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AI voice agents are changing contact centers in two distinct ways: they can handle or route routine calls without a human, and they can help human agents resolve more complicated interactions. The first automates parts of the customer conversation; the second supports the employee with tools such as transcription, summaries, knowledge and workflow guidance. Some deployments combine both approaches, but neither approach by itself guarantees better service or lower costs.
What AI voice agents do in a contact center
Automate routine caller requests
A customer-facing voice agent can answer common questions, guide callers through simple tasks or direct them to the right team. DoorDash describes adding generative AI to its existing interactive voice response (IVR) system for Dasher support, including questions about app troubleshooting, sign-ups and payment options. The intended role is to resolve suitable requests before a live agent is needed and route the rest appropriately. AWS’s DoorDash case study describes the system and its rollout.
Assist human agents during complex calls
Agent-assist tools do not take over the caller conversation. They help an employee while the employee remains responsible for the interaction. In Pega’s U.S. Bank case, the described capabilities include real-time workflow suggestions, form filling, relevant knowledge and policy suggestions, interaction summaries, conversational analytics, coaching and compliance monitoring. This model is aimed at helping employees manage complicated or emotionally difficult cases, rather than replacing them with a voice bot. Pega’s U.S. Bank case describes its Voice AI deployment.
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Bring voice and digital customer history together
Voice tools can also be part of a broader contact-center desktop. Microsoft’s Kotsovolos case describes transcription, summaries and sentiment analysis across voice and digital channels, with customer history surfaced for employees. Zendesk describes a unified desktop that brings voice together with customer context and digital-channel history. The practical aim is to give the employee useful context without making the customer repeat information, though the cited materials do not provide a controlled comparison of how well different systems preserve context during handoff.
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Evaluate and supervise both people and AI
Automation still needs oversight. Cisco describes quality-management tools for supervisors to assess and coach AI as well as human agents. DoorDash reports using an evaluation framework and A/B testing for its system. These examples point to a broader shift: contact centers need to assess automated interactions as part of service quality, not treat deployment as a one-time software installation. Cisco’s September 30, 2025 announcement describes its Webex Contact Center offerings.
What results have contact centers reported?
The figures below come from named customer cases or vendor announcements. They are outcomes reported for those deployments, not independent industry averages or predictions for another contact center.
| Deployment | Reported outcome | What the figure applies to |
|---|---|---|
| Kotsovolos, Microsoft Customer Stories, 2026 | 30% lower average handling time and a six-percentage-point increase in customer satisfaction | Results Microsoft reports after Kotsovolos deployed Dynamics 365 Contact Center. The case says the retailer handled about five million customer interactions a year with about 400 human agents before the reported outcomes. Microsoft’s Kotsovolos case. |
| DoorDash, AWS case study; publication date not stated on the reviewed page | 49% fewer agent transfers, a 12% increase in first-contact resolution and $3 million in year-over-year operational savings | AWS attributes these results to DoorDash’s existing Connect Customer/IVR experience. The case then describes a further generative-AI solution and rollout, so these figures should not be presented as outcomes of that later generative-AI layer. AWS’s DoorDash case study. |
| U.S. Bank, Pega Voice AI; publication date not stated on the reviewed page | Eight seconds lower average handle time and a two-point increase in customer-satisfaction scores | Figures Pega reports for its U.S. Bank case. The case does not establish a directly comparable baseline or measurement method with the other deployments in this table. Pega’s U.S. Bank case. |
| CarShield, Cisco announcement, September 30, 2025 | 66% of calls contained without human intervention | Cisco attributes this result to CarShield’s Pre-Call Screening AI Agent; it is not a general containment rate for contact-center voice agents. Cisco’s announcement. |
The numbers use different deployments and measures—among them handling time, transfers, satisfaction and containment. They cannot be ranked as though they came from a shared test. Zendesk’s June 24, 2026 announcement says 40% of contact-center volume is voice and attributes to Zendesk research the finding that 75% of contact-center leaders say legacy technology prevents true omnichannel service. Those are Zendesk-reported research claims, not measurements of AI-agent performance. Zendesk’s announcement.
