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AI voice agents are already handling tasks such as answering support calls, taking restaurant orders and scheduling healthcare appointments. But the label covers different levels of automation: some systems resolve routine calls on their own, while others help a human representative during a conversation. The 13 examples below show what organizations and vendors say is in use, and how much detail is available about each deployment.
What counts as an AI voice-agent deployment?
A voice agent is useful to assess by its job, not its label. In these examples, the job ranges from answering common questions to taking an order or scheduling an appointment. Some sources describe systems that speak directly with callers; others describe AI tools that support human representatives rather than replace them.
- Autonomous or self-service: The system speaks with a caller and attempts to answer a question or complete a task.
- Task automation: The system handles a defined transaction, such as placing a restaurant order or booking an appointment.
- Agent assistance: AI finds or summarizes information for a human representative, who remains on the call.
These distinctions matter when comparing results. A reduction in transfers from a self-service phone path is not the same measure as order conversion, appointment scheduling, or faster information retrieval for staff.
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13 named deployments and customer examples
The table combines detailed customer stories with vendor story-index listings. Its rows are not equally documented: the DoorDash and Donatos accounts provide rollout and outcome details, while several index entries supply only a named organization and the outcome area highlighted by the publisher.
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| Example | What the source describes | What is reported, and how much is established |
|---|---|---|
| DoorDash | A Dasher-support self-service path, followed by a generative-AI contact-center solution using Amazon Bedrock, Amazon Connect Customer and Amazon Lex with Anthropic’s Claude. | AWS says the earlier IVR experience reduced agent transfers by 49%, increased first-contact resolution by 12% and saved $3 million year over year. These figures belong to the earlier IVR account; the case page also describes a later generative-AI rollout, so they should not be attributed to that change. The generative-AI solution was tested in early 2024 and later rolled out to all Dashers. AWS says it fields hundreds of thousands of support calls daily. |
| Donatos | Phone ordering across 174 restaurant locations, integrated with the chain’s proprietary point-of-sale system and payments. | Revmo’s September 12, 2025 announcement says a seven-wave rollout was completed in June. Through August, the vendor reported 301,388 calls handled, 71% order conversion versus 58%, and 99.9% order accuracy. It also reported 4,840.8 labor hours reallocated in August 2025. These are vendor/customer-announcement figures, not independently audited results. |
| Virtua Health | Syllable says its inbound scheduling agents went live in three weeks and schedule across multiple departments with Epic integration. | The customer-story page describes the deployment and integration; it does not provide a deployment-specific outcome metric in the available account. |
| Bell Canada | Google describes customizable contact-center solutions with AI-powered agents for business customers. | Google’s 2025 customer-experience document reports $20 million in savings across customer operations. It does not establish that this figure is savings from voice agents alone. |
| Mahindra Finance | Sarvam’s story index lists voice agents for sales, collections and employee engagement. | Sarvam claims the agents handled more than one crore calls in 12 Indian languages. The index-level account does not provide comparable methodology or a detailed rollout description. |
| Carter’s | Listed by PolyAI in a case-study index under seasonal hiring. | The index identifies the customer and topic, but does not supply enough underlying detail here to assess the deployment or its results. |
| SafeRide Health | Listed by PolyAI in a case-study index focused on call volume. | The index does not provide enough methodology or deployment detail for comparison. |
| Pacific Gas and Electric | Listed by PolyAI in a case-study index focused on labor hours. | The index does not establish a comparable result or the method used to calculate it. |
| UniCredit | Listed by PolyAI in a case-study index focused on net promoter score. | The index does not provide enough information to evaluate the measure or attribute an outcome independently. |
| Audibel | Listed by PolyAI in a case-study index focused on abandonment. | The index does not provide the underlying definition, baseline or measurement period. |
| Fogo de Chão | Listed by PolyAI in a case-study index focused on customer satisfaction. | The index does not provide enough detail to assess the measure or compare it with other deployments. |
| Hawksmoor | Listed by PolyAI in a case-study index focused on missed calls and bookings. | The index does not provide underlying methodology sufficient to compare any reported outcome. |
| Howard Brown Health | Named in PolyAI’s live case-study index. | The index listing establishes it as a named customer example, but the available summary does not describe the deployment or its results in enough detail to assess them. |
PolyAI’s index also names Quicken, a UK pub-restaurant chain, Atos and Simplyhealth, among others. Those index listings add examples but do not, by themselves, establish a common level of deployment detail or evidence.
