Copilot
Microsoft 365 Copilot
Generative AI inside Word, Excel, Outlook and Teams, grounded in the organization's own content and bounded by each user's existing permissions.
Legal · Modern Work & Automation
Thirty-six hours a week returned to the business teams
One of the world's largest law firms, Global. Operational and administrative teams were spending substantial time on document creation, reporting and data analysis — work that was necessary, repetitive and entirely non-billable.
The situation
What the organization was dealing with
Operational and administrative teams were spending substantial time on document creation, reporting and data analysis — work that was necessary, repetitive and entirely non-billable.
Law firms have an unusual economic structure: the people who generate revenue are also the people whose time is most expensive to waste, and a large share of firm activity is written work that is necessary and unbillable. That makes generative AI a more natural fit for legal operations than for most sectors, and also more sensitive.
What was done
The work, and the part that was actually hard
The firm was an early adopter of Microsoft 365 Copilot, starting with several hundred licences and taking a deliberate 'coalition of the willing' approach to adoption rather than a mandated rollout.
What stands out is the adoption model rather than the technology. A 'coalition of the willing' — starting with several hundred licences among people who wanted them — produces internal advocates rather than compliance. In a partnership structure, that distinction is the difference between a tool people use and a tool people were told to use.
Results
What was published
These are the figures exactly as reported in the source. Nothing has been rounded, extrapolated or restated.
Saved weekly on content and analysis36 hrs
Licences at first wave100s
Report turnaroundFaster
What changed
- Document creation and reporting streamlined across operational teams
- Workflows optimised and time on low-value tasks reduced
- Faster and more accurate reports improving internal interactions
- Adoption led by volunteers, which built genuine internal advocacy
Platforms involved
What each product was doing here
Azure & data
Microsoft Azure
The cloud platform underneath: compute, managed data services, networking and the governance model around them.
What transfers
If you were to attempt this
The staged, volunteer-led rollout transfers to any partnership or professional structure where mandating a tool is culturally difficult and where early advocates carry more weight than a communications plan.
Where it usually gets harder than expected: The published saving is for operational and administrative teams, not fee earners. That is a meaningful qualification: the firm was careful about where it claimed value, and any firm repeating this should be equally careful about which population it measures.
How we would take it on
Our approach to Modern Work & Automation work
1
Check what you already own
Most organizations are licensed for more than they have deployed. This step frequently reduces the engagement we were about to be paid for.
2
Assess permissions before AI
Copilot reaches whatever the user can reach. A decade of casual oversharing becomes visible on day one, and it is entirely avoidable if you look first.
3
Pick scenarios with the business
The functions that will use it know where their week goes. Scenario selection done inside IT produces pilots that impress nobody.
4
Measure in tiers
Usage, then time and quality, then business KPIs. Anyone promising the third before the first exists is guessing.
Recognise the problem?
If any of the above describes your organization, tell us where it hurts most. We will tell you what the same platforms could realistically do in your environment, what we would measure, and whether we think it is worth doing at all.
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