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Most contact center agents hate customer surveys. And for good reason. A customer is furious about a company policy, a delayed shipment, or a broken process. The agent does everything right. The customer leaves a terrible survey. And somehow, the agent gets punished. We've spent decades measuring customer satisfaction while making the people serving those customers miserable. But what if surveys could actually help agents instead of being used against them? This week, Amas Tenumah and Bob Furniss welcome Scott Thomas from Proponicy to challenge everything we think we know about customer feedback, quality assurance, and the role of AI in the contact center. Scott makes a surprising claim: At many of his company's client organizations, agents actually love the feedback they're receiving. Naturally, we had questions. In this episode: Why agents hate surveys: How companies routinely punish employees for problems they have no authority to fix. The simplest survey fix nobody uses: Separating satisfaction with the company from satisfaction with the human who helped you. What happens when customers know an agent will read their feedback: Why asking customers to help a specific person improve can change the conversation. The QA contradiction: What happens when quality assurance says an agent failed but the customer says the agent was fantastic? AI evaluating 100% of calls: Could comprehensive, evidence-based evaluations be fairer than judging someone on a handful of randomly selected interactions? Coaching instead of punishment: Turning customer feedback into recognition, practical coaching, and continuous improvement. The end of after-call work? How AI-generated notes can free agents to focus on customers instead of typing while they talk. AI that helps humans instead of replacing them: A different vision for automation in an industry obsessed with reducing headcount. The bigger question: We've built entire systems to measure agents, monitor agents, score agents, and correct agents. But how much of that effort actually makes them better at helping customers? Perhaps the problem was never that customers weren't giving us enough feedback. Perhaps it was what we chose to do with it. About our guest Scott Thomas is with Proponicy, a company focused on customer feedback, agent performance, quality monitoring, and AI-powered contact center improvement. Website: https://proponicy.com Listen to The Contact Center Show with Amas Tenumah and Bob Furniss for an unscripted conversation about what needs to change in customer service. #CustomerService #ContactCenter #CustomerExperience #CX #EmployeeExperience #AI #QualityAssurance

summary In this episode, Amas Tenumah and Bob Furniss explore the rapidly evolving world of AI, chatbots, and their implications for contact centers. They discuss real-world experiments with AI agents, the future of customer service, and how businesses can prepare for a world where bots may replace human agents. key topics AI and chatbots in contact centers Real-world AI experiments with Muse Implications of AI replacing human agents Customer experience in a bot-driven world Business strategies for AI adoption

Email was supposed to make customer service cheaper. It didn’t. It was supposed to make it more efficient. It didn’t. Instead, we built a channel where customers write essays explaining their problems, wait hours or days for a response, answer a follow-up question, wait again, and somehow call this progress. Maybe it’s time to kill customer service email. This week on The Contact Center Show, Bob and I get into that argument, plus: Salesforce getting very serious about the contact center. Why digital workforce management may finally be moving beyond forcing phone-era math onto email and chat. Why the back office might be a better place to spend your next technology dollar than another chatbot. And why the contact center technology market has become one giant game of Hungry Hungry Hippos where every vendor suddenly claims to do everything. Also, naturally, we end by discussing whether AI is going to kill us all. A normal episode. #CustomerService #ContactCenter #CX #AI #Dreamforce

This episode cuts through the gap between what executives are saying about AI and what is actually happening inside contact centers. Companies are reporting major efficiency gains, yet contact volume and contact-center employment haven’t collapsed. Amas and Bob unpack the contradiction: pent-up customer demand, harder calls reaching humans, rising handle times, and ROI stories that are far more complicated than “AI saved us 30%.” They also tackle the growing anxiety workers—particularly younger workers—have about AI taking their jobs. Their argument isn’t that AI won’t transform the industry. It will. The advantage increasingly belongs to people who understand what the technology can do, what it cannot do, and where human judgment still matters. Then the conversation turns to Dreamforce and Salesforce’s AI future. If the interface increasingly becomes an AI assistant sitting in front of Salesforce, does Salesforce risk moving from the center of the user experience to infrastructure running quietly in the background? Bob adds an important Dreamforce reality check: what gets announced on stage can still be nine to twelve months away from becoming something customers can actually use. In this episode: AI efficiency versus actual headcount; why better service can create more demand rather than less; the hidden customer demand traditional contact-center metrics miss; what leaders should tell employees worried about AI; why judgment may become more valuable as automation improves; Salesforce, Claude and the rise of “headless” enterprise software; and why you shouldn’t confuse a Dreamforce announcement with a deployable product. The central question: If AI really is making the contact center dramatically more efficient, why aren’t there dramatically fewer humans working in it?