What the detailed cases reveal about implementation
DoorDash: separate the older self-service results from the later generative-AI rollout
AWS describes DoorDash’s initial IVR experience as a way to resolve common Dasher questions and reduce transfers to live agents. The 49%, 12% and $3 million figures are reported for that existing self-service path. The same AWS case page describes a subsequent generative-AI solution, tested in early 2024 and later rolled out to all Dashers. Do not treat the earlier IVR figures as proof of the generative-AI system’s impact.
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AWS says the later system uses a knowledge base and was intended to expand toward taking actions through a logistics workflow service. The case page also reports a response latency of 2.5 seconds or less using Claude 3 Haiku and 50% less generative-AI application development time using Amazon Bedrock. These are AWS-published case-study claims, not independent measurements.
Donatos: connect the conversation to the transaction
Revmo says its ordering agent integrates with Donatos’ proprietary POS and payment systems. That connection is central to the task: an ordering agent must do more than recognize a caller’s request if the order and payment are to reach the restaurant’s operating systems. Donatos President Kevin King said the rollout met expectations for customer experience and revenue; this is a customer statement published in the vendor announcement, not an independent evaluation.
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Healthcare scheduling: integrate with the appointment system
Syllable says Virtua Health’s scheduling agents work across multiple departments and integrate with Epic. That is a more specific description than simply saying an agent answers calls: the system is presented as part of the scheduling workflow. The page also quotes a MemorialCare vice president saying Syllable replaced the organization’s navigation-center phone tree with AI agents in under eight weeks and that AI answered 100% of calls. That quotation describes a separate customer account, not Virtua Health’s deployment.
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The detailed accounts do not describe a simple choice between AI and people. AWS says routine Dasher inquiries can free live agents to handle more complex issues. Revmo says human staff focus on edge cases and escalations after its ordering agent handles calls. Those descriptions make escalation part of the service design, rather than evidence that every caller or situation can be handled automatically.
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When assessing a deployment, look for what happens when the system cannot complete a task: whether a caller can reach a person, which cases are transferred, and whether useful conversation context reaches the human agent. The available DoorDash and Donatos summaries describe human escalation or focus areas, but do not fully specify transfer rules or how context is passed in every case.
Why the published numbers are not a vendor leaderboard
The outcome figures measure different things over different periods and come from different publishers. Donatos’ order-conversion comparison, DoorDash’s transfer and first-contact-resolution figures, and Google’s Bell Canada savings figure are not interchangeable. Most are published by a vendor, cloud provider or customer in a vendor-hosted story; the accounts described here do not establish independent validation or a shared measurement standard.
- Check the baseline: DoorDash’s reported transfer reduction and first-contact resolution improvement are linked to its existing IVR account. Donatos’ conversion figure is given as 71% versus 58%, but the announcement does not establish a common benchmark with another business.
- Check the period and scope: Revmo reports call and accuracy figures through August and labor hours reallocated in August 2025. Google’s $20 million figure applies to Bell Canada customer operations, not explicitly to voice agents alone.
- Check whether the figure is customer-specific: Syllable says its customers automate more than 5 million calls annually in aggregate. OhMD’s healthcare page reports that practices using its voice and text AI agents cut phone time by 66% and grew bookings by 25% in aggregate. Neither figure should be assigned to a particular named customer without a customer-specific account.
- Check the depth of the source: A vendor index can identify organizations and the metric areas a case story highlights, but it is not the same as a detailed account with a defined baseline, period and method.
Google’s 2025 document also describes Discover Financial tools that help 10,000 contact-center representatives search and synthesize information during calls. That is agent assistance, not evidence that autonomous voice agents handled those calls, so it should not be counted as an autonomous voice-agent deployment.
How to judge whether a deployment is useful for your organization
Start with a repeatable caller task, then evaluate whether the system can complete it within the organization’s actual workflow. A demo that sounds natural does not establish that orders reach a POS, appointments reach the scheduling system, or exceptions reach a qualified person.
Quick Recap
- Define the task and autonomy: Is the system answering FAQs, routing calls, taking orders, scheduling, or assisting a human? What is it allowed to do without approval?
- Map the workflow integration: Identify the knowledge base, POS, payment, EHR or other system the agent needs to read from or write to. Donatos’ POS connection and Virtua Health’s Epic integration illustrate why this matters.
- Set escalation rules: Decide which requests require a person, how quickly a caller can reach one, and what context is carried into the handoff.
- Measure the relevant outcome: Choose a task-specific baseline and time period before rollout. Track a result that matters to the service—such as successful task completion, transfers, order accuracy or appointment completion—rather than relying on an unrelated headline metric.
- Record rollout scope: Distinguish a pilot or test from a production rollout, and document locations, call volume, languages and changes over time.
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