training is independent by design. The focus is on building transferable capability — skills, patterns, and judgement that work across tools — not locking participants into a single platform or product.
The starting point remains vendor-neutral: participants learn transferable methods before any tool-specific or vendor-specific work follows.
We can work with vendors, platforms, and implementation partners where that helps an organisation build capability. What matters is transparency: no tool or vendor should be recommended because of a hidden commercial preference or a conflicted incentive.
Participants can apply the methods across whatever tools are already in use; Microsoft Copilot, ChatGPT, Google Gemini, Claude, DeepSeek, or specialist tools relevant to their context. Once a shared foundation is in place, tool-specific or platform-specific work can follow.