AI was supposed to replace contact center workers. Instead, there are more people working in contact centers today than there were at the beginning of the year. That does not mean AI failed. In many areas, it has exceeded expectations. In this season finale, Amas and Bob examine what they got right, what changed their minds, and what contact center leaders should expect next. They discuss why AI is changing frontline work rather than eliminating it, how automation is exposing leadership failures, and why the agents who remain will handle increasingly difficult work. They also explore where AI is already delivering—particularly in quality assurance, knowledge management, and customer self-service—and why even impressive bots still fail when companies deny them the information or authority required to solve real problems. The next phase will not be about acquiring more technology. It will be about managing what companies have already bought, determining where humans still matter, and separating genuine guidance from vendors selling both the product and the consulting required to make it work. Plus, Amas and Bob revisit their position on customer surveys, predict the issues that will dominate the rest of the year, and reflect on The Contact Center Show’s rise to number 22 among Apple’s management podcasts. In this episode: Why AI has changed contact center jobs without eliminating them What Bob learned from two very different AI service experiences Why better automation creates harder work for human agents How weak bots expose leadership, data, and authority problems Where AI has exceeded expectations Why customer surveys may still have value when used to develop agents The growing importance of AI management and governance Why technology companies are becoming consulting companies The danger of buying more features than an organization can use What The Contact Center Show should tackle next season The Contact Center Show will return at the end of September. Until then, explore the archive and send Amas or Bob your feedback on LinkedIn.

In this episode, Amas Tenumah and Bob Furniss explore the evolving landscape of customer service, tiered service models, and the role of technology in delivering personalized experiences. They discuss how companies can strategically implement paid and differentiated service levels to enhance customer satisfaction and operational efficiency. takeaways Companies should consider tiered service models to meet diverse customer needs. Charging for premium support can recoup costs and improve service quality. Operational execution of tiered support requires careful planning and training. Technology enables effective routing and personalized service levels. Customer expectations vary based on loyalty and payment for support. Chapters 00:00 Introduction and casual chat about sports 4222:10:50 Pivot to service: Should companies charge for attended support? 6972:11:09 Examples of tiered service in airlines and hospitality 9388:51:23 Consumer perspective on paying for better service 11916:38:11 Operational challenges of implementing tiered support 15472:11:43 Technology solutions for differentiated customer routing 215277:45:28 Historical context of tiered service in business 227499:58:49 Recommendations for adopting tiered service models 279166:39:06 Operational considerations and workforce planning 353611:06:28 Ensuring customer satisfaction and avoiding queue degradation 375277:46:37 Closing remarks and World Cup discussion

Customer surveys were once the backbone of quality programs. Today, response rates are collapsing, customers are ignoring them, and contact centers are left wondering how to measure performance without reliable feedback. Fresh from Customer Contact Week in Las Vegas, Amas shares a conversation that kept coming up among industry leaders: transactional surveys are failing. Some organizations have seen response rates drop nearly 90%, creating real problems for coaching, quality scoring, bonuses, and operational decision-making. Bob and Amas explore: Why surveys became the default measure of customer experience.Whether survey fatigue has finally reached a breaking point.Why AI-powered quality monitoring may be replacing traditional Voice of the Customer programs.The hidden flaw in most customer surveys.How changing who the feedback is for—from the company to the individual agent—can dramatically improve participation.The conversation also tackles a bigger question: if customers are giving you their time and feedback, what responsibility do companies have to actually use it? If your contact center still depends on post-interaction surveys, this episode is a timely look at what comes next.

