Enterprise AI workspace feature adoption is accelerating — but most organisations have a configuration, not an architecture.
Most enterprises have a configuration, not an architecture
- Microsoft reported Copilot for Microsoft 365 deployed across more than 600 enterprise customers in its first year, with over 70% of early users reporting individual productivity gains. McKinsey's 2023 enterprise AI adoption research found that organisations in the top quartile of AI value realisation were distinguished not by which tools they used but by whether they had a data and integration architecture underneath them. The majority had not.
- Gartner's 2024 Digital Workplace Strategy maturity assessment: fewer than 20% of enterprises had a defined digital workplace architecture — a documented model of how workspace interactions flow to operational intelligence. The rest had a configuration running on admin console defaults.
Decide which workspace signals feed your intelligence layer before M365 locks them in
Before your current M365 configuration locks in data flows that are difficult to reroute, three questions require explicit answers: which workspace signals should flow to your intelligence layer and under what governance, have you designed the anonymisation and consent framework, and what specific organisational learning question is your workspace data model meant to answer? That question is the starting point. If it does not exist yet, your workspace architecture does not exist yet either. D3 frames the workspace as the data surface layer of the broader platform architecture — connecting the two is a foundational platform design decision, not a feature request.
