All field notes

Training on AI tools usually covers features and skips limits. This checklist covers what users actually need to work with a system that is sometimes wrong.

Most of the value of an onboarding is created before the engineer starts. Here is the checklist that makes week one productive.

Payment routes, milestone structures and the friction each one carries. The simplest answer is usually to pay a US entity.

Standups across time zones work when they are short, written-backed and scheduled inside a committed overlap window.

Scaling works when the first engineer has built the foundations. Here is the sequence that avoids the classic second-hire slowdown.

Protection comes from contract terms, architecture and access design rather than from geography. Here is what each one should cover.

Banking has the volume agents need and the supervision regime that makes them hard. This guide shows bank executives where agents fit, how they sit alongside model risk management, which deployments come first, and what examiners will ask.

Schools and colleges face an AI conversation dominated by cheating and chatbots. This guide gives education leaders the more useful frame: administrative relief, student support, and the boundaries that protect learning and data.

Healthcare's administrative burden is where agents pay off immediately; its clinical decisions are where caution is warranted. This guide gives healthcare executives the sequence, the boundaries, and the governance.

Insurance runs on document-heavy, rule-bounded work, which is where agents earn their cost. This guide shows insurance executives the sequence across claims, underwriting support, and service, and where regulatory limits apply.

Logistics is exception management at scale, which is exactly what agents absorb. This guide shows logistics executives where agents fit across shipment, carrier, and customs work, what stays human, and how to measure.

Manufacturing's AI conversation is dominated by the shop floor, but the fastest returns are in the paperwork around it. This guide shows manufacturing executives where agents fit, what stays with control systems, and how to sequence.

Agents break the link between revenue and hours, which is the foundation professional services firms are built on. This guide shows firm leaders how to encode expertise, reprice, protect judgment, and keep developing people.

Public agencies have the backlogs agents were made for and the accountability standards that make them hard. This guide shows public sector leaders where to start, what must stay human, and how to procure and govern.

Retail has the volume, the margins pressure, and the customer exposure that make agent decisions consequential. This guide shows retail executives where agents pay off, where brand risk sits, and how to sequence.

Bias testing works when it measures outcomes across groups rather than checking intentions. This checklist covers defining, measuring, and monitoring disparity.

Consent for AI processing has to be specific, recorded, and withdrawable — and withdrawal must actually reach every system. This checklist covers the mechanics.

AI systems copy data into more places than teams realise — indexes, caches, logs, warehouses. This checklist covers retention and deletion that actually reaches all of them.

A model's self-explanation is another generation, not an account of its computation. This checklist covers explaining decisions in ways that are actually true.

AI is now a CEO-level decision, not a technology project. This guide explains what the chief executive owns in an agentic AI program, the decisions only the CEO can make, the numbers to ask for, and how to run the first quarter.

The CFO decides whether AI is a cost line or a capacity engine. This guide explains agent economics, the budgeting model that fits, the controls finance must insist on, and how to tell real returns from projected ones.

Revenue teams lose deals to slow response, inconsistent follow-up, and manual proposal work. This guide shows CROs where agents remove those losses, how to keep reps and customers in control, and how to measure the result.

When agents do defined work, the CHRO owns the questions nobody else will: what happens to roles, how people are told, what skills the company now needs, and how HR's own processes use AI without losing trust.

The CIO turns AI ambition into an operable platform. This guide covers the shared layers agents need, how to govern agent access to enterprise systems, vendor and model strategy, and how to keep shadow AI from outrunning control.