Voice AI can lower contact-center costs and improve service when it reliably completes routine tasks or helps customers reach the right person faster. The goal is not maximum automation or a high containment rate: it is a lower cost per resolved issue while customers still get accurate answers, quick access to human help, and no need to repeat themselves.
Results depend on the work automated, the systems the AI can access, and how success is measured. Public examples report meaningful gains, but they come from different industries and deployments, so they are evidence of what may be possible—not a forecast for your contact center.
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Measure the cost of resolving an issue, not just handling a call
“Cost per call” can mean the cost of one interaction, whether or not the customer’s problem was solved. That measure can fall if a call is diverted or ended quickly, even when the customer calls back or switches channels to get an answer.
For operating decisions, track the fully loaded cost per resolved contact: the relevant contact-center operating costs divided by the number of customer issues resolved. Define what counts as a resolution and which costs are included. Depending on the deployment, costs may include agent time, voice AI, integrations, maintenance, and the work required to monitor and correct the system.
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Keep interaction-level cost as a useful efficiency measure, but evaluate it alongside repeat contacts, first-contact resolution, customer satisfaction, and access to an agent. A lower interaction cost is not a win if the same unresolved issue returns and consumes more service capacity.
Which contact-center work should voice AI handle first?
Start with bounded, frequent intents
Look for common requests with a clear answer or a limited set of permitted actions. Examples include routine account questions and status checks. Use call reasons and, where available, transcript analysis to identify the intents that create repeatable work. Prioritize candidates that have enough volume to matter and a reliable way to confirm whether the task is complete.
Do not automate an intent merely because callers ask about it often. If resolution depends on judgment, sensitive circumstances, exceptions, or information the system cannot verify, keep a human involved or limit the AI to gathering context and routing the contact.
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Give the system the information and permissions the task requires
A voice interface cannot complete an account action by conversation alone. It needs access to current, approved information and, for actions such as checking a status, a permissioned connection to the relevant backend system. Keep knowledge content maintained and make the AI’s allowed actions explicit; responses based on stale information or access broader than the task requires can erode trust and create operational risk.
An AWS-published ResultsCX case describes a self-service solution using an AI-powered knowledge base and API connections for near-real-time answers. Its design directed routine inquiries toward self-service while leaving more complex interactions to agents. The practical lesson is that task completion depends on the information and actions behind the voice experience, not just its conversational fluency.
What reported deployments show—and what they do not
The outcomes below come from distinct deployments and studies with different definitions, contexts, and methods. Treat them as examples, not directly comparable benchmarks or proof that voice AI alone caused every change.
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| Source and context | Reported result | How to interpret it |
|---|---|---|
| McKinsey Global Institute, 2025; an unnamed utility’s customer operations | AI agents handled roughly 40% of calls and resolved more than 80% of the calls they handled without human involvement. The case reported about 50% lower average cost per call and a six-percentage-point increase in customer satisfaction. | One utility example; the company is not identified in the public description. The figures are not an industry average. |
| AWS-published ResultsCX case; a mobile virtual network operator (MVNO) | The case reported 45% containment, a 20% reduction in annual operating expenses, a 79.7-second average handling time, and 37% lower overall call volume over six months. | The AWS page’s publication date is not displayed. The reported figures describe this deployment, which used QnABot on AWS and ResultsCX’s SupportPredict suite. |
| Cognizant case; a US bank | The case reported 77.35% containment, customer satisfaction rising from 57.5% to 66%, 200 full-time-equivalent employees saved, and $9 million in savings. The page states the bank handled 10 million interactions monthly. | The page’s publication date is not displayed. The implementation included Amazon Connect, AWS Lex, routing, integrations, and a phased cloud contact-center modernization; the results should not be attributed to voice AI alone. |
| Cognizant case; a wealth-management virtual assistant | The case reported $6.7 million in lower operating costs, 166,000 fewer calls, and a 5% increase in its customer-experience index. The assistant automated more than 400 common inquiries and offered live-agent transfer or callback. | The page’s publication date is not displayed, and it does not provide enough detail to independently calculate cost per call. |
| Forrester Consulting study commissioned by Five9, 2025 | Five9 summarized a modeled three-year composite outcome of up to 28% cost containment and 120 seconds saved for each contact reaching a live agent. | The composite was based on interviews with six decision-makers. It is a modeled study outcome, not a universal measured result. |
The cases use different denominators and service models. For example, containment describes contacts handled without an agent under a particular definition; it does not by itself show that a customer’s issue was resolved. Ask how each source counted containment, what costs were included, which period was measured, and whether repeat contacts and satisfaction were tracked over the same period.
