Best Practices for AI-Driven QA in Support
AI-driven QA is transforming support teams by automating quality assurance and analyzing 100% of interactions across all channels. Traditional manual reviews cover only 2–5% of conversations, leaving gaps in oversight. AI tools address this by offering real-time insights, consistent scoring, and actionable data to improve customer satisfaction (CSAT), agent performance, and operational efficiency.
Key Takeaways:
Full Coverage: AI evaluates every interaction (calls, chats, emails, etc.), providing unbiased and consistent scoring.
Faster Insights: AI scales review processes up to 50x faster than manual methods, identifying issues instantly.
Enhanced Coaching: Predictive analytics highlight trends and training opportunities, boosting agent efficiency by 25–30%.
Workflow Optimization: Automates ticket tagging, routing, and triaging, saving time and improving resolution speed.
Proven Impact: Companies using AI-driven QA report reduced handle times, increased CSAT, and significant cost savings.
Why It Matters:
Support teams can now spot issues, coach agents, and improve customer experiences in real-time without adding staff. By using metrics like Internal Quality Score (IQS), First Contact Resolution (FCR), and sentiment analysis, AI ensures measurable improvements in service quality and operational ROI.
Bottom Line: AI-driven QA is no longer optional - it’s a must-have for scaling support while maintaining high standards. Start by defining clear metrics, integrating AI tools, and continuously refining models for long-term success.
AI-Driven QA vs Manual QA: Key Performance Metrics and Benefits
Best Practices for AI-Driven QA
Implementing AI-driven QA requires a well-thought-out framework that improves service quality across all customer interactions. Here’s how to ensure your AI-driven QA delivers meaningful results and operates smoothly.
Set Clear and Measurable QA Metrics
Before rolling out AI, it’s important to define what “quality” means for your team. Start by identifying key metrics like Internal Quality Score (IQS), Customer Satisfaction (CSAT), and Net Promoter Score (NPS). Pair these with operational metrics such as First Contact Resolution (FCR), Average Handle Time (AHT), First Response Time (FRT), and Customer Effort Score (CES). Use simple scorecards with three to five categories - like Solution, Tone, Grammar, Empathy, and Process Adherence - and set clear benchmarks, such as maintaining an IQS above 90% or achieving an FRT under two hours. Regular calibration sessions help align the team on scoring standards. These metrics ensure targeted improvements in both agent performance and customer satisfaction.
Automate Interaction Scoring with AI
AI-powered auto QA can evaluate 100% of customer interactions in real time, offering unbiased and consistent insights. Modern AI tools assess conversations using both binary and scaled criteria, ensuring detailed and objective scoring. For instance, 88% of customer support teams now score interactions on whether a solution was provided. By adopting a hybrid approach - where AI handles routine scoring and flags complex or sensitive cases for human review - teams can boost their reviewing capacity by up to 50 times. Agents also benefit from instant feedback, which accelerates their improvement.
"We focus on accurately capturing what is present - what we know how to catch. We do not penalize agents or reduce their scores for something our system may not have accurately captured."
Dr. Mervi Sepp Rei, Director, Machine Learning, Zendesk
Use Sentiment Analysis for Real-Time Insights
Sentiment analysis enables teams to gauge customer emotions - like frustration, satisfaction, or confusion - during live interactions. This real-time insight allows for proactive responses, rather than waiting for delayed manual reviews. Configuring AI to flag interactions with negative sentiment or high churn risk ensures that critical cases get immediate human attention, helping managers step in before issues escalate. This approach directly enhances customer retention and satisfaction.
Apply Predictive Analytics for Coaching and Training
Predictive analytics takes coaching to the next level by identifying performance trends early. AI can review every interaction to uncover where agents may need support - whether it’s with following procedures, understanding issues, or improving soft skills like empathy. Predictive scores can highlight “at-risk” interactions, while classifiers assess areas like comprehension, sentiment, and resolution effectiveness. To ensure accuracy, validate predictive scores through human calibration. Teams that integrate AI insights with operational data have seen agent efficiency improve by 25–30%, with coaching accuracy surpassing 90%, compared to 70–80% with traditional manual reviews.
