Verint conversational AI is usually researched by people responsible for service quality: making sure customer conversations meet a standard, every time, and finding out quickly when they do not. This article does not describe Verint's products. It takes that quality job and shows a firm of 10 to 250 people how to run it on the platform: a written standard, a score for each reviewed call and message thread stored on the contact, and a short weekly coaching routine based on the results.
Most firms check quality by accident
In a firm without a quality team, conversations are reviewed only when something goes wrong. A client complains, a manager listens to the call, and a lesson is shared by email. The other ninety-nine conversations that week are never looked at, including the ones where a prospect was lost through a missed question or a slow reply.
Scoring a steady sample of conversations against the same short checklist changes that. You stop relying on complaints to find problems. You see which parts of the standard slip most, which people need help with which step, and whether your AI agents meet the same bar as your staff.
Before you start
- A written conversation standard of five to seven checks, such as "confirmed the reason for contact", "asked about timing", "agreed a next step with a date".
- Calls recorded through platform numbers and message channels flowing into Conversations.
- A custom field or note where scores can be stored on the contact.
- A manager with thirty minutes a week for coaching.
- Agreement with the team on how scores will be used. Present it as coaching, not surveillance.
How to set it up
- Go to Settings > Custom Fields.
- Select the Contact object for the new field.
- Click Create field.
- Open Field type and choose a number type for the score.
- Enter the Field name and choose a Folder name.
- Check the Live preview.
- Click Create custom field.
- Repeat for a multi line field holding the missed checks.
How to use the scores
Use them for three things only. First, coaching: each week, pick the most commonly missed check and discuss two real examples with the team. Second, process fixes: if every person misses the same check, the problem is the process or the script, not the people. Third, agent tuning: if your AI agents score low on a check, update their instructions.
Avoid using scores as a league table. Once people feel ranked, they learn to tick boxes rather than help customers, and the scores lose their meaning. For how AI agents and people should share the work, see AI sales agent.
Say the agency scores 300 calls and threads a month against six checks, and the report shows that "agreed a next step with a date" is missed in a third of first calls. The sales lead could run two coaching sessions with real call extracts and add a booking link to the follow-up email template. The following month's scores would then show whether that check is missed less often, and whether more first meetings turn into proposals.
What it replaces
It replaces random call listening, end-of-month quality spreadsheets and the habit of reviewing only complaints. It does not replace the manager's judgement; it focuses it. A manager who used to listen to three calls a week now reads the three that most need attention.
Running time: about thirty minutes a week for the coaching review, plus a quarterly check that the standard still matches how you sell.
Teams write the standard as qualities, such as "was friendly" or "showed expertise". Two reviewers will score those differently. Write checks that can be answered from the transcript with yes or no. Friendliness shows up in outcomes like a booked next step.
When not to use it
Do not score conversations if you have not told staff it is happening, and do not score them for disciplinary purposes without a proper process. If your volume is very low, a manager reading every thread may be simpler. And for regulated advice conversations, structured scoring supports your compliance checks but does not replace the review your obligations require.
Troubleshooting
Scores swing for similar conversations. The checks are open to interpretation. Rewrite each one with an example of a pass and a fail.
Short calls score badly. A two-minute call to rearrange a meeting cannot meet a sales standard. Agree that only conversations above a minimum length are scored.
The team disputes the scores. Share the reviewer's one-line reason for each missed check, with the moment in the transcript. Most disputes end once people see the specific moment.
Reviews pile up. Scoring by hand does not scale to every thread. Score a fixed weekly sample, plus every call that ended without a next step.
Frequently asked questions
Does Verint conversational AI do something the platform cannot?
Specialist quality and workforce products are built for large contact centres, with forecasting and scheduling across many agents. A firm of this size rarely needs those. Scoring conversations against a standard and coaching from the results is the part that matters, and it runs from the call transcripts, conversation summaries and custom fields already on the platform.
Can I score AI agent conversations with the same standard?
Yes, and it is a good way to compare. If the agent consistently misses one check, add it to the agent's instructions. Read conversational AI bot for how instructions shape agent behaviour.
How many checks should the standard have?
Five to seven. Fewer misses important steps, more makes the report hard to act on. For examples of what good handled conversations look like, see conversational chatbot examples.