AI adoption and intelligent automation
Microsoft Copilot, AI agents and workflow automation — introduced with the data governance and access controls that make them safe to switch on.
Most organisations can enable Copilot in an afternoon. The harder question is what it will be able to see once they do. AI tools inherit whatever permissions and oversharing already exist in the tenant, so adoption work starts with the data, not the licence. This practice area covers the readiness work, the automation itself, and the governance that keeps both defensible.
What this covers
Copilot readiness
Permissions and oversharing review, sensitivity labelling, retention alignment and licensing design — established before Copilot is turned on rather than after someone surfaces a file they should not have seen.
Agents and Copilot Studio
Scoping, building and governing custom agents: what they may access, how they are tested, who owns them, and how they are retired when the process they serve changes.
Workflow automation
Power Automate and Power Platform flows for the repetitive work that currently moves by hand, with environment strategy and connector governance so the estate stays manageable.
Governance and secure adoption
Purview policy, audit and usage visibility, acceptable-use guidance and the administrative model that keeps AI adoption reviewable as it spreads.
Typical starting points
- Copilot licences have been bought and nobody is certain what the data access looks like
- A pilot worked, and nobody has decided how it scales or who governs it
- Staff are already using consumer AI tools with company information
- There is pressure to "do something with AI" and no map of where it would pay off
Common questions
Do we need Copilot licences before this is worth doing?
No. The readiness work — permissions, labelling, retention — has value on its own and is usually the thing standing between a licence purchase and a useful rollout. It is often better done first.
Will you build us a custom AI model?
No. This practice is about adopting and governing Microsoft AI services and automation platforms safely, not training bespoke models. Where a problem genuinely needs that, it is worth saying so early.
Not sure where your risk actually sits?
Start with a scoped assessment of your Microsoft 365 and Azure environment.