For teams where reviews of AI-generated text and deliverables vary from person to person, this article explains how to build AI review criteria, an evaluation rubric, and a hallucination check.
We set out why AI PoCs stall at the trial stage and lay out how to run an implementation project that carries through to production, operational design, governance and building AI capability in-house.
For companies running several SaaS tools internally, this article explains the fundamental design of business automation through AI SaaS integration. It works through iPaaS, API integration, webhooks, OAuth, access control and audit logs.
An explanation, for HR leaders and executives, of the skills employees need in the age of AI agents. It sets out the thinking behind reskilling that develops capable AI talent—business design, supervision, critical thinking, dialogue design and more.
How to move AI use beyond individual effort and build it into HR appraisal, grading systems and development plans. We set out the design points for skill maps, behavioural indicators and goal setting.
A practical guide to designing generative-AI training for corporate planning departments — covering market research, competitor analysis and management-document preparation, from PEST and 3C through fact-checking and using Kanata.
For legal teams struggling with a contract-review backlog, this article explains how to use generative AI for a first-pass check, and how to embed clause checklists, risk management and confidentiality controls through training.
For DX leads and department managers who struggle to explain the impact of generative AI, this article sets out how to visualise workload reduction using time logs, baselines, surveys and dashboards.
A practical guide to early-adoption KPIs for the DX, IT and HR teams rolling generative AI out across the organisation. It explains how to make usage, scope of application and outcomes visible, and how to design metrics that feed a continuous improvement cycle.
We map out the risks of letting AI agents use personal IDs, and explain the key design points for non-human IDs, service-account AIs, machine identities, audit logs, key management and lifecycle management.
For companies starting to entrust work to autonomous AI, this article explains how to design risk-sensitivity training that prevents overconfidence on the ground. It covers scenario exercises, permission management, data protection, AI governance education, and post-incident review.
After AI training, usage clusters around a few staff, questions feel hard to ask, and the front line never quite runs on its own. For people in transformation, HR and IT functions facing those problems, this article sets out how to design internal communities and study sessions.
