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Voice AI can reduce contact-center costs and improve service when it reliably completes routine tasks or helps customers reach the right person faster. The key measure is not how many calls the system contains, but whether the customer’s issue is resolved at lower total cost while service outcomes hold or improve.
Measure the cost to resolve an issue, not just the cost of a call
A lower cost per interaction does not necessarily mean a lower cost to serve. If a caller is deflected but has to call again, the first interaction may look inexpensive while the underlying issue costs more to resolve.
Use cost per resolved contact as the economic anchor: total relevant service costs divided by the contacts that result in a resolved issue. Define which operating costs belong in the calculation and what counts as resolved before comparing the AI system with the existing service model. Keep that definition consistent across channels and reporting periods.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallContainment is useful, but it is not proof of success. It can be inflated when a caller cannot readily reach an agent. Omilia cautions against treating containment as a stand-alone measure; pair it with repeat contacts or first-contact resolution, customer satisfaction, transfer and abandonment rates, and the share of callers who request or need a person.
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Start with routine tasks the system can actually finish
Look for high-volume, bounded intents: requests with a recognizable goal, a manageable set of steps, and a reliable way to confirm completion. Status checks and routine account inquiries are examples cited in contact-center implementations. Analyze call reasons and transcripts to identify where customers repeatedly need help, then prioritize tasks that can be completed without guesswork.
Answering a question is not the same as resolving it. For a useful self-service experience, the voice system needs current, approved information and access to the backend actions needed to complete the request. In an AWS-published ResultsCX case, an AI-powered knowledge base and API connections supported near-real-time answers and guided routine inquiries to self-service. The case describes a system using QnABot on AWS and ResultsCX’s SupportPredict suite.
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Do not automate an intent merely because it is frequent. Check that the system can identify the customer’s goal, obtain the information it needs with appropriate permissions, complete the task, and recognize when it has failed or the request is outside its scope. Uncertain or sensitive cases should move to a person rather than loop through repeated prompts.
What reported results show—and what they do not
Published results illustrate what particular deployments have reported, not what another contact center should expect. The cases below differ in sector, measurement, scope, and reporting method; their percentages are not a like-for-like benchmark.
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| Source and deployment | Reported result | How to read it |
|---|---|---|
| McKinsey Global Institute, 2025: an unnamed utility’s customer operations | AI agents handled roughly 40% of calls and resolved more than 80% of that handled volume without human involvement. Average cost per call fell by about 50%, while customer satisfaction rose by six percentage points. | A single utility example. The public description does not name the company. It also describes AI-supported escalation with verified account details and interaction history. |
| AWS-published ResultsCX case: an MVNO; publication date not displayed on the accessed page | The case reports 45% containment, a 20% reduction in annual operating expenses, a 79.7-second average handling time, and a 37% reduction in overall call volume over six months. | The solution used QnABot on AWS and ResultsCX’s SupportPredict suite. The page gives the per-self-service-interaction cost as “0.31 cents”; that wording is ambiguous, so it should not be read as a dollar amount. |
| Cognizant case: a US bank; publication date not displayed on the accessed page | The bank’s implementation is reported to have achieved 77.35% containment, raised CSAT from 57.5% to 66%, saved 200 FTE, and delivered $9 million in savings. The page states that the bank handled 10 million interactions monthly. | The work included cloud contact-center modernization, routing, integrations, and phased rollout using Amazon Connect and AWS Lex. The figures describe that implementation, not voice AI in isolation. |
| Cognizant wealth-management case; publication date not displayed on the accessed page | The case reports $6.7 million lower operating costs, 166,000 fewer calls, and a five-percent increase in its customer-experience index. | The virtual assistant automated more than 400 common inquiries and offered live-agent transfer or callback. The page does not provide enough detail to calculate cost per call independently. |
| Forrester Consulting study commissioned by Five9 and summarized in 2025 | The study reports up to 28% cost containment and 120 seconds saved per contact reaching a live agent. | This is a three-year modeled composite based on interviews with six decision-makers, not a universal measured result. |
These figures cannot be combined into a single expected savings rate. Ask what each source counted as a call, a resolution, containment, and cost; check the time period and whether repeat contacts were included. Where a broader modernization accompanied the AI, avoid attributing every reported gain to the voice system alone.
Track cost and customer outcomes together
Baseline performance before launch, then review results by intent and customer segment. A practical measurement panel includes:
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- Cost per resolved contact: the cost of getting the underlying issue resolved, not only the first interaction.
- Containment: a documented definition, such as a contact completed by automation without an agent, paired with evidence that the issue stayed resolved.
- First-contact resolution or repeat-contact rate: whether customers need to return for the same issue.
- Agent-call handling time: average handle time for calls that reach a person, interpreted alongside call complexity and resolution quality.
- Customer satisfaction: measured separately for AI-handled contacts and escalated contacts so a strong result in one group does not hide a poor experience in the other.
- Transfer and abandonment rates: whether customers can get to a person and whether they leave before their issue is handled.
- Human-request and human-needed rates: how often callers ask for an agent and how often the system must escalate to complete or safely handle the request.
Use consistent survey methods and measurement windows where possible. A change in satisfaction is difficult to interpret if the survey, the population, or the period changed at the same time as the system.
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Customers should have a clear route to a person when they ask for one, when the system cannot resolve the request, or when the issue needs human judgment. Automation that blocks access can make a containment figure look better while worsening the service experience.
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Transfer enough context to avoid making the caller start over: verified account details where appropriate, the recognized intent, and the conversation history. McKinsey’s utility example describes verified account details and interaction history accompanying escalations. The goal is not simply to pass a call; it is to let the agent continue the work already done.
Keep agents central to the service model. Voice automation can take routine work off their queue, leaving people to handle complex, sensitive, or otherwise unsuitable interactions. Prepare agents for these escalations and give them useful context rather than adding a second system or extra steps that slow resolution.
Roll out in stages and compare the right options
Begin with a limited set of intents and a smaller deployment group, then expand as performance and failure modes become clear. Cognizant describes a phased rollout, moving smaller entities first and using agile releases to reduce transition risk. At each stage, review customer feedback, resolution, repeat contacts, handoffs, and operating cost against the baseline.
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When evaluating a design, compare the existing IVR/contact-center baseline with plausible uses of voice AI: end-to-end self-service for bounded tasks, AI-assisted routing, or agent support. The most useful comparison is not a feature checklist alone; examine whether each design can resolve the intents in scope and whether its total service cost and customer outcomes are measurable.
| Evaluation area | Questions to ask |
|---|---|
| Resolved-intent coverage | Which customer tasks can the system complete end to end, and how will completion be verified? |
| Knowledge and backend access | Does it use current approved information and permissioned connections to the systems required for the task? |
| Handoff | Can a caller reach a person promptly, and does the agent receive useful context? |
| Measurement | Are containment, resolution, repeat contacts, satisfaction, transfer, abandonment, and cost defined consistently? |
| Customer access and experience | Can customers who need a person or cannot use the automated flow still get help? |
| Operating requirements | What integration, governance, rollout, and ongoing support work is needed, and is it included in the total-cost calculation? |
The published cases provide examples, not an independent head-to-head evaluation of vendors or designs. There is no portable ROI percentage in these results that can substitute for measuring the contact center’s own baseline and pilot outcomes.
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