The more important question might be: why are we still struggling with the same training problems we've had for twenty years? In this episode, we tackle a challenge facing nearly every contact center: new hire training that produces high attrition, slow ramp times, and inconsistent performance. The conversation started with a client losing roughly half of its new hires during training and another portion during nesting. Despite new technology, the outcomes felt painfully familiar. We explored some uncomfortable questions: Are organizations hiring the right people for the job? Do recruiters actually understand the roles they're filling? Are training programs teaching agents what to know instead of how to find answers? Why are contact centers still trying to memorize information that changes every few weeks? How much time do leaders spend observing their own training programs? Bob argues that many training failures begin before day one, with hiring processes that prioritize filling seats instead of finding the right fit. We also discuss one of the biggest missed opportunities in modern training: AI is changing how agents work, but many training organizations haven't changed how they train. As AI-powered knowledge systems, agent assist tools, and automation become standard, training leaders need a seat at the table. Yet in many organizations, training teams are disconnected from the technology decisions that will fundamentally reshape agent performance. One of the biggest insights from the discussion: The real opportunity isn't using AI to automate training. It's using AI to automate the things training used to spend time on so trainers can focus on the skills that matter most: Building rapport Problem solving Judgment De-escalation Relationship-building Handling difficult conversations Technology changes. The fundamentals don't. Topics discussed: Why new hire attrition remains so high Hiring mistakes that create training failures Teaching agents how to find answers versus memorizing answers The role of nesting and floor support Why training content quickly becomes obsolete The disconnect between training teams and AI initiatives Agent assist and the future of onboarding Tough skills versus technical skills Why fundamentals still matter in modern contact centers How AI should reshape training priorities

Bob just returned from Italy with a story that should make every customer service leader pay attention. At train stations in Venice and Florence, there were no employees to help. Just kiosks. If you wanted a ticket, you figured it out yourself. If you had a question, there was nobody to ask. It wasn't a glimpse of the future. It was the present. That experience led us into a bigger discussion about AI, automation, and what customer service becomes when human interaction disappears. We unpacked a recent Anthropic report showing that customer service roles have some of the highest exposure to AI-driven task automation. But exposure to tasks is not the same as elimination of jobs. The deeper question is this: Is customer service simply a collection of transactions, or is it fundamentally about relationships? We discussed real-world results from an enterprise deployment of agentic AI where: Escalation rates were 4x higher when customers interacted with AI versus humans.Customers were significantly more likely to demand supervisors from bots.Contact volume increased by 50% in less than six months.Companies discovered that delivering bad news remains far more effective when done by a human.History suggests that new channels rarely reduce demand. Email didn't reduce contacts. ATMs didn't eliminate bank tellers. They changed the nature of the work. AI may do the same. At the same time, organizations are racing toward automation while learning that token costs, increased interactions, and customer behavior may complicate the promised economics. The technology is arriving at bullet-train speed. The question is no longer whether AI is coming. The question is: Who are you in an AI-first world? Will your company become a vending machine that happens to sell products? Or will you intentionally preserve the human elements that create trust, loyalty, and relationships? Because customer relationship management was never supposed to become customer technology management. Topics discussed: Anthropic's AI exposure findingsWhy task automation doesn't automatically eliminate jobsThe difference between transactional and relational serviceReal-world lessons from agentic AI deploymentsRising escalation rates with AI interactionsThe hidden cost of token consumptionWhy customers treat bots differently than humansThe future role of human agentsHow leaders should rethink customer service strategy in an AI-first era

AI hype is colliding with operational reality. A shoe company gains $127M in value by saying “AI,” while contact center leaders are told their entire model is obsolete. The shift from CCaaS to “Customer Experience Automation” reframes everything: not just support, but marketing, sales, and service collapsing into one AI-driven layer. The problem: the foundation is broken. Knowledge is fragmented. Customer data is duplicated. Organizations are misaligned. This episode dissects the gap between what the industry is promising and what companies can actually execute—and why customer service leaders are about to become the last line of defense when it fails. Key Quotes “Customer experience automation just means AI is now the one doing the talking.”“Your human agents can’t find the right answers today—but now the AI is supposed to?”“This isn’t a technology problem. It’s an organizational problem.”“If this fails, customer service cleans it up. Again.”“The train is moving. You either help steer it or get run over by it.”Practical Takeaways Stop debating AI capability. Start fixing knowledge.Treat data quality as a blocking issue, not a backlog item.Force alignment between marketing, sales, and service before automation.Assume AI will act autonomously—and design safeguards accordingly.Position customer service as the control layer, not the endpoint.

