For IT, DX and data-platform teams looking to put internal data to work, this piece sets out the design choices behind RAG, knowledge foundations, vector search, document preparation, access permissions, metadata, PII protection and accuracy evaluation.
An explanation of Human in the Loop (HITL) from the angles of AI-operations design, approval flow, quality gates and lines of accountability. We unpick both the worry of leaning on AI too heavily and the inefficiency of people being involved too much.
A practical guide to connecting generative AI training for sales teams to real work: meeting preparation, proposal writing, minutes summaries, CRM entry and role-play. It also sets out the points executives, IT leads and marketing and sales leaders should weigh.
A look at the failures that tend to crop up early in a generative-AI rollout, viewed through the proof of concept, resistance on the ground, governance, training and impact measurement. A guide for those in industry on why AI adoption fails to take root, and how to put it right.
A guide that sorts the copyright risk of generative AI into three challenges — input, training, and output use — and sets out the steps for legal and compliance teams and the production floor to translate it into internal rules.
Sorting the information you must not enter into generative AI into categories such as confidential data, personal information, credentials and customer information. A reference table and decision criteria that IT, legal, HR and DX teams can use in internal rules and training.
For HR and DX leads: how to design AI training so company-wide reskilling actually takes root on the ground. We set out company goals, role-based skill maps, KPIs, and the measures that make adoption stick.
For executives and digital-transformation leads weighing generative AI investment, this piece sets out the impact on operational efficiency, customer experience, people development and risk, alongside a clear framework for moving from PoC to organisation-wide rollout.
A practical look at what manager-focused AI training should cover when it comes to supporting your team's use of AI. We set out the backbone of the programme: deciding which work to delegate, reviewing outputs, giving feedback, and managing risk.
For frontline staff and HR/DX leads frustrated by unstable generative-AI output, this piece explains how to write prompts that work at the office. It introduces a template built around purpose, context, format, constraints and examples.
How much of your AI usage logs should you retain? A guide for IT, security, audit and DX leads. We set out the essentials of auditing generative AI, accountability, retention periods, access rights and review history, and offer a practical way of thinking about log design you can actually use.
For business and corporate planners and department managers who lose time to producing weekly KPI reports, this article explains a setup in which a task-executing AI agent supports data collection, aggregation, anomaly detection, drafting suggested comments, and distribution.
