A guide for customer-support leaders and supervisors on embedding FAQ AI, enquiry classification and a response-support AI through training. We set out a practical design that reduces over-reliance on individuals and holds quality steady.
A guide to building a system that gathers AI use-case ideas from the frontline. It shows how to grow use cases from the bottom up through submission forms, idea-sharing sessions, knowledge sharing and an internal suggestion scheme.
A structured set of ten themes to cover in company-wide AI-literacy training, for HR, DX and IT staff wrestling with how to design the curriculum. It organises everything from the basics of generative AI through practical use to risk management.
For companies where use on the ground fails to spread even after introducing AI, this article sets out why AI doesn’t bed in, resistance on the ground and the skew in usage rates. It explains adoption measures built on fit with the work, training follow-up and knowledge-sharing.
For DX-promotion and operational-reform leads weighing up which tasks to hand to AI in front of a catalogued task inventory, this piece works through the decision criteria—routine versus non-routine, judgement versus work, data availability, risk and human involvement.
A practical guide to building an internal AI tool allowlist. To curb shadow AI, we set out the criteria for deciding what to allow, allow conditionally or prohibit, plus how to draw up the list, handle exception requests and keep it under review.
A guide for marketing leaders on designing training that embeds SEO AI, advertising AI and landing-page improvement AI in everyday work. It shows how to streamline planning, first drafts and improvement ideas so that people can concentrate on judging quality.
A checklist-style guide for enterprises comparing business AI tools such as ChatGPT, Claude, Gemini and Copilot, organising the points to confirm across data retention, training use, data destinations, authentication, logging, permissions and support.
A task-by-task look at what generative AI can and cannot do, across email drafting, minutes, research, judgement work and more. We set out where front-line staff and managers should draw the line so they can start using it safely.
A look at how the autonomous operation of task-executing AI agents changes business automation by AI agents, always-on running, parallel processing, and KGI/KPI management.
A guide to the data-leakage, copyright and personal-information risks that most often arise in business use of generative AI, organised for IT, legal and front-line managers. We explain, through practical cases, the criteria for shaping internal rules.
An explanation of what an AI agent is, set against how it differs from a generative AI assistant. It runs through the points worth getting straight before corporate adoption — task execution, tool use, memory, observability and accountability.
