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.
A guide to measuring the benefits of a generative-AI rollout along three axes — reduced workload, improved quality and contribution to revenue. We set out the thinking behind baselines, surveys, sampling, A-tests, attribution and the AI ROI calculation.
For DX leads and frontline managers unsure how to prepare for AI, this guide explains how to go about a business-process inventory, operational visibility, business analysis and BPR. It introduces how to spot candidates for AI from task lists, the distribution of effort and sub-task breakdowns.
An explanation of what hallucination is, for the front-line staff, IT, legal and DX teams who have just begun using generative AI. It sets out how mistaken answers arise, examples of where they crop up in business, and the steps for checking sources and human review.
