Answers for operators putting AI to work.

Write one row per test case before the agent goes live. Download the worksheet and inspect a saved-report check with eight synthetic failure cases.

What a managed AI worker fee covers, what can be billed separately, what work stays with your team, and a blank worksheet for comparing two provider proposals.

A procurement checklist for AI deployments: access scope, retention, deletion, training, subprocessors, approvals, audit records, incidents and exit, with what the model providers actually publish.

Five tests for choosing the first workflow, a worked scoring table, and the workflows to leave for later.

The five drivers that set the price of an AI workflow install, a line-by-line cost breakdown, and the tool costs people forget.

Twelve questions that separate partners who install working systems from partners who sell demos, with good answers and red flags.

What the EU AI Act means for services firms automating internal work: AI literacy, the employment uses listed as high-risk, and GDPR limits on automated decisions.

One sample task sent through workflow automation, a prompted chat session, an agent and a managed worker, showing that capability and who keeps it running are separate questions.

The operating map, context layer, connections, controls, acceptance test, launch, and ownership behind a real implementation.

The model usually works. Access, data, ownership, controls, and adoption are where the project stops.

Four routes for a recurring job: buy software, hire a person, build in-house, or use a managed AI worker. A decision table, the failure modes, and how to test a small scope first.

Why context is more than a knowledge search box, and why source quality still limits the system.

How review mode, narrow authority, escalation, and readback make AI systems usable in real operations.

The five scope drivers and why project sizing is useful even when it is not a price.