For companies where, after firm-wide AI training, adoption stalls on the ground, this article sets out how to design department-by-department AI training. It covers role-based use-case mapping, pre-work, output exercises, on-the-job learning and embedding support, showing how to make it work as practical, hands-on training.
For people on the ground keen to extend generative AI into document creation, summarising, translation and research, this article sets out usable task examples, points to watch and how to measure the effect. It offers practical examples you can try in HR, finance, planning and support work.
A business-focused guide to the differences between ChatGPT, Claude and Gemini. We compare security, data handling, features, operations and cost to help you choose the generative AI that fits your organisation.
For those tasked with drawing up a generative AI guideline, this article organises scope of application, prohibited inputs, tool selection, logging, copyright, personal data, the response to breaches, and review frequency. It offers example items you can use as a first draft of internal rules.
For front-line managers, IT teams and DX leads who, having got generative AI in the door, are unsure "what to use it for", this article explains how to choose easy-to-start work such as drafting, summarising, rephrasing, research and minutes.
For anyone who loses time writing up minutes after a meeting, this piece sets out how to draft and summarise minutes with AI. It walks through preparation beforehand, recording and note-taking, and the points worth checking once the output is in front of you.
A clear, beginner-friendly explanation of what generative AI is, written for business teams, IT, HR and DX leads. We set out how it works, what it can and can't do, and what to watch for when putting it to work.
Introducing a case study on how Kanata’s AI Chat reduced internal HR and general affairs inquiry handling by 91%. This article explains the concrete steps and key success factors behind automating first-line inquiry responses at a 500-employee company by organizing internal policies, FAQs, and application manuals so they could be referenced by AI.
