May 4, 2026 • 14 min read
17 Contact Center Quality Assurance Best Practices for 2026
Director of Content & Market Research
May 4, 2026
Contact center quality assurance (QA) has always had a visibility problem.
Traditional programs relied on just 1-2% of interactions, meaning most coaching decisions are based on a tiny, often misleading sample.
With quality analysts and coaches unable to see the full picture, key knowledge, process, and behavioral issues often went undetected.
Meanwhile, agents would complain about the initiative’s fairness. “You just happened to choose the one call I did that!”
Seasoned analysts understand the frustration.
Automated quality assurance (QA) promised to change the game, ensuring 100% coverage and enabling a massive increase in data supply.
However, data overload then became an issue. Teams gained visibility, but not clarity, and dashboards expanded while insights didn’t.
The next generation of AI promises to change that, auto-identifying trends in the performance of agents and coaches and auto-generating improvement recommendations.
Yet, operational maturity needs to catch up with technological advancement.
“Vendors may offer powerful capabilities, but operations teams often just want to replicate their existing QA processes at scale. They’re not always asking for, or using, the additional analysis available.”
Against this backdrop, here are the latest best practices that contact centers can follow to transform their quality assurance program and embrace the next chapter of QA.
1. Think of Contact Center QA Through Three Lenses
Contact center QA allows service leaders to:
- Ensure compliance
- Enable performance improvement
- Bolster business intelligence
Compliance is the simple part: did the agent follow regulations and critical business policies?
Performance improvement involves identifying what agents do well and development opportunities. The latter involves pinpointing problem areas, which could relate to behaviors, coaching gaps, or process ambiguity.
Yet, business intelligence is often forgotten. This involves sharing QA insights across the broader organization, so everyone can action customer experience insights.
Contact centers frequently give up on this due to technical fragmentation and broader business leaders not seeing QA as relevant to their needs.
However, in 2026, the most advanced service operations are utilizing QA insights to inspire change across the enterprise.
2. Adjust Your Approach to QA Scorecards
Traditionally, QA scorecards have focused on checkbox compliance rather than real performance, rewarding fake empathy and turning agents into script-reading robots.
In his podcast, Advice from a Contact Center Geek, Thomas Laird advocates for an approach that gives agents the flexibility to think and adapt.
While doing so, he recommends contact centers start with a scorecard that groups criteria based on connection (30%), resolution (30%), and compliance (40%).
On connection and resolution, Laird advocates for moving away from rigid, surface-level questions and honing in on specific outcomes.
For instance instead of asking:
- Did the agent use the customer’s name multiple times?
- Did the agent follow the troubleshooting script in order?
Ask questions such as:
- Did the agent adapt to the customer’s tone?
- Did they demonstrate understanding of the issue?
- Did the agent identify the root cause of the issue?
- Did they provide a meaningful solution within policy?
Standard practice is to aim for 9-12 total questions, as anything beyond that can become overly complex and harder to manage.
Yet, over time, contact centers can learn what drives customer satisfaction, retention, and revenue across specific contact reasons and channels, adapting their scorecards accordingly.
Some even align their scorecards with an agent’s tenure to ensure quality remains relevant and engaging for senior team members.
3. Distinguish Between Binary and Scaled Scoring Criteria
Many contact center QA programs fail to distinguish between binary criteria (yes/no) and scaled evaluation criteria (e.g., Likert scale).
Even worse, some Auto-QA tooling only supports pass/fail measurement.
“This is a serious problem. I’ve seen cases, like wrongful termination disputes, where flawed QA measurement played a role. This isn’t just a process issue; it has real consequences.”
As such, while compliance criteria may be binary, contact centers should consider scaled criteria elsewhere on the scorecard.
4. Add Unscored Elements to the Scorecard to Track Trends Beyond Agent Performance
Auto-QA solutions leverage powerful conversational intelligence models, which can analyze much more than agent performance.
Contact centers can leverage this capability by adding unscored sections to their scorecards.
For example, if the technology enables open responses, contact centers can add questions such as:
- Why was the customer calling?
- Why were they cancelling?
- Did the customers mention a competitor? If so, what did they say?
As a result, they can collate a wealth of insight to share with operations, marketing, sales, and product teams, which could transform customer experiences beyond the customer-agent conversation.
“We’ve worked with organizations where QA insights have improved coordination between customer service teams and field technicians… Something as simple as ensuring the correct customer contact number is captured during the call can significantly improve the field experience. That, in turn, improves overall customer satisfaction.”
5. Become the “Rossetta Stone” for the Business
Automated contact center QA programs collate a wealth of customer experience data that can benefit the broader business.
However, organizational politics, integration complexity, and misalignment prevent service teams from democratizing that insight.
