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Enterprise AI copilots are being used for far more than drafting emails. Documented deployments include assistants embedded in workplace software and CRM, custom agents for operational or regulatory questions, clinical documentation support, recommendation systems for salespeople, and customer-facing service tools. These 21 examples show the range of work involved—but they are a curated set, not a census of proven, fully scaled systems.
21 documented enterprise AI copilot deployments
The examples below come mainly from Microsoft-authored customer stories and a Microsoft Learn catalog; Salesforce’s account of its own deployment is also vendor-published. A research paper provides technical detail about one Microsoft Sales Copilot system. Treat reported outcomes as attributed claims, not as independently verified results.
| Organization | What the copilot does | What the source reports |
|---|---|---|
| ABB Group | Genix Copilot connects generative AI with ABB’s industrial IoT and analytics suite so users can ask questions about operations and receive insights. Microsoft gives carbon emissions across plants as an example query. | Microsoft describes the solution and its intended operational use; no quantified outcome is stated in the cited account. |
| Accenture | Workforce rollout of Microsoft 365 Copilot and agents. | Microsoft reported in November 2024 that Accenture was rolling out the tools to 100,000 employees and had committed to deploy them to 200,000 more. Those are announced rollout figures, not confirmation that all seats were active. |
| Banco Ciudad | Employee assistance and custom agents using Microsoft 365 Copilot, Copilot Studio, and Azure. | Microsoft reported in 2025 that the bank freed 2,400 employee work hours annually and projected monthly savings of US$75,000. The dollar amount is a projection. |
| Commonwealth Bank of Australia | Employee AI skilling as part of work toward secure, customizable banking experiences. | Microsoft describes a structured skilling initiative; the cited account gives no quantified result. |
| Make-A-Wish | Microsoft 365 Copilot and Copilot Studio used alongside cloud and data services to unify data, rebuild applications, and support staff productivity. | Microsoft says the work is intended to protect sensitive family data; the cited account does not provide a quantified productivity result. |
| NASA Earth Science Data Systems Program | Earth Copilot, built with Azure OpenAI Service, for working with Earth-science data. | Microsoft’s November 2024 roundup says the program announced the solution. This is an announced solution in that source, not evidence of a measured outcome. |
| Chi Mei Medical Center | AI copilots using Azure OpenAI Service for operational workflows and patient touchpoints, including clinical documentation. | Microsoft’s 2024 customer story says doctors’ report-writing time fell from one hour to 15 minutes, and nurses’ documentation took under five minutes per patient rather than 10–20 minutes. These are vendor-published workflow figures, not an independent clinical evaluation. |
| EY | Microsoft 365 Copilot for employee and client-facing work, including routine-task automation. | Microsoft reported in 2024 that EY was scaling Copilot to 150,000 employees and that EY professionals reported productivity gains of up to 14 hours per week. Both figures are attributed to that report. |
| GHD | Microsoft 365 Copilot used to review requests for proposals. | Microsoft says employees reduced review time from hours to 15 minutes. The cited account does not describe an independent measurement. |
| Salesforce | Einstein Copilot deployed inside Salesforce, with intended workflows in sales, service, and marketing. | In its first-person vendor account, Salesforce said deployment began in February 2024 and thousands of employees used the assistant daily. |
| Microsoft Sales Experience (MSX) | A retrieval-and-reranking recommender integrated into the production Dynamics CRM used daily by Microsoft sellers, surfacing relevant content through a Copilot interface. | A 2024 paper by Singh and coauthors describes the system and reports an evaluation. The available account does not provide a comparable outcome figure for the other deployments here. |
| Dunaway | A Copilot Studio conversational agent for regulatory answers and compliance workflows. | Described in Microsoft Learn’s Copilot Studio catalog; no quantified outcome is stated in the cited description. |
| Grupo Bimbo | Copilot Studio used to standardize planning and execution across a global internal audit team. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| Holland America Line | A Copilot Studio virtual concierge for the company website. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| La Trobe University | AI and Copilot Studio supporting academic productivity and services. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| mobilezone | Copilot Studio, Dynamics 365, and MCP servers used to modernize service delivery and internal operations. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| Nexi Group | Copilot Studio and Azure AI services for customer interactions and payment-card support. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| Rabobank | Text- and voice-enabled Copilot Studio conversational AI for customer service. | Described by Microsoft; no quantified result is stated in the cited catalog entry. |
| Signetic | Power Platform and Copilot Studio supporting operational workflows and patient care. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| Singapore Civil Defence Force | Power Platform and Copilot Studio digital solutions for automating manual processes and supporting the workforce. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
| T-Mobile | A Power Apps and Copilot Studio application that gives representatives device information to support customer interactions. | Described in Microsoft Learn’s catalog; no quantified result is stated there. |
What these deployments reveal about enterprise copilots
“Copilot” describes several different kinds of system
Some examples put an assistant inside existing workplace software or CRM. Others use custom agents for a defined task, such as compliance questions, audit planning, or customer service. The MSX example is a recommendation system that retrieves and reranks content for sellers; it is not simply a general-purpose chat assistant. Customer-facing tools such as virtual concierges and conversational banking differ again because the user is a customer rather than an employee.
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Integration is part of the use case
The examples connect AI to industrial analytics, CRM, organizational data, business applications, and customer-service workflows. That context shapes what an assistant can help with: a natural-language interface to plant data, a content recommendation in a seller’s CRM, or device information presented to a service representative are not interchangeable deployments. The product label alone does not explain what information the system can access or where its output enters the work.
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Deployment stage matters as much as the tool
The set includes announced solutions, skilling programs, rollout commitments, catalog descriptions, and reports of ongoing use. Those stages are not equivalent. For example, an announced solution is not evidence of broad adoption, and a commitment to deploy a stated number of seats is not an active-user count. A customer story describing a workflow also does not establish how widely it is used.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a copilot example for your organization
Compare deployments with similar users and tasks rather than ranking them by headline figures. A clinical documentation measure, a proposal-review time, and an employee rollout count describe different things and cannot establish which system delivers the best return.
Rank #2
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- Workflow: Is the system drafting, searching, summarizing, recommending, answering questions, or automating a process?
- User: Is it intended for employees, specialists, customer-service representatives, or customers?
- System integration: Which business applications, enterprise data, or customer channels does it connect to?
- Stage and scale: Is the source describing a pilot, an announced solution, a rollout commitment, or reported day-to-day use?
- Human review: The accounts summarized here do not consistently explain what review occurs before an answer or action is used. Ask who checks outputs and what happens when they are wrong.
- Outcome measurement: Look for a stated baseline, the method and period of measurement, and whether the result is independently evaluated or reported by a vendor or customer.
The published examples do not use a shared evaluation method, so their reported figures are not a common ROI benchmark. Microsoft’s customer roundups and catalog are vendor-authored, Salesforce’s deployment account is first-person vendor reporting, and the MSX paper provides technical detail about one system rather than a comparative evaluation of all 21.
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Quick Recap
Rank #3
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