1
scopeProject scope & goals
Clarify the scenarios you want to improve, expected outcomes, and whether the focus is workflow improvement rather than installing a single tool.
AI Data Handling & Principles
AI Data Handling & Implementation Principles
This page is an early-stage governance guide for discussing data scope, access, hosting, human review and operating responsibility in an AI project. It supports discovery and internal alignment but is not legal advice or a substitute for the organisation’s policies.
- Clarify data sources and scope before talking about models and features
- Confirm access permissions and deployment options before integration and rollout
- Set realistic expectations — AI projects usually iterate on knowledge, process and permissions
Practical scope
What AI projects usually discuss
The six items below are not a fixed checklist — most projects clarify these directions over time; depth and order depend on your organisation.
2
dataData sources & availability
Understand which documents, sites, internal systems or folders the data comes from — and what is appropriate to use at this stage.
3
accessAccess permissions & roles
Discuss who can use the system, who administers it, and who can see outputs — aligned with department roles and internal policies.
4
deploymentDeployment approach & security
Discuss public cloud, on-premise, private cloud or hybrid models, plus security and approval requirements.
5
monitoringLogging, monitoring & review
Whether you need query logs, usage records, error tracking or internal review data for ongoing governance.
6
rolloutPhased rollout & responsibilities
Start with a smaller pilot, expand gradually, and clarify internal/external ownership and maintenance.
What we do not assume on day one
- That every internal document is ready for AI use without preparation
- That one permission model fits every department
- That an AI project needs no ongoing tuning after launch
- That model capability alone replaces process design and data preparation
This page is a conversation starter, not legal advice
Every organisation differs in data policy, security requirements, approval processes and deployment constraints. This page helps align expectations early — what we will discuss, and what we will not assume upfront. Final arrangements are confirmed in project agreements.
Align technical and business stakeholders
Put "where data comes from, who can access it, where it runs, and how we support it after launch" on the same page — reducing mismatched expectations.
Support assessment and preparation
Before major build or procurement, clarify data inventories, permission questions and pilot scope so prerequisites are realistic.
Set phased, realistic expectations
AI initiatives usually iterate on knowledge sources, process and permissions — pilots and phased rollout keep adjustments manageable.
FAQ
AI governance planning questions
Direct answers about scope, delivery and practical next steps.
Book an AI consultation
Confirm usable AI routes, implementation scope and ongoing care around your tools, data requirements and target work.