Foundation: a clean tenant.
Most of the conversations we have about AI start with a more boring conversation about the underlying environment. Conditional Access. Defender. Purview. A Copilot rollout reads everything a user has access to — which means oversharing becomes a data-exposure event the moment Copilot is enabled. Sensitivity labels. Sharing policies. Retention. The basic governance hygiene that nobody got around to in the rush to ship Microsoft 365 in 2020.
We do this work as the first half of any AI conversation. AI tools amplify whatever's underneath them. Clean foundation first.
Augmentation: Copilot for Microsoft 365.
Copilot lands well for organizations that have already done the work of cleaning up their SharePoint permissions, defined sensitivity labels, and trained their teams on what an LLM is and isn't. It lands poorly for organizations that turn it on hoping it'll fix the underlying mess.
For the customers we work with, the path looks like this: Copilot Readiness assessment → permission remediation → sensitivity-label rollout → Purview governance → pilot Copilot to a controlled group → measure → expand.
Honest about the limits: Copilot is great at summarizing, extracting, drafting, and explaining content that already lives in your tenant. It's less great at workflow automation, voice interactions, domain-specific reasoning, or anything that needs to reach systems outside Microsoft 365.
Custom AI: when Copilot isn't enough.
For the workflows Copilot can't reach — voice receptionists, custom domain assistants, task-specific automation, integrations with line-of-business systems — the right answer is usually a custom AI layer built on top of the Microsoft 365 environment we just modernized.
This isn't our core practice — our work stops at the foundation and the Microsoft-native augmentation layer. For the custom-AI piece, we work with an AI automation partner that builds production AI on top of Microsoft 365 environments. They handle the AI build; we handle the M365 environment they build on.