"Predictive CSAT doesn't replace surveys or QA reviews, it enhances them... it fills in the blind spots, making experience measurable at scale."
Automate Ticket Tagging, Routing, and Triaging
AI doesn’t just enhance scoring and coaching - it also optimizes support workflows. Tasks like manual ticket categorization can eat up time that agents could spend solving customer problems. AI automation simplifies this by instantly tagging, prioritizing, and routing tickets based on factors like issue type, sentiment, and customer history. For example, IrisAgent’s automated ticket tagging and routing ensures tickets are assigned to agents with the right expertise, freeing up time to focus on resolving issues. This integration highlights how AI can streamline processes and elevate support standards.
Using AI-Powered Agent Assistance
AI-powered tools are reshaping the way support teams operate by offering real-time assistance during customer interactions. Instead of flipping through multiple screens or hunting for information, agents get instant, context-specific suggestions, curated articles, and automated responses right within their workflow. These tools seamlessly integrate with broader QA strategies, boosting the quality of customer support.
Streamline Workflows with AI Assistance
Modern AI tools can instantly analyze customer sentiment and intent, helping agents tailor their communication accordingly. For instance, if a customer's message reflects frustration, the AI flags it, prompting the agent to adjust their tone and prioritize the interaction. This real-time guidance complements efforts to maintain consistent and unbiased scoring. IrisAgent’s GPT-based assistance takes it a step further by embedding CRM widgets that recommend past tickets, related articles, and even product bugs. This eliminates the need for constant context switching, enabling agents to resolve issues faster without leaving their workspace.
Key features like ticket summarization allow agents to quickly understand the context during escalations or ticket transfers without wading through lengthy comment threads. Tools for tone adjustment guide agents to match their responses to the customer's emotional state - offering empathy to a frustrated customer or staying professional in more complex scenarios. Meanwhile, suggested macros analyze ticket content and recommend the most appropriate standard responses, speeding up replies to common questions.
Boost Productivity with No-Code AI Solutions
No-code AI tools take productivity to the next level, enabling agents to deliver fast and accurate responses without requiring technical expertise. These platforms allow teams to deploy AI tools quickly, avoiding the lengthy setups associated with traditional systems. With IrisAgent’s no-code approach, teams can implement AI-driven solutions almost immediately, achieving quick returns on investment. Instead of designing complicated scripted workflows, teams simply input business policies in plain language, and the AI determines the best resolution path.
To ensure a smooth rollout of no-code AI, it’s essential to assemble a team that includes a Project Lead, experienced agents, and a CRM Admin. Writing clear, step-by-step instructions for the AI - such as "Ask for the account number" - helps ensure consistent and accurate interpretation. Breaking down complex tasks into manageable steps further enhances the AI's performance, ensuring reliable results across all customer interactions. This approach not only optimizes agent assistance but also contributes to scaling QA efforts and improving overall support efficiency.
Scaling QA Across Multiple Support Channels
Support teams today handle a wide variety of customer interactions - email, chat, social media, and phone calls - all within a single journey. A customer might start with a Twitter message, switch to live chat, and finish with an email follow-up. Without the help of AI, ensuring consistent quality across these multiple touchpoints can feel like an impossible task. Manual reviews often miss the majority of conversations, especially when dealing with high volumes or automated interactions. AI-driven QA flips the script by evaluating every single interaction across all channels, leaving no conversation unchecked. This fragmented landscape calls for a unified, comprehensive approach to quality assurance.
Maintain Consistency Across Channels
AI tools bring consistency by applying the same scoring criteria to all channels - whether it’s a chat, email, or social media response. Standardized scorecards assess key elements like resolution, tone, empathy, and language proficiency, using fixed rubrics to eliminate subjectivity.
"AI-powered quality assurance (QA) uses AI to automate the process of reviewing customer interactions for resolution completeness, communication, language proficiency, and more." - Christelle Agustin, Content Writer, Gorgias
To keep things consistent, develop multi-channel scorecards with universal questions like “Did the agent address the main issue?” or “Was the tone aligned with the brand?”. For voice interactions, automated speech recognition (ASR) can transcribe calls into text, enabling AI to apply the same standards for empathy and accuracy as it does for written communication. For example, a fintech company using Zendesk QA configured their AI to flag privacy concerns and signs of vulnerability in real time across chats and emails, ensuring agents adhered to strict disclosure rules.