To challenge this dynamic, service leaders need to act as a “Rosetta Stone” for the business, according to Robbins. That involves translating CX insights into language that resonates at the executive and board level.
“Too often, CX teams speak in their own metrics, and executives disengage because they don’t see the connection to business outcomes.”
In other words, it’s not enough to present data; leaders have to translate it into what matters for marketing, sales, product, and the broader business.
6. Think of Contact Center QA Beyond the Scorecard
The contact center scorecard can be an excellent tool to monitor and improve agent performance, while adding business intelligence. However, it’s just a tool.
Quality assurance should be measured from multiple perspectives:
- QA evaluations
- Customer feedback (surveys, reviews, forums)
- Business indicators (upsell, retention, referrals)
By adding this context, contact centers not only gauge what has happened, but also why and the downstream impact.
Ultimately, this enables analysts to prioritize interventions more effectively, leading to smarter, better-informed decisions. For instance, adjusting policies instead of over-coaching agents or redefining metrics when they conflict with customer outcomes.
“My advice: Don’t rely on a single measurement method. Use at least two or three perspectives to understand what’s really happening.”
7. Focus on the “So What”?
By analyzing all contact center interactions and pulling in additional context, contact centers have more data than ever before. But, the key question is: so what?
QA leaders need to have a set process for translating that insight into actions, prioritizing interventions.
“If I went back to running operations today, I’d assume I already have the data. My focus would be coaching, microlearning, simulations, and knowledge management.”
“Then, I’d prioritize actions, with one initiative for employees, one for customers, and one for the business,” concluded Robbins. “That’s how you turn insight into results.”
8. Leverage AI Simulations to Action QA Data
Contact centers increasingly use AI-driven customer simulations to turn QA insights into action, with vendors like AmplifAI and Centrical now offering this technology.
How does it work? Well, if an agent scores poorly on a call, the contact center can trigger an AI simulation to recreate that interaction.
The interaction will feature the same customer persona and problem in a safe, repeatable environment.
As such, the agent can get the negative contact out of their system and feels more comfortable next time a similar issue arises.
9. Track the Impact of Coaching Interventions
While few contact centers run simulations, all run coaching sessions. These are often informed, both at an individual and team level, by QA data.
Yet, after the coach has applied the training, they mustn’t just assume it has sunk in. Instead, QA should track its impact.
If the issue stops occurring or performance improves, excellent. If not, investigate why. Perhaps there’s a better way to engage that agent or a different coaching activity that could transform their performance.
Over time, coaches can understand what resonates with particular agents and maximize the value of performance interventions.
“Microlearning and frequent coaching interventions are excellent. But the key question is: are they changing behavior?”
10. Monitor for Fraud Detection and Reputational Risk Control
Every customer interaction should be treated as a potential fraud exposure point.
Auto-QA enables contact centers to establish a continuous fraud prevention system that monitors every interaction for risk.
Such a fraud prevention system may ask:
- Did the agent follow the required identity verification procedures?
- Did the agent properly authenticate the customer before discussing or changing account details?
- Did the agent recognize and escalate suspicious behavior, aligning with defined fraud protocols?
Some of this may already be covered within a scorecard’s existing compliance criteria. Nevertheless, by incorporating such scorecard criteria, contact centers can monitor every interaction for risk.
11. Ensure Agents Are Engaged In the QA Process
When QA programs overemphasize minor behaviors (e.g., exact phrasing and script adherence), focus more on mistakes than strengths, and lack a clear connection to career growth, agents disengage.
A QA program that supports engagement does a few key things:
- It makes feedback evidence-based: Ground every coaching conversation in real data and patterns, not isolated interactions, so agents see feedback as fair and credible.
- It links behaviors to outcomes: Clearly isolate how specific actions impact metrics, such as customer satisfaction, first contact resolution, or conversations.
- It recognizes what’s working: Don’t just coach gaps, highlight and reinforce strong behaviors to build confidence and consistency.
- It personalizes coaching: Tailor feedback and development plans to each agent’s strengths and opportunities instead of using a one-size-fits-all approach.
As automation handles simpler tasks, agents are increasingly dealing with more complex, emotionally charged interactions. A QA program that follows these best practices will help support agents through complexity, building their confidence and competence.
12. Run QA Activities to Spotlight Issues Beyond the Agent’s Control
To reiterate a key point: quality assurance ensures compliance, improves performance, and enhances business intelligence.
However, don’t think of performance just at the agent level; also consider it at the process level.
To do so, set aside the scorecard and utilize the underlying conversational intelligence engine of an Auto-QA platform to consider:
- Are specific issues consistently leading to long hold times?
- When do agents say, “Bear with me for a moment”?
- What’s driving high transfer rates across specific contact reasons?
“These signals can reveal knowledge base gaps and process issues. Instead of just coaching agents, you can fix the underlying issue, whether that’s updating documentation, improving systems, or refining workflows.”
