How AI can Improve Contact Center Operations

Before the holidays, I spoke with Joel Sylvester, Chief Client Officer, and John Coulter, Vice President of Business Development at Five Star Call Centers, about their journey deploying AI. Five Star Call Centers, a Midwest-based BPO operating in the US and Latin America, uses a mix of client-selected and internally chosen technologies. As a mid-sized player, they’ve embraced technology to stay competitive and were early AI adopters. Joel and John, both industry veterans, generously shared their insights. As with many practitioners, they initially approached AI with caution regarding accuracy, choosing to first deploy it for back-office functions. One of their initial AI use cases was recruitment. Five Star Call Centers recruits 5,000 associates annually, processing over 50,000 resumes. AI manages the top of the funnel by reviewing applications and conducting avatar-based initial interviews, with recruiters remaining in the loop. This approach doubled efficiency, improved seasonal handling, and freed recruiters to focus on subsequent interviews. Beyond efficiency gains, attrition rates dropped by an impressive 40%. Their next step was leveraging AI for role-based training. AI eliminated the need to remove top-performing agents from production for mock calls, chats, and emails and allowed for individualized training programs. The impact was significant: a 20% reduction in training time to reach the same performance levels. Agents also appreciate the flexibility to choose training times, boosting show rates by 20%. Automated Quality Assurance (AQA) was another breakthrough. Previously, only 3% of calls were randomly reviewed and scored. With AI, over 70% of calls are now analyzed automatically, surfacing the important ones to supervisors. This transformation has freed up 50% of their time, enabling more targeted training programs and proactive agent coaching. Finally, they implemented agent assistance, most often leveraging the built-in capabilities of the CCaaS platforms to provide real-time nudges based on customer sentiment and keywords or intents identified during conversations. The impact of agent assistance is harder to quantify—given that simpler interactions are increasingly handled via self-service, leaving the urgent, more complex, and emotional ones to agents. Associates appreciate its unobtrusive design: usage is voluntary, and there’s no penalty for opting out unless performance metrics are unmet. Five Star Call Centers' journey exemplifies the value of a phased approach to AI adoption, addressing one use case at a time while prioritizing buy-in at every stage. It highlights varied metrics required to measure AI's impact. I was particularly impressed by their focus on adoption, ensuring associates were engaged at every step and giving them control over AI. #cx #contactcenter #ai

For decades, we've been playing a risky game in contact centers—reviewing just 2-3% of interactions and hoping it tells the full story. But what if we could analyze EVERY customer conversation? AI-powered quality monitoring is transforming how the best contact centers operate: ✓ 100% interaction coverage across all channels ✓ Real-time insights instead of after-the-fact reviews ✓ Objective evaluation based on consistent criteria ✓ Personalized coaching tailored to each agent The results? Targeted interventions that actually work, predictive performance scoring that focuses on outcomes (not just scripts), and the ability to identify exactly what your top performers do differently. The shift from sample-based to comprehensive QA isn't just an upgrade—it's a complete transformation that turns quality from a compliance function into a strategic driver of customer experience. Are you still gambling with the 3% sample approach, or are you ready to see the complete picture? Share your thoughts below! . . . #ContactCenterExcellence #CustomerExperience #AIinnovation #QualityAssurance

This is pretty cool. Anyone looking for the much-anticipated (but less often observed) gen AI productivity boost should read about our work with Deutsche Telekom, where an AI-powered “personal trainer” helped thousands of employees improve customer experience, resolution rates, and more. Deutsche Telekom is a powerhouse, with millions of customers and thousands of call center and field service agents around Germany. But as they grew, the company found it hard to maintain the high bar they set for their employees, with ripple effects across customer experience, operational efficiency, and employee satisfaction. Traditional learning programs weren’t cutting it—the question was, how to customize learning at scale? Enter gen AI. By teaming up with Deutsche Telekom—from the c-suite to the call centers—our colleagues helped build and launch a capability-building engine that could individually upskill service agents and better meet customer needs. Leveraging our in-house frontline AI product, we used millions of pieces of call data, field service notes, customer feedback and more to identify skill gaps and provide tailored learning interventions. The results so far are clear—a 14 point increase in customer satisfaction and a 10 percent increase in first-time resolution rates year over year, with more I’m sure to come. But the real lesson? The technology is essential, but not enough. It’s the people and organization that will determine success. You can find the whole story here: https://lnkd.in/gYsymvXj Huge congrats to Deutsche Telekom, and thanks to Julian Raabe, Nicolai von Bismarck, and all the colleagues who helped bring this work to life.

