Contact centers are on the front lines of AI adoption. Salesforce research found that AI agent implementation in customer service organizations jumped from 39% to 66% between 2025 and 2026. Also, 77% of service teams with AI agents now deploy them across both customer-facing and internal operations.
In some contact centers, there are more AI agents than humans running the phone lines and answering emails. Yet many organizations still measure quality as if humans were doing all the work.
Contact centers already struggle to use the data they collect: 96% record calls but 73% don’t have time to analyze them. With agents deflecting and handling more volume, the traditional quality assurance (QA) methods are becoming even less practical.
QA can no longer exist primarily to grade human representatives after interactions end.
QA is Not Just for Grading Human Agents
Maintaining quality in customer service has been, and in many organizations still is, an incredibly manual process.
- A manager or QA analyst pulls a sample from a recorded call, works through a scorecard, drafts a brief evaluation of the representative, and files the feedback away for the next coaching session or 1:1.
- Those insights support the manager’s relationship with the team member, guiding their career pathing and professional development goals.
- Those improvements, ideally, trickle down to customer experience (CX).
Not only is a manual process like this incredibly time-consuming from an operational standpoint, but it also only makes sense when the human representative is the one driving the customer interaction.
Today, a customer is more likely to interact with some form of automation or virtual agent before they ever reach human support. They may even get passed from AI to human, back to AI, before reaching a true resolution.
Consequently, monitoring quality on only one part of that conversation, between the customer and a human rep, leaves you with an incomplete picture of what happened.
Fully automated QA requires a new level of customization and segmentation…
Perhaps not surprisingly, the majority (65%) surveyed said that although their QA technology was effective at evaluating their agents’ performance and supporting better training, those insights rarely translated into broader CX improvements.
Moreover, and tellingly, only 12% of respondents said that their QA insights reach the rest of the organization.
Held back by poor integration and a lack of automation, QA insights can remain trapped in the contact center, with no automated way to surface broader patterns and put them in front of leadership or the teams who can act on them.
What the New QA Should Look Like
In the modern contact center, QA should be fully automated, accounting for the entire interactions across every channel and acting as a continuous loop to improve the entire CX that:
- Evaluates both human and AI interactions.
- Identifies what worked.
- Feeds those lessons back into real-time coaching and the virtual agents themselves.
Here is what this new automated set of QA processes should have, how it should be used, and what it can enable.
1. Out with the old, in with fully automated QA, not just the pieces
Automating your QA system doesn’t just swap out the human reviewer with an AI-generated scorecard. It should fundamentally change what gets evaluated, the speed of insights, and what happens to those insights once an interaction ends.
2. Ability to evaluate every interaction
Automated QA analyzes interactions across every channel, from voice and email to SMS and web chat, providing service leaders with a far more robust view of their service quality.
Rather than clipping samples from calls at random, management gains a rich view of what’s happening across the entire CX to catch blind spots or patterns and gather more data to work from.
3. Real-time agent improvement
QA doesn’t work if it doesn’t help your human agents improve in real time; it should never wait until the interaction ends to act.
[From our research, across industry and media sites] QA feedback helps agents improve, even though most conversations are never reviewed. That helpful feedback then lands on only a minority of interactions.
Automated QA makes it easier to quickly address gaps and errors in real time. You don’t have to wait weeks to discuss a clunky call with a human agent or step out of a meeting to correct a team member.
Real-time scoring and AI-powered agent assist identify what your human agents need while they’re still on a call or as they’re answering a chat, surfacing relevant information or guidance in the moment. The team member gets help right when they need it. The customer gets a better experience.
4. Identify what needs to improve
QA insights shouldn’t live in a report or end with a coaching conversation. Automated QA goes beyond human-agent scoring to address and improve the entire agentic system.
Automated analysis can surface recurring customer issues, knowledge gaps, ineffective workflows, and places where an AI agent isn’t handling a request as intended.
Those patterns give leaders a clearer picture of where the broader operation needs attention, allowing you to fix handoff moments and workflows or provide targeted coaching before the customer feels a blip.
5. Customized, integrated scorecards
Of course, handing QA over to AI doesn’t mean contact centers can simply set it and forget it. Organizations still have to decide what they’re looking for, how quality should be measured, and what happens when the system finds a problem.
After all, AI can’t evaluate an interaction well if the organization hasn’t established what a successful outcome looks like.
Fully automated QA requires a new level of customization and segmentation because quality doesn’t mean the same thing for every interaction. A customer calling to resolve a billing issue may require a different scorecard from a prospect calling to make a purchase decision.
Automated QA should therefore evaluate the entire journey…
Organizations should be able to define scorecards around their specific use cases, while AI evaluates interactions against those criteria. And those scorecards need to apply to both human and AI interactions.
But you have to evaluate AI differently from humans. A human agent can be evaluated on whether they provided accurate information, followed the right process, and resolved the customer’s issue. AI needs to be held to the same standards, but the workflows themselves need to be evaluated more closely.
So, put in the processes that ask whether the AI:
- Used the right tool?
- Took the right action?
- Recognized when it couldn’t solve the problem?
- Escalated at the right time?
- Preserved the customer’s context?
A natural-sounding conversation isn’t enough if the AI fails to take these steps and actions.
6. Entire customer journey evaluation
Automated QA must also keep up with increasing complexity. Contact centers are moving beyond simple bots toward agentic and multi-agent experiences, where specialized AI agents may handle different parts of a customer’s request.
For the customer, however, the technology behind the interaction matters less than whether each handoff brings them closer to a resolution. According to our Closure Index, 85% of consumers say the ability to move seamlessly from AI to a human when needed is important to achieving true resolution.
Automated QA should therefore evaluate the entire journey, including whether the right agent handled each task, whether context was preserved, and whether a handoff happened at the right moment.
As systems become more autonomous, evaluating how those pieces work together becomes more important than assessing individual responses in isolation.
Shaping the Quality of Customer Experiences
QA used to be the last step in the interaction: review what happened, assign a score, and move on. In an AI-powered contact center, it can become part of what happens next.
Instead of simply documenting performance, fully automated QA turns every interaction into an opportunity to improve your CX. The result is a QA process that doesn’t just measure quality. It shapes it.
This article appears in November 2026.