AI voice agents are changing contact centers in two distinct ways. In the first, an automated voice agent talks to the caller directly and tries to resolve a routine request or route it to the right place. In the second, a human agent gets real-time help: suggested answers, filled-in forms, call summaries, and compliance prompts. Many platforms offer both, so the most useful question to ask of any deployment is which of the two jobs it is doing.
The clearest evidence comes from named customer case studies and vendor announcements. These describe what the organizations that published them reported for a specific deployment. They are not independent industry averages, and none of them promises a similar result somewhere else.
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Two jobs that often get lumped together
Vendors often present “AI voice agent” as a single product. In the deployments described by their makers, the technology does two different jobs, with different risks and different ways of measuring success.
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| Dimension | Automated voice self-service | Agent assistance |
|---|---|---|
| Who speaks with the customer | The AI agent | A human agent, supported by AI |
| Typical job | Answer or route routine requests, such as app troubleshooting, sign-ups, and payment questions | Surface knowledge and policy, guide workflow steps, fill in forms, and summarize interactions |
| Example in the cases reviewed | DoorDash’s generative-AI extension of its IVR for Dasher support | U.S. Bank’s deployment of Pega Voice AI for employees handling complicated or emotionally difficult cases |
| Measures the cases emphasize | Transfers, containment, and first-contact resolution | Average handle time, customer satisfaction, coaching, and compliance monitoring |
| What to watch | A caller who cannot reach a person when the automation fails | An employee who trusts a poor suggestion, or a tool that adds steps instead of removing them |
Some platforms offer both jobs under one roof. The evaluation questions later in this article apply to each job separately, because a system that is good at answering a password-reset question may not be the right tool for guiding a staff member through a difficult account dispute.
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What the reported figures measure
The numbers below come from the organizations named in each row. Each is tied to a deployment, a publisher, and a date, and most of them cannot be compared with one another.
| Reported figure | Deployment | Publisher and date | Baseline or caveat |
|---|---|---|---|
| 30% lower average handling time; six-percentage-point improvement in customer satisfaction | Kotsovolos, after its Dynamics 365 Contact Center deployment | Microsoft customer story, 2026 | Baseline: not stated in the customer story. A single company’s reported outcome. |
| About 400 human agents; five million annual customer interactions | Kotsovolos, stated scale before the reported deployment outcomes | Microsoft customer story, 2026 | Context for the outcomes above, not an outcome in itself. |
| 49% fewer agent transfers; 12% increase in first-contact resolution; $3 million in year-over-year operational savings | DoorDash’s existing Connect Customer/IVR experience, as reported by AWS. The figures are not attributed to the later generative-AI layer. | Amazon Web Services case study; publication date not stated on the reviewed page | Baseline and measurement period: not stated. |
| Eight seconds lower average handle time; two-point increase in customer satisfaction | U.S. Bank, using Pega Voice AI | Pega customer case; publication date not stated on the reviewed page | Baseline and method: not stated. Do not set against other cases without matching methods. |
| 66% of calls contained without human intervention | CarShield’s Pre-Call Screening AI Agent | Cisco announcement, September 30, 2025 | One deployment and one call type. Containment is the vendor’s measure, not a quality score. |
| 40% of contact-center volume is voice; 75% of contact-center leaders say legacy technology prevents true omnichannel service | Market-level claims, not a deployment | Zendesk announcement, June 24, 2026, citing Zendesk’s own survey work | Survey sample and method: not stated in the announcement. Treat as a vendor’s view of the market. |
Two cautions apply to the whole table. A contained call is one that the caller did not need to transfer to a person; it says nothing about whether the caller’s problem was solved. And every figure here is a statement by a vendor or customer, not an independent measurement.
How the deployments divide the work
Extending an existing IVR for routine questions (DoorDash)
DoorDash added a generative-AI layer to its existing Connect Customer IVR to support Dashers, its delivery workers. The routine questions it targets include app troubleshooting, sign-ups, and payment options. DoorDash says the aim is to reduce transfers and resolve more requests before a live agent is involved. The case also describes A/B testing and an evaluation framework used to judge the system, and it calls latency important for a live conversation. The product lead put the goal this way:
“We wanted to empower Dashers to get help with their most common questions and issues as quickly and efficiently as possible, saving them time, effort, and increasing their trust in DoorDash’s self-service capabilities,” — Chaitanya Hari, Contact Center Product Lead, DoorDash.
DoorDash also says its live agents were freed to handle higher-complexity issues. That is the company’s own description of the change in work, not an independent finding.
Giving staff the call history and the transcript (Kotsovolos and Zendesk)
Microsoft’s Kotsovolos customer story describes transcription, summarization, and sentiment analysis across voice and digital channels, with customer history surfaced to employees. The idea is that a person who picks up a call should not start from zero. Zendesk makes the same argument from the platform side. Its June 24, 2026 announcement describes a unified desktop that combines voice with customer context and history from digital channels, and its CEO framed the problem this way:
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- Your conversations stay private and yours: Compliant with ISO 27001, ISO 27701, SOC 2, HIPAA, GDPR, and EN 18031, with zero data used to train AI models. Trusted by 2.5M+ professionals, including legal, medical, and business professionals handling sensitive information
“Voice can no longer live in an isolated operational silo,” — Tom Eggemeier, CEO, Zendesk.