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How AI voice agents work with human agents
A useful operating model is to automate tasks that are both common and suitable for self-service, while giving callers an effective route to a person when a request is unusual, complex or not resolved. The handoff should preserve the caller’s history and make clear where the automated interaction stopped. The reviewed cases describe customer context and routing, but do not establish a cross-vendor comparison of handoff quality.
Agent-assist tools address a different need: keeping a person in the conversation while providing guidance, summaries, knowledge or documentation support. That distinction matters when evaluating results. A reduction in transfers from an IVR, for example, is not the same outcome as shorter handling time for an employee receiving real-time assistance.
Company perspectives also need to be kept separate from independently established workforce effects. Kotsovolos CRM Project Manager and Technical Lead Iliana Potsika said, “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.” That is the project lead’s stated aim, not independent evidence that AI will or will not change staffing elsewhere.
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What to assess before deploying a voice agent
1. Task fit and containment
- Identify the specific calls the system should complete, such as the routine support questions in DoorDash’s case.
- Decide which requests should go to a person and what should trigger that route.
- Define how the team will evaluate completed requests, transfers and unsuccessful interactions; a containment figure alone does not describe service quality.
2. Human handoff and context continuity
- Check what information the receiving employee sees, including customer history and the point reached in the automated interaction.
- Test whether the caller must repeat information after transfer. The available cases describe context-sharing capabilities but do not establish that every deployment achieves seamless handoff.
3. Integration with telephony, IVR, customer records and knowledge
The underlying system design can shape what the voice agent can do. Kotsovolos combined Dynamics 365 Contact Center with Azure Communication Services for telephony, Omilia IVR for conversational routing and AudioCodes as a voice gateway, while retaining its existing IVR investment. DoorDash built on its existing Connect Customer IVR, adding a generative-AI layer, a knowledge base and a testing framework. These are examples of different integration choices, not a universal architecture.
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Responses need to arrive quickly enough to work in a live conversation. DoorDash specifically identifies latency as important and describes A/B testing. Ask how the system will be evaluated at scale and how teams will detect cases where answers, routing or timing fall short.
5. Support for agents and supervisors
For agent-assist or hybrid deployments, check whether the system provides the capabilities the team needs: relevant knowledge, workflow guidance, summaries, analytics, coaching and quality monitoring. Those are distinct functions; a voice bot that handles routine requests does not automatically provide them to human agents or supervisors.
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6. Deployment scope and availability
Confirm the current availability of the required features, geographies, languages and telephony options with the vendor. Product announcements can describe offerings whose availability or scope changes, so verify the specific configuration being considered.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What a staged rollout can look like
Kotsovolos offers one documented example, not a required schedule for every contact center. Microsoft says a 10-agent group used the initial functionality during the first three weeks, followed by a 50-agent production pilot and gradual onboarding of all 400 users. The implementation took seven months from analysis to production and was completed in February 2026; TTEC Digital was the implementation partner. Microsoft’s case study attributes this rollout account to the Kotsovolos deployment.
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The sequence illustrates how an organization can gather employee feedback, test a production pilot and expand onboarding in stages. It should not be read as a universal timeline: the case describes one retailer’s implementation and does not establish how long another organization’s project will take.
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What the evidence can—and cannot—show
Customer cases show that organizations have deployed both customer-facing automation and tools that assist employees, sometimes within a broader voice-and-digital contact-center platform. The reported results demonstrate what those organizations or vendors say happened in particular deployments; they do not establish typical return on investment, resolution quality across vendors or the overall effect on contact-center jobs.
For a buyer, the most informative questions are therefore operational: which calls are appropriate to automate, what happens when automation cannot resolve them, how the system connects to existing tools, and how performance will be evaluated. The right design may automate selected interactions, assist agents, or combine the two—without assuming that one pattern suits every contact center.
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