Looking to deploy a proof of value discovery of Microsoft Copilot to ~150 back-office staff. My question is for organization who have successfully delivered to a similar scale: What size and make up of delivery team did you have? What is the typical investment of time from a general copilot user/consumer, in order for them to derive value from the tool?

Our PoV approach, delivery team, and time investment

We started very conservatively and in waves. First, we onboarded five IT directors and one IT architect to validate governance and guardrails. Next, we expanded to our managing directors (CFO, CIO, and business unit leads) to secure sponsorship and set the tone. From there we launched a pilot group, initially IT-heavy, then progressively business teams.
We’re now at ~200-250 users. Usage has polarized over time: ~40% are intensive users, while ~60% tapered off after the initial enthusiasm. The most measurable success so far is GitHub Copilot: our developers consistently realize ~2,000-3,000 accepted lines of code per month through Copilot, which is a clear productivity signal for engineering.
Delivery team (size & makeup)

For a ~150 back‑office PoV, this lightweight structure worked well for us:

Core squad (4–6 people):
- Product owner (IT) to own scope, backlog, and success metrics
- M365 platform engineer/administrator for enablement, licensing, and guardrails
- Security & compliance/Privacy rep to handle data boundaries, DLP, and approvals
- Change & adoption lead (comms, training assets, office hours)
- (Optional but helpful) Analytics lead to instrument usage dashboards and signal where to coach

Extended network (8-12 champions) across Finance, HR, Operations, Customer Service, etc., each accountable for 2-3 role‑based scenarios and peer coaching in their area.

This kept governance tight while giving every function a clear point of contact and a feedback loop.
Typical time investment for a general Copilot user

We kept the ask deliberately small and habit‑forming rather than heavy training:
- single enablement session (~60–90 minutes) to cover prompting basics, data boundaries, and 3–4 role‑specific scenarios (e.g., email triage, doc drafting, meeting prep, spreadsheet analysis).
- A two‑week “habit sprint” with micro‑tasks (≈10 minutes/day) embedded in real work (replace, not add to, existing tasks).
- Optional weekly office hours (30 minutes) for Q&A and sharing “what good looks like.”

In practice, most general users started seeing tangible value within the first two weeks, provided we gave them role‑relevant prompts and templates and nudged them to apply Copilot to work they were already doing (not net‑new work).

What we’d repeat (and what we’d avoid)
- Start with leaders + IT, then seed cross‑functional champions who own real use cases in their teams.
- Make success scenario‑based (e.g., “reduce inbox triage time by X” or “first draft quality for customer emails”), not tool usage for its own sake.
- Instrument usage and outcomes early so you can spot the “taper” cohort and target coaching.
- Keep governance simple but explicit (data boundaries, external sharing, sensitive content).
- Avoid broad, one‑size‑fits‑all training; short, role‑based enablement beats long generic sessions.

We’ve worked on a very similar rollout with our AI-native workspace that teams use alongside or instead of Copilot.

In that project, the customer had a delivery team of just two people. Most of the enablement was handled by us. We sat with their teams, mapped the right use cases, and helped them build the first set of agents they could start using within a week. After that, it became largely self-serve most users were able to create or tweak their own assistants in a few minutes.

The first phase went live in under a week for ~100 users, and the rest of the organization (around 300 people) was onboarded over the next few weeks. They chose us mainly for the hands-on support and the fact that the platform is built AI-native rather than bolt-on.

Happy to share more details if you’d like to compare approaches or understand what worked well for them.