How to evaluate an AI call-center project

Define pilot eligibility

Choose one task, reference volume and evaluation period. Separate public information, personal records and write operations. Configure a prompt agent for this scope; do not assume it replaces your entire contact-center operation.

Require integration evidence

For every action, record its system, owner, permission and success evidence. A balance lookup and request creation need different proof. Without a validated integration, describe the outcome as a request received for human review.

Test the complete experience

Use the same matrix across options: missing information, noise, interruption, out-of-scope request and tool error. Listen, review the run and check the destination. Completed execution does not prove the business task was resolved.

Calculate effort and continuity

Include consumption, telephony, source maintenance, integrations and human time. Record remaining staff work and repeat contacts. Compare periods with similar tasks and preserve each metric’s denominator.

Define stopping conditions

Assign who can pause the pilot and how to restore responsible service. Unauthorized disclosure, unsupported confirmation or duplicate writes require investigation. Preserve the case, correct its cause and repeat the matrix before expansion.

What evidence should you request before approving a pilot?

Request the tested configuration, task scope, dependencies and an observable outcome. For retrieval, compare returns with authorized records; for creation, find the new destination record; for transfer, verify receipt on a supported telephone channel. Mark functions not demonstrated as awaiting validation.

A worksheet can contain four columns: request, expected evidence, dependency owner and observed outcome. Include integration failures, missing references and requests for a human. Also compare maintenance effort: who updates documents, credentials, rules and destinations after the pilot?

Turn the need into an evaluation brief

Evaluation starts with service that needs improvement. List requests, observed volume, channels and expected outcomes. Saying a company needs AI does not identify whether the problem involves waiting, missing information, manual queries or incorrect routing. A clear operational need makes demonstrations easier to assess.

Choose verifiable tasks. In a fictional example, reporting authorized order progress is more precise than “answer customer questions.” This task requires identifying records, querying sources and communicating permitted states; each stage provides evaluation criteria. Define what callers should know or obtain by the end instead of relying on how polished the opening sounds.

Before the demonstration, check what service needs to work: current information, connections to business systems and a team that receives pending requests. An agent may understand a request without being able to complete it. That limit should be clear in the proposal and when comparing solutions.

Define exclusions and approval conditions. Projects may start with general information before supporting record modifications. Avoid approving entire operations because a straightforward component produced convincing dialogue. Record task-specific evidence so reviewers can explain which capabilities passed, which failed and which remain untested. This prevents enthusiasm about one successful example from expanding commitments beyond demonstrated behavior.

Ask for action evidence as well as spoken responses

Demonstrations should show how conversation connects with outcomes. When agents claim to create requests, inspect corresponding records. When reporting status, compare source returns. Speech proves communication, not external execution. Separate caller-facing correctness from system state so both can be evaluated independently.

Distinguish native functions, project configuration and external integrations. Presentations may combine these without explaining who maintains them. Identify owners and behavior when each dependency fails. Ask which capabilities are already available in the tested setup and which would require additional implementation before launch.

In a fictional case, demonstrations query prepared orders. Ask what happens when references are absent or access is refused. Responses should preserve those states instead of using the same success narrative for every return. Include cases where results are partial or delayed because these expose whether wording follows evidence or expectations.

Record what was actually tested. Illustrative recordings can demonstrate pacing and voice, but do not establish integration availability in your project. Approval should use known configuration and observable operations within intended scope. Keep prepared examples as introductions, then require representative checks before treating claims as operational commitments. If a function cannot be demonstrated, record it as unverified rather than assigning success based on a verbal explanation of how it could work.

Include failures changing task outcomes

Build cases with unknown references, denied authorization, empty results and unavailable services. Add date, number or intent corrections during conversations. These tests show whether systems preserve limits when requests leave ideal paths. Include both informational and action tasks because failure consequences differ.

Classify severity before comparison. Long sentences reduce clarity; changes to wrong records may cause rework and inappropriate disclosure. Average ratings should not hide critical failures among many easy examples. Define approval rules preventing unacceptable outcomes from being offset by attractive voice or strong performance on unrelated tasks.