Managing burnout is one of the top challenges contact centers face. I’ve seen how high turnover can affect even the most dedicated teams. When agents are stretched thin, customer experience suffers and the burden spreads across the organization. That’s just one reason why I find the potential of voice-first agentic AI so compelling. It offers a powerful way to shift the dynamic by supporting agents in real time and reducing the manual overhead that fuels stress and fatigue. • Before a call: AI reviews the customer’s history, detects intent from recent interactions, and summarizes key points so the agent can prepare with confidence. • During the call: AI analyzes the conversation live, prompting the agent with suggestions, answering common questions automatically, and flagging risks before they escalate. • After the call: AI generates an accurate summary, identifies unresolved issues, and suggests targeted coaching moments, without the agent taking manual notes or filling out reports. When agents feel supported from start to finish, everything changes: satisfaction improves, performance rises, and burnout becomes far less common. My colleague Antonio Nucci, PhD explores this topic in a new blog post. I highly recommend it for CX leaders committed to building healthier, higher-performing teams: https://lnkd.in/g-yXRpUv

Learnings from transforming CX with Gen AI for a Financial Services giant in APAC 🚀 One of the largest Financial Services players in the APAC recently leveraged Verloop to transform its contact center. The outcomes? Transformational change in customer support experience which not only drove CSAT up but also helped them bring efficiency into their CX Ops. Here is a snapshot of outcomes and learnings Outcomes -------------- 1. About 30% increase in Customer Satisfaction score 2. 43% fewer tickets assigned to their support desk 3. 70% Reduction in Average Response Time 4. 30% Cost Savings by CX efficiency Learnings -------------- 1. Effort - Easier said than done; most models are great for building demos but a nightmare when implementing large complex scenarios 2. Focus - Niche-trained LLMs work better than a large model 3. Latency - Latency in response especially in audio calls is a deal breaker. 4. RAG + LLM - Balancing when to refer to RAG vs when should LLM handle the task takes a while 5. Cost - Models cost significant amount of money to run; attach and focus on business outcomes 6. Data Quality - Investing time in data cleansing and organization pays off massively 7. AI + Human - AI handles the repetitive tasks, while AI-assisted human agents are required for empathy and complex problem-solving 8. Keep Building - Continuous improvements and training of flows is critical more so in the first few months of launch Implementing Guardrails --------------------------- 1. Focus on Ethical AI usage with strict guidelines to ensure AI operates within ethical boundaries, maintaining transparency and customer trust. 2. Adhere to rigorous data privacy regulations to protect customer information. Protecto works like a charm! 3. A key trait of any such implementation is AI knowing when to hand over Launch Experience -------------------- 1. Collaborative Approach - Everyone is learning in this journey; engage early and frequently with all stakeholders 2. Stay Agile - Launch iteratively and keep improving instead of one big bang launch 3. Human training - Focus on training all stakeholders; things are different vs structured data We started Verloop with the idea that the future of contact centers is AI-first, human-assisted. These engagements help us stay on the course and keep building towards our vision. We are already living in the future and it is slowly spreading everywhere! 🌟 #contactcenter #GenAI #CXTransformation #transformation Verloop.io CA. Ankit Sarawagi Melisa Vaz Nikhil Gupta Urvashi Singh Kiran Prabhu Ravi Petlur Kumar Gaurav