Monitor Channel Performance in Real Time
Once consistent scoring is in place, real-time monitoring takes QA to the next level by shifting from after-the-fact audits to immediate oversight. AI tools analyze customer sentiment and intent as conversations happen, helping support teams quickly identify channel-specific issues or broader process gaps. Instead of uncovering recurring complaints weeks later, teams can spot patterns in hours and adjust their strategies before customer satisfaction takes a hit.
A health tech company introduced a "Member Experience Score", powered by AI, to evaluate every interaction across all channels. They also launched a "Close the Loop" initiative, where AI-flagged high-risk interactions were escalated for human follow-up. This approach allowed them to recover negative experiences before they impacted CSAT scores. Real-time intervention is especially critical during busy periods - 71% of CX leaders now rely on AI and automation to manage spikes in activity, like the holiday shopping season. By tracking escalation triggers and bot-to-human handoff rates, teams can quickly identify and resolve friction points.
Use IrisAgent to centralize cross-channel QA insights and address issues proactively by prioritizing tickets based on sentiment and business impact.
Measuring ROI and Optimizing AI Performance
When it comes to implementing AI-driven QA, proving ROI is non-negotiable. Without clear metrics and baselines, 95% of AI investments fail to deliver measurable returns. The issue isn’t that AI lacks potential - it’s that teams often struggle to measure its impact effectively. To avoid falling into this trap, focus on tracking the right KPIs, regularly refining your AI models, and translating operational improvements into financial gains.
Track Key Performance Indicators
To assess AI's performance, monitor crucial metrics like First-Contact Resolution (FCR), Average Handle Time (AHT), and customer satisfaction (CSAT). Among these, FCR stands out as a key indicator - customers value having their issues resolved over simply receiving a quick response. Additionally, track sentiment delta, which measures how customer mood shifts during interactions. This helps determine whether your AI is easing frustrations or inadvertently escalating them. Other critical metrics include containment rates (how often AI resolves issues independently) and escalation rates (how often cases require human intervention).
Consider real-world examples: In 2024, Klarna’s AI assistant managed 2.3 million conversations without human involvement, slashing resolution times from 11 minutes to just 2 minutes - an 80% reduction - while maintaining high CSAT scores. Similarly, Virgin Money’s AI assistant “Redi” achieved a 94% CSAT score by combining AI efficiency with continuous human oversight. These successes highlight the importance of tracking meaningful KPIs and using data-driven insights to refine AI performance.
Update AI Models Regularly
AI systems aren’t static - they require ongoing updates to stay relevant. Customer needs evolve, products change, and new challenges arise. If your AI isn’t continuously learning and adapting, it risks providing outdated or irrelevant responses. To keep your AI sharp, incorporate real-world cases into its training and use human oversight to catch subtleties the system might miss. Performance dashboards can help you monitor trends, identify escalation patterns, and ensure your AI stays effective.
Regular reviews are essential. Schedule evaluations every 30, 60, and 90 days to assess whether your AI is improving or losing accuracy, as AI systems tend to drift faster than traditional software. Take Calendly’s approach: In 2025, they used Loris.ai to analyze conversations and refine their QA process. This led to a three-minute reduction in AHT and a 23% drop in cost per case. Consistent updates like these ensure your AI remains aligned with real-world scenarios and delivers measurable results.
Calculate ROI from AI-Driven QA
Once you’ve fine-tuned your KPIs and updated your AI models, it’s time to quantify the financial impact. Start by establishing baselines 8 to 12 weeks before rolling out AI. Record metrics like time, cost, volume, and error rates for human-handled interactions. After implementation, compare these figures to assess improvements. AI-driven QA can evaluate 100% of conversations with over 90% accuracy, outperforming manual scoring’s 70% to 80% accuracy and cutting QA costs by over 50%.
