Start with the problem you need to solve. Explore team training, agents, automation, internal knowledge, custom models and alternatives to costly software.
Prepare the data evidence for a high-risk AI project: where examples came from, what they represent and which parts of the intended use remain untested.
A system's purpose and behaviour matter more than its product label. Prepare the feature description and unresolved questions for a prohibited-practices review.
Plan the information people see when they first meet an AI assistant, and separate that interface work from obligations concerning generated content.
A department name does not settle whether an AI system is high-risk. Prepare the intended use, output and decision process for a classification review.
The AI Act applies in stages, and the July 2026 amendment changed important dates. Record the provision and system behind each task before scheduling work.
Buying a model API does not settle your role in the finished AI system. Map the product, its intended use and the organisations involved before agreeing the implementation scope.
Supplier quotes, travel, campaigns and code fixes: follow the steps from an instruction to an action, including the evidence and approval needed along the way.
A meeting plan covering what to observe, which questions to ask and how to record the demonstration. Examples test mechanisms rather than business savings.
Describe one workflow, the expected result and access to company systems. Use this brief to request comparable proposals and separate development costs from the cost of operating the system.
The decision depends on staff availability, knowledge of company systems and responsibility after launch. Compare internal delivery, outsourcing and a shared project using the same workflow.
Choose a pricing model by examining what is known about the work. A change record helps compare supplier proposals and keep decisions visible.
Turn a new enquiry into a quote draft with approved prices, checked calculations, duplicate handling, versioned approval and a clear implementation budget.
A project takeover needs a clear view of the running system, control of dependencies and a test of the successor team. Use this technical inventory.
Set up ChatGPT Space and Pages for a team: document access, ownership, a first shared workflow, training costs and checks before wider use.
Compare ChatGPT Space, Projects and Claude Cowork on a business workflow: shared documents, file access, trial costs and practical selection criteria.
Plan a recurring report with ChatGPT Team Tasks: source access, the execution account, time zones, missing-data checks and a realistic process budget.
Migrate an integration to Claude Sonnet 5.5: check thinking, forced tools, HTTP 400 errors, streaming, trial costs and a practical rollback plan.
Prepare Codex Cloud for a company repository: reusable environments, network access, secrets, current limitations, costs and first-task acceptance.
Introduce Codex Code Review with useful context, verified findings, omission tracking and review-cost measurement while retaining human acceptance.
Connect process analysis, data preparation, integrations and team training in one implementation plan. Define delivery responsibilities and ongoing support.