In a fictional example, customers correct codes before queries. Inspect submitted codes, returned records and spoken information. Verbal acknowledgment does not establish that operations used current values. Where timing matters, record whether corrections preceded submission or arrived while earlier queries were still pending.

Establish expected exits when completion is impossible. Clarification, referral or pending records may be appropriate according to existing processes. Test alternatives too, avoiding approval of conversations merely promising unavailable next steps. A pending request needs ownership and discovery; a transfer needs compatible configuration and reachable destinations. Review what callers understand after failures because accurate technical status can still be communicated in a way that implies unsupported resolution.

Validate channels and receiving staff processes

Editor tests support instruction and response review but do not establish complete telephone operation. Validate number association, audio and actions on the final channel. For SIP-dependent projects, follow documented configuration and test intended origins. Separate behavior quality from delivery because failures require different owners and corrections.

For transfer, confirm destination and compatibility. Direct transfer in Tigy does not automatically deliver context. If service depends on staff summaries, responsible integrations must exist and demonstrate delivery separately. Do not assume receiving staff know earlier conversation details simply because calls connect successfully.

Ask staff to find pilot-created requests and continue the process. This reveals incomplete records, unowned notifications or information difficult to locate. Technical receipt does not establish operational use. Verify that records contain current corrected details and that staff understand what was completed versus what still needs action.

Include periods when staff are unavailable. Guidance should reflect existing service without promising immediate assistance or unaccepted callback deadlines. Call-center evaluation follows caller journeys until truthful next steps. If collection is the approved out-of-hours option, inspect records and ownership rather than treating courteous closing language as proof of continuity. A realistic pilot needs successful cases and limits showing how the operation behaves under ordinary constraints.

Compare costs using the same outcome definition

List consumption, telephony, external components, implementation, maintenance and staff effort. Use equivalent units and matching periods. Isolated per-minute prices may omit dependencies or compare services with different scopes. Obtain applicable cost information from the project's actual arrangements rather than copying unverified figures from unrelated examples.

Define outcomes before calculating cost per task. Transfers may be useful while remaining intermediate stages. Recorded requests may still await service. Denominators should match the business outcome being evaluated. Use separate measures where informational completion and human referral serve different purposes instead of combining them under one ambiguous success label.

In a fictional case, pilots receive one hundred contacts but only some queries resolve without intervention. Do not count every contact as a completed task. Include effort repairing errors and handling pending work where it belongs to operation. Record cases requiring repeat calls because their apparent initial success can conceal additional cost.

Separate estimates from observed results. Small pilots do not prove savings across all service. Explain conditions and limits, then monitor whether they hold when volume, request mix or staff availability changes. If outcomes change, revisit assumptions rather than applying the original ratio automatically. Compare quality alongside cost so lower expenditure does not hide incorrect answers, inaccessible alternatives or increased work moved to callers.

Approve a process that continues working after changes

Complete evaluations consider who maintains solutions. Assign owners for documents, instructions, tools and telephony. Dependencies can change without automatically updating the others. Approval should establish a workable operating process, not merely a configuration understood by one person who prepared the demonstration.

Keep approved cases and representative failures. Repeat them after changes affecting tasks. If APIs change fields or policies acquire new validity dates, review outcomes and guidance rather than only checking that settings remain saved. Preserve configuration identifiers so evidence can be tied to the version actually tested.

Define how to detect problems, narrow scope and restore service through available processes. Written plans are not automatic product capabilities. Staff should know how to execute necessary operational measures. Establish escalation ownership and what evidence helps investigate without exposing unrelated sensitive information.

Expand when quality, continuity and maintenance are demonstrated. Appropriate solutions meet chosen needs with evidence and clear responsibilities. Long feature lists do not replace that proof, and convincing conversations do not finish evaluation. Revisit approved scope when request types or operating conditions change. New capabilities should retain checks for established tasks so expansion does not degrade the reliable service that justified the original pilot. Keep untested claims visibly separate from demonstrated outcomes until relevant checks establish that they work in the intended project.