To calculate savings, use formulas like (hours saved × hourly rate) or (incident reduction × cost per incident). For example, if AI saves your team 500 hours per month and your agents earn $25 an hour, that’s $12,500 in monthly savings - or $150,000 annually. Another metric to track is cost per successful outcome, which divides total token costs by the number of completed goals. AI-driven QA can boost agent efficiency by 25% to 30% and improve customer satisfaction by 5% to 10%. However, remember that saved time only matters if it’s reinvested to drive additional business value.
"You can't manage what you don't measure. Key performance indicators, or KPIs, are the bedrock of both business and technology success." - Hussain Chinoy, Gen AI Technical Solutions Manager, Google Cloud
Tools like IrisAgent’s predictive analytics can help you monitor KPIs in real time, making it easier to calculate ROI with precision and confidence.
Conclusion
AI-driven QA has become a game-changer for support teams aiming to stay competitive. It offers complete interaction coverage, unmatched precision, and reduced costs - all critical factors for modern customer support success. These advancements are reshaping the way support teams function.
But it’s not just about faster evaluations. AI-driven QA goes a step further by identifying opportunities for real-time coaching, flagging at-risk customers, and uncovering hidden product feedback. These benefits are evident in practical applications.
Take Dropbox, for instance. When they implemented IrisAgent AI in early 2025, the results spoke volumes. They saved an impressive 160,000 minutes, cut Average Handle Time by 2 minutes, and maintained top-notch quality standards.
"Our focus is clear: empower our support agents to do their best work and ensure our customers get the help they need - quickly, accurately, and at scale."
Maria McSweeney, Head of Global Support, Dropbox
To get the most out of AI, treat it as a partner that complements human expertise. Tailor your scorecards to align with your brand values, keep your models updated based on ongoing performance, and measure ROI with clear benchmarks and KPIs. Combining AI with human oversight is the secret to delivering scalable, high-quality support.
At this point, the question isn’t whether to adopt AI-driven QA - it’s how soon you can implement it effectively. Set clear goals, focus on the right metrics, and let data steer your improvements. Start integrating AI-driven QA now and take your support operations to the next level.
FAQs
How does AI-driven quality assurance enhance agent performance and customer satisfaction?
AI-powered quality assurance (QA) is changing the game for agent performance by analyzing every customer interaction - whether it’s an email, chat, or call - in real time. Unlike traditional manual reviews that might miss critical insights, AI evaluates 100% of interactions. It can tag sentiment, flag compliance issues, and score conversations automatically. This allows supervisors to quickly pinpoint areas where agents need improvement and recognize behaviors that drive success. On top of that, agents benefit from instant coaching tips, like suggested phrases or next steps, helping them make adjustments during conversations and provide more efficient, tailored support.Platforms like IrisAgent take AI-driven QA to the next level. With features like real-time monitoring, automated ticket tagging, and predictive analytics, the tool delivers actionable insights that help agents work smarter. They can resolve issues quicker, strike the right tone, and even address potential customer concerns before they escalate. By turning QA into a continuous improvement process, AI helps boost first-contact resolution rates and ensures consistently better customer experiences.
What metrics should you track to ensure successful AI-driven quality assurance in customer support?
To gauge how well AI-driven quality assurance is working in customer support, it's crucial to keep an eye on a few essential metrics. These include first-contact resolution (the percentage of issues resolved in a single interaction), customer satisfaction score (CSAT), escalation rate (how often issues need to be passed to higher-level support), and average handling time (the time it takes to resolve a ticket).You might also want to track the task completion rate and cost per contact to evaluate efficiency and cost-effectiveness. Paying attention to these metrics helps pinpoint areas that need improvement and ensures your AI tools are making a positive impact for both your team and your customers.
How does sentiment analysis improve real-time customer support?
Sentiment analysis gives support teams a window into a customer’s emotions - whether they’re frustrated, confused, or satisfied - by examining the tone in messages, emails, or call transcripts. With this knowledge, agents can focus on urgent or negative interactions, assign issues to the right team members, and adapt their communication style to calm tense situations and deliver a more tailored experience.IrisAgent incorporates sentiment analysis into its AI-powered tools, offering automated ticket tagging, triaging, and predictive analytics. By assigning a sentiment score to every interaction, it helps agents and supervisors track customer well-being in real time, address problems before they escalate, and enhance resolution outcomes. The result? Quicker responses, happier customers, and lower churn rates.
