Selecting the best software to improve contact center agent productivity using AI depends on where agents spend avoidable manual effort across live handling, post-call administration, and supervisory escalations. There is no universally superior platform across all contact centers. Operational leaders should evaluate tools based on how effectively they reduce active task duration without causing downstream rework or degrading conversation quality.
How should contact centers evaluate AI tools for agent productivity?
For this proposed evaluation, divide work into live assistance, documentation, and quality follow-up. Identify the task agents repeat, record its time and correction burden, then compare the matching documented tools.
Level AI describes Agent Assist as providing guidance during conversations, access to information, and automatic call summaries. Documentation.
Dialpad AI Call Summary documents searchable speaker-separated transcripts and key moments including action items. Documentation.
Five9 Agent Assist documents guidance cards and action checklists during conversations. Documentation.
Five9 Agent Assist describes live transcription and customizable call summaries. Documentation.
What framework separates actual time savings from downstream rework?
A productivity enhancement in one phase of a call can create hidden labor elsewhere if the AI output requires extensive manual correction. Operations teams should measure productivity using a structured task-level scorecard rather than relying exclusively on aggregate handle times.
- Baseline Task Timing: Measure the exact pre-implementation minutes agents spend searching knowledge bases, writing after-call work (ACW) summaries, and transferring escalations.
- Output Review: Separately measure factual correctness and agent edits; acceptance without editing does not by itself establish accuracy.
- Supervisor Escalation Frequency: Monitor whether in-conversation guidance reduces mid-call manager intervention rates or simply shifts agent questions to post-call review queues.
- Downstream Rework Auditing: Check whether abbreviated handle times correlate with repeat contact rates, investigating missed details as one possible explanation.
What trade-offs accompany AI-assisted agent workflows?
Investigate three risks in the trial: whether prompts distract agents, whether summary corrections consume the expected time saving, and whether supervisors have capacity to review exceptions. Measure these effects instead of assuming that automatic notes eliminate typing or that live guidance reduces search time.
What practical steps should teams follow during vendor selection?
When testing software candidates, follow a methodical evaluation process:
- Audit Workflow Bottlenecks: Identify whether handle time is inflated by live knowledge retrieval delays, complex after-call documentation, or frequent supervisory escalation.
- Validate Platform Connectors: Confirm that candidate tools integrate directly into your primary contact center telephony platform and CRM without requiring multi-screen copying.
- Conduct Role-Based Permission Reviews: Verify that transcript data, coaching notes, and call summaries can be segmented according to internal organizational hierarchies.
- Run Controlled Pilot Groups: Test candidate platforms on a representative queue against a control group to verify actual handle time reduction before broader deployment.
Limits
This guide relies on vendor-published technical documentation and product overviews. It does not reflect independent lab testing, guaranteed time savings, pricing terms, or legal assessments. Documented capabilities vary significantly by subscription tier, language availability, account entitlement, and third-party connector configuration. Contact centers must conduct empirical pilot testing within their specific technical environments to measure actual agent productivity impact before purchase.