13. Tie QA and Knowledge Management Together
If a quality monitoring initiative reveals that many agents share the same misunderstanding of a policy, it should prompt a review of the knowledge base.
Whether the knowledge is inaccurate or missing, contact centers can confirm the correct policy, utilize AI to help generate new content, and update the knowledge system. That helps to keep it dynamic and relevant.
Yet, some contact centers are going further by integrating their QA solution with the knowledge base and detecting whether the information agents give customers aligns with actual policy.
Software companies, including evaluagent and Level AI, have touted this type of ‘policy-aware’ QA, allowing contact centers to check whether agents are actually following procedures, not just answering confidently.
14. Link QA and Workforce Management More Closely
Workforce management (WFM) teams can utilize QA data to understand which agents perform best across specific queues and channels, leveraging that information to inform their schedules. That can improve both efficiency and agent morale.
“With the latest AI solutions, QA teams can also break down customer contacts by intent. That data is valuable to WFM teams who can compare predicted vs. actual contact drivers and improve future planning.”
Additionally, with access to real-time QA and intent data, planners can gauge what is driving demand and make smarter intraday management decisions.
In the future, AI may even be able to leverage QA data and auto-adjust agent schedules, so - for example - an agent has a scheduled ten-minute learning intervention after a particularly tricky contact to reset and engage in a simulation, putting that negative interaction behind them.
15. Use QA to Inform Your Agent Assist Strategy
Agent-assist solutions guide contact center reps as they resolve customer queries, sharing knowledge and recommending next-best actions.
However, these recommendations aren’t always useful. Often, the agent already knows what they need to do, and the pop-ups prove less of a help and more of a hindrance.
“AI can make work more intense rather than easier,” added Calvert. “This creates a mismatch: leadership celebrates the technology, but frontline teams are dealing with unintended consequences.”
A 2025 study published by Cornell University found agent-assist to be delivering such unintended consequences, adding new “earning, compliance, and psychological burdens” to the contact center agent role.
Quality assurance initiatives can drive more targeted agent-assist deployments that don’t place such burdens on the contact center team.
Such initiatives investigate where agents often slip up when resolving particular queries, enabling AI assistance in the moments where it’s needed most, not at every point along the resolution journey.
16. Validate AI-Generated QA Data
“We’ve found that up to 20% of AI scoring can be incorrect, and that, generally speaking, analysts place too much trust in AI initially,” said Vander Well.
Much of this challenge boils down to transcription accuracy. Yet, Vander Well also notes that AI can be inconsistent in the answers that it gives.
Many contact center QA vendors are addressing this. For example, Zendesk has an AI Trust Score, which tracks the correlation between AI and manual scoring criteria to ensure reliability.
However, other contact centers are solving the consistency issue by having AI complete the same form multiple times, comparing the results, probing discrepancies, ensuring AI can explain which answer is correct, and systematically refining its logic.
Over time, this reduces randomness in AI responses, forces internal consistency, and enables contact centers to scale Auto-QA more confidently.
17. Don’t Boil the Ocean with AI Right Away
Organizations often get overexcited about Auto-QA, immediately applying automation across every queue and channel, while looking at every possible metric.
Yet, few contact centers can absorb that level of change at once.
As such, leaders may choose to start by focusing on their most important problems, prioritizing those based on impact rather than convenience or novelty, and monitoring accordingly. Possible examples include:
- Compliance-heavy workflows (e.g., disclosures, collections, disputes)
- Fraud-sensitive interactions (authentication, account changes)
- Regulated processes with strict timelines (e.g., credit bureau disputes)
- High-volume or high-cost contact reasons
By taking this approach, the business benefit is immediately measurable. It also allows teams to build trust in AI-driven QA outputs before scaling the deployment.
The Future of Contact Center Quality Assurance
Most contact centers already have call recordings, transcripts, QA scoring systems, and CRM and workflow data.
As such, the difficulty for many is not a lack of information; it’s that insights are often not operationalized fast enough or consistently enough.
The latest contact center QA solutions are helping to flip the script.
Indeed, many tech providers are creating a cyclical QA and learning process where:
- AI detects an issue in an interaction (e.g., missing disclosure, incorrect dispute handling)
- The system flags it immediately
- That insight is pushed to a supervisor or agent
- A corrective action is triggered (e.g., coaching, microlearning, AI simulation)
- The system tracks whether the issue was corrected in future interactions
However, many contact center QA teams aren’t built to deliver on this connected strategy, often using AI to automate their old, existing processes.
The best practices outlined above should help contact centers to embrace this new vision for the future of quality assurance.
To learn more about the future of QA technology, unpack CX Foundation’s rundown of 11 Contact Center Quality Management Software Providers & Their Differentiators in 2026.