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 that company-wide reskilling actually embeds 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.
A guide to designing the permissions you grant a task-executing AI agent. It separates view, edit and approve, and covers time limits, IP restrictions and access reviews — the thinking behind running AI safely on least privilege.
A clear look at the basics of business-execution AI agents, from the angles of AI browser operation, SaaS automation AI, internal-system operation AI, API integration, connectors and guardrails.
An explanation, for HR leaders and the executive team, of the skills employees need in the age of AI agents. It sets out a reskilling approach to developing people who can put AI to work, covering task-design ability, supervision, critical thinking, dialogue design and more.
For companies with too many AI candidates to set priorities, this article explains how to build an evaluation matrix that compares them on impact, feasibility, risk, cost and reusability.
