A high-volume healthcare distributor operating across multiple business entities
The client is a medical device distributor serving healthcare customers at scale, running Microsoft Dynamics 365 Finance and Supply Chain Management across several distinct business entities. Velosio has partnered with the organization through a multi-year D365 F&SCM rollout and the continued expansion of its broader Microsoft technology environment.
Complexity comes from the shape of the operation rather than any single system. Multiple entities mean multiple data sets, go-live timelines, and process variations. A high-volume customer service desk fields dozens of calls a day per rep, many from newer employees still building fluency in D365. And a sales organization without D365 licenses had no direct path to the order and inventory data it needed to serve customers. This is an organization that continually looks for better ways to work — which is exactly what made it a candidate for agents.
Why Copilot Studio, and why Velosio
The organization built its first agent internally in Microsoft Copilot Studio, proved the model on a single business entity, and then brought in Velosio to scale it. The criteria that shaped both decisions were practical: meet employees where they already work, ground answers in live ERP data, and move fast enough to hit a pending go-live date.
No new interface to learn
Agents were deployed directly into Microsoft Teams, where customer service and sales already spend their day. Employees type a question in plain language — no additional system to log into.
Answers from live D365 data
Rather than static documentation or cached reports, the agents pull from live Dynamics 365 data, so order status, tracking, inventory, and invoices reflect current reality.
A partner who already knew the environment
Velosio's multi-year history with the client's D365 F&SCM footprint meant no lengthy ramp-up. Both sides pointed to this as what makes a long-term partnership valuable.
A model built to replicate
Success on the first entity created a template. The goal was never one agent — it was a repeatable approach that could be extended entity by entity as the business grew.
A connected agent layer for customer service and sales
The organization started narrow and deliberate: one business entity, focused on the highest-frequency question types the CS team fielded. After that agent launched and adoption took hold, Velosio worked with the company over approximately four to five months to expand capabilities across the existing agents, handle edge cases, and stand up an agent for another entity ahead of its D365 F&SCM go-live.
| Workstream | What changed | Business value |
|---|---|---|
| Core agent build | Agents built in Copilot Studio and deployed in Teams, covering order status, tracking, inventory availability, and invoice retrieval from live D365 data. | Answers in seconds, inside a tool employees already use |
| Sales team enablement | Sales reps without D365 licenses gained conversational access to order and inventory information. | CS desk relieved of second-audience help desk load |
| Capability expansion | Velosio extended existing agents to handle edge cases and added dozens of new capabilities surfaced by CS team feedback. | Manual processes progressively eliminated |
| Multi-entity scale | A new agent developed for an additional business entity, timed to that entity's D365 F&SCM go-live. | Repeatable enterprise playbook, not a one-off pilot |
Adoption was designed in, not hoped for
The businesses held internal contests to name their agents. It sounds like a small thing, but it reflected something real about the rollout: when employees have a hand in something — even something as simple as picking a name — they show up differently. Adoption followed quickly and the response was unambiguous.
From multi-screen lookups to answers in seconds
The agents now handle thousands of interactions per month across 300 to 400 customer service and sales users who rely on them daily for information they previously had to dig for manually. Estimated time savings range from two to five minutes per interaction depending on the request. Across a high-volume team, that compounds quickly.
Handle time and hold time down
Reps answer routine questions inside the chat window instead of navigating multiple systems, cutting both average handle time and customer hold time.
Onboarding starts with the agents
Rather than requiring new hires to build D365 fluency first, teams now point them to the agents on day one as the fastest path to the information they need.
Sales serves itself
Sales reps get order and inventory answers directly, removing a standing queue of internal requests from the customer service team.
An accelerating build cadence
CS feedback continually surfaces new capabilities worth adding, and with an internal AI team now in place, the pace of development has increased.
| Metric | Before | After | Type |
|---|---|---|---|
| Invoice copy retrieval | ~3 minutes navigating D365 to locate the account, find the invoice, and reprint | Rep asks for the PDF; it arrives in seconds | Validated |
| Time per interaction | Multi-screen lookup, sometimes plus a Power BI report | 2–5 minutes saved per interaction | Customer-estimated |
| Sales access to ERP data | No D365 license; questions routed to customer service | Direct conversational access in Teams | Validated |
| New hire ramp | D365 fluency required before working efficiently | Agents used from day one as primary information path | Qualitative |
Why Microsoft Copilot Studio matters
The strategic value here is not that a chatbot answered questions. It is that Copilot Studio let the organization put live ERP data in front of people who could not otherwise reach it, inside the tool they already had open, without licensing every user into D365 or building a new front-end application. That changes who in the business can act on operational data — and it turns a governed ERP investment into an everyday productivity layer.
Just as important, the approach proved replicable. Building in Copilot Studio meant the first entity’s agent became a pattern the organization could extend to the next entity, then the next, with capabilities compounding as feedback came in. That is the difference between an AI pilot and an enterprise playbook.
A foundation built for CRM data and growth-oriented AI
The current agents draw on ERP data. The next step is expanding into CRM data, which would give the sales team a broader view of customer relationships and activity that the existing agents cannot provide today. With an internal AI team now established and a working multi-entity model, the organization has the foundation to keep extending agent coverage across the business.
Beyond that, the focus shifts from efficiency to growth: using AI not just to speed up what people already do, but to open capabilities that were not practical before. Velosio continues to partner with the organization on that roadmap — expanding agent capability, supporting additional entity go-lives, and connecting new data sources as the environment evolves.