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Real-time guidance inside the employee’s workflow (U.S. Bank, through Pega)
Pega’s U.S. Bank case describes an augmentation model for employees who handle complicated or emotionally difficult cases. The features it lists are:
- Real-time workflow suggestions during the call
- Automated form filling
- Relevant knowledge and policy suggestions
- Interaction summaries
- Conversational analytics
- Coaching and compliance monitoring
The case presents these as help for the representative, not as a replacement for the representative. Its effect on handle time and satisfaction is covered in the figures table above.
Screening before a person picks up (CarShield, through Cisco)
Cisco’s September 30, 2025 announcement describes CarShield’s Pre-Call Screening AI Agent, which handles calls before a person is involved. Cisco also describes quality management tools that let supervisors assess and coach both AI and human agents. Its president and chief product officer summarized the pitch like this:
“AI is how you deliver those experiences at scale, and Webex Contact Center brings together AI and human agents to make it easier than ever to deliver consistent quality with speed.” — Jeetu Patel, President and Chief Product Officer, Cisco.
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Architecture decides more than the voice model
The two deployments with the most detail in their case studies name their components. Comparing them shows the design choices a buyer has to make. Where a case does not name a component, the table says so.
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| Layer | Kotsovolos (Microsoft case) | DoorDash (AWS case) |
|---|---|---|
| Core contact-center platform | Dynamics 365 Contact Center | Amazon Connect Customer, through its Connect Customer IVR |
| Telephony | Azure Communication Services | Not stated in the case |
| Conversational routing | Omilia IVR | Generative-AI layer added to the existing IVR |
| Voice gateway | AudioCodes | Not stated in the case |
| Existing investment | Existing IVR retained | Built on the current Connect Customer IVR |
| Knowledge and testing | Not stated in the case | Knowledge base, testing framework, A/B tests, and an evaluation framework |
Both cases kept a telephony and IVR investment that was already running and added a voice AI layer on top of it. Whether that approach fits a given center depends on what it already runs.
Before comparing platforms, settle these design questions:
- Integration: Which telephony, IVR, CRM, and case tools must the agent connect to, and who maintains those connections after launch?
- Approved knowledge: Which sources can the system answer from, and who approves changes to policy content?
- Latency: Is the response fast enough for a live conversation? DoorDash names latency as important, but this article cites no specific latency target from any vendor.
- Evaluation: How will the team test changes before they reach all callers? DoorDash’s A/B testing and evaluation framework is one example of how this can be done.
How Kotsovolos staged its rollout
The Kotsovolos case is one example of a staged introduction. It is not a required schedule, and other organizations will need different timing depending on size and complexity.
- Analysis to production. The case gives seven months from analysis to production, completed in February 2026. TTEC Digital is named as the implementation partner.
- A small first group. A 10-agent group used the initial functionality during the first three weeks.
- A production pilot. A 50-agent group ran the system in production before wider use.
- Gradual onboarding. The remaining users were onboarded in stages until all 400 were using the system.
The value of this sequence is that problems surface while only a few agents depend on the system. The case also quotes the project’s CRM lead on the human side of the change:
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.“Our focus is not to replace people with AI. AI is here to empower our teams to deliver a better customer experience. The future is AI working hand in hand with our teams.” — Iliana Potsika, CRM Project Manager and Technical Lead, Kotsovolos.
Designing the handoff from automation to a person
The cases discuss continuity and unified customer context, but none offers a controlled comparison of handoff quality across vendors. Test this with your own call flows before committing. At minimum, a live agent who takes over from an automated voice agent should receive:
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- The caller’s history from the channels the center already uses, including digital contacts
- The point at which the automated interaction stopped and the reason for transfer
- Any summary the system has already generated, so the caller does not repeat the same details
- Access to the same approved knowledge the automation drew on
The workforce claims in these cases are company perspectives. Kotsovolos, DoorDash, and Pega each describe employees working alongside the technology or taking on more complex work. None of the materials establishes what happens to staffing levels or job roles over a longer period.
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Platforms named in these cases include Dynamics 365 Contact Center, Amazon Connect Customer, Zendesk Contact Center, Cisco Webex Contact Center, and Pega Voice AI. Microsoft’s Kotsovolos story also names TTEC Digital as an implementation partner. Use the following questions to compare any of them:
- Task fit and containment. List the actual requests your callers make. Find out which the system can complete and which it should route to a person, and ask the vendor to define how it counts a contained call.
- Handoff. Ask for a demonstration of the handoff checklist above, using a real call scenario from your center.
- Integration. Confirm which connections are standard and which require custom work.
- Latency and testing. Ask how response speed is measured and how changes are tested against live traffic.
- Agent and supervisor tools. Check for summaries, guidance, knowledge access, analytics, coaching, and quality monitoring. Cisco and Pega both list these.
- Availability. Confirm current geography, languages, telephony options, and feature status with the vendor. Product announcements can describe planned or changing availability, so check a feature’s status before you rely on it.
Limits of the evidence and open questions
The outcome figures come from customer stories and vendor announcements. No independent, cross-vendor benchmark is available here for typical return on investment, resolution quality, or workforce effects. Use each number as a named outcome in its own setting, and do not expect the same result from another organization.
Rules on call recording, disclosure, consent, privacy, and automated calling depend on jurisdiction and on the details of each deployment. The cases in this article do not address them. Before launching an AI voice agent, check the requirements in every market where you take calls with legal counsel and the relevant regulators.
Several of the cited pages are undated or dated only by their announcement. Confirm the current status of any feature or figure with the publisher before you cite it.
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