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Use a balanced scorecard, not containment alone
Containment can be misleading if customers have difficulty asking for an agent or if a contact is counted as contained before the underlying issue is resolved. Omilia has specifically cautioned that containment may be inflated when an IVR prevents callers from reaching an agent. Pair it with outcome and access measures, and define each metric before launch.
- Cost per resolved contact: Use a consistent definition of resolution and include the costs relevant to the deployment.
- Containment: State exactly which contacts qualify, such as those completed without an agent, and whether successful resolution is required.
- First-contact resolution or repeat-contact rate: Check whether customers need to return about the same issue.
- Average handle time: Track this for contacts reaching agents, while checking that shorter calls do not come at the expense of resolution.
- Customer satisfaction: Separate AI-handled contacts from escalations so that a blended score does not hide a poor experience in either group.
- Transfers and abandonment: Monitor how often callers are handed off, how long the handoff takes, and how often customers leave before receiving help.
- Requests or need for a person: Track how often callers ask for an agent and whether the system recognizes situations that warrant human assistance.
Baseline these measures before deployment, then review them by intent and customer segment. A program-wide average can conceal that one routine task works well while another creates repeat calls or disproportionately poor service for a particular group.
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Make escalation part of the voice experience
Human handoff is not a failure to automate. It is a necessary path for unresolved, sensitive, complex, or explicitly requested help. Make it easy for a caller to reach a person and avoid sending them back through the same questions they have already answered.
When a contact transfers, pass along the verified account details, the caller’s intent, relevant actions already taken, and the conversation history. McKinsey’s utility example describes verified account details and interaction history moving with the escalation. That context lets the agent continue the work rather than restart it.
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Roll out in stages and compare designs fairly
- Document the baseline. Record volume, resolution, repeat contacts, costs, satisfaction, handling time, transfers, and abandonment for the intents under consideration.
- Select a small set of intents. Begin with frequent, bounded requests and define what a successful resolution looks like for each.
- Connect approved information and actions. Validate the knowledge source, backend permissions, and the system’s response when information is missing or an action fails.
- Test the full customer journey. Include successful completion, edge cases, requests for an agent, failed integrations, and transfers with context preserved.
- Expand only when the evidence supports it. Review the scorecard by intent and customer segment, investigate repeat contacts or service declines, and adjust or pause an intent that is not resolving issues reliably.
Cognizant describes a bank modernization that moved smaller entities first and used agile releases to reduce transition risk. That is one implementation example, not a required rollout formula; the appropriate stages depend on the contact center’s systems, risk, and traffic.
Keep agents in the service model as automation expands. Train them for escalations and use any agent-assistance tools only when they make resolution easier rather than adding another step to the interaction.
Evaluate options against the same customer and cost outcomes
Compare the existing IVR and agent workflow with at least two plausible alternatives: voice AI that completes selected tasks end to end, and AI that primarily routes contacts or supports agents. The latter can be a better fit when a task requires human judgment but customers still benefit from faster routing or better-prepared agents.
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- Can it access the current knowledge base and perform necessary backend actions with appropriate permissions?
- How quickly can a caller reach a person, and what account, intent, and conversation context follows the transfer?
- Are containment and repeat-contact outcomes defined and measured consistently?
- How are satisfaction, accessibility, and the experience of customers who need or request a person evaluated?
- What are the deployment, integration, maintenance, governance, and operating costs?
Compare options using the same baseline, intent definitions, resolution criteria, and measurement period. The cited cases illustrate possible outcomes and design considerations; they are not an independent head-to-head evaluation of providers, and their results do not establish a portable return-on-investment benchmark.
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