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.
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.