Exploring the future of AI in customer service, the role of agents, and how technology is transforming contact centers. AI in customer service The role of human agents vs. bots Generative AI and workflow orchestration Implications for contact center staffing Future trends in AI and automation 00:00 Introduction and Guest Credibility 01:04 The Reality of AI Agents Today 01:43 AI as a Smart Assistant in Customer Calls 02:35 Agent Interaction and Human Oversight 03:34 Issuing Credits and Automation in Calls 04:56 Differences from Past AI Systems 05:24 Generative AI and Workflow Orchestration 06:38 Automating Routine Tasks with API Calls 07:21 Focusing on Customer Conversations 08:30 The Future Role of Human Agents 09:18 The Next Generation of AI in Customer Support 09:58 Scaling AI and Multiple Conversations 10:57 Supervising Bots and AI Agents 11:51 AI in Escalations and Approvals 12:30 The Impact on Contact Center Staffing 13:25 New Entrants and Innovation in AI 14:30 Channels and Self-Service in the Future 15:22 Transitioning from Live Agents to Digital Support 15:53 Industry Trends and CFO Expectations 16:14 Implications for Workforce and Business Models 17:08 The Economics of AI and Customer Support 18:28 Preparing for the AI-Driven Contact Center 19:35 Historical Context and Future Predictions 20:15 Limitations and Realities of AI Adoption 21:09 Customer Behavior and AI Impact 22:04 Self-Service and Customer Expectations 22:50 The Extent of AI Automation 23:17 The Role of Technology in Customer Support 24:43 Amazon’s Approach to Automation 25:36 Limitations of Current AI Models 26:36 Decision-Making Boundaries for AI 27:04 The Human Element in AI-Driven Support 28:15 Closing Remarks and Future Outlook

This episode explores the transformative impact of AI on quality assurance (QA) in contact centers. Hosts Amas Tenumah and Bob Furniss discuss how AI is revolutionizing call monitoring, evaluation, and coaching, emphasizing practical steps for leaders to leverage this technology effectively. 00:00 Introduction to QA in Contact Centers 00:51 The Impact of AI on Quality Assurance 03:50 Revolutionizing QA Processes 09:09 Shifting Perspectives on QA Monitoring 12:39 Leveraging AI for Enhanced Coaching 17:40 The Future of QA in an AI-Driven World "You can listen to 100% of calls now" "AI can help identify patterns and issues" "Show the value your QA team delivers"

Exploring the evolution and challenges of Omnichannel strategies in customer service, from early concepts to current practices. Insights from industry veterans Bob Furniss and Amas Tenumah on what works, what doesn't, and how to focus on the most impactful channels. key topics Origins of Omnichannel in 2010 Challenges in integrating multiple channels The importance of focusing on top channels Customer expectations vs. technological capabilities Lessons learned from industry experiences Chapters 00:00 The Origins of Omnichannel 05:20 Challenges in Implementing Omnichannel Strategies 10:09 Finding Focus in Omnichannel Efforts 14:43 The Future of Customer Interaction

key topics summary In this episode, Bob Furniss and Amas Tenumah explore the roles and goals of supervisors in contact centers, emphasizing coaching, relationship-building, and strategic management. They share practical tips for setting goals, managing time, and developing future leaders. Goals and metrics for supervisors Time management and coaching focus Building relationships with team and leadership Mentoring future leaders in contact centers Strategic management at the director level sound bites "Make people think for themselves" "Be the evangelist for your contact center" "Spend time in books and learning" Chapters 00:00 Introduction to Remote Supervision Challenges 02:24 Setting Goals as a Supervisor 05:05 Coaching and Development Focus 07:47 Managing Up: Supervisor and Manager Dynamics 10:46 Building Relationships with Management 13:16 Transitioning to Managerial Goals 15:56 Efficiency vs. Effectiveness in Management 18:38 The Importance of Relationships in Leadership 21:22 Continuous Learning and Development

summary This episode features a deep dive into contact center KPIs, exploring their flaws and how to measure customer experience effectively. Hosts Amas Tenumah and Bob Furness challenge industry norms and share practical insights for improving contact center performance.  key  topics Flaws in common KPIs like FCR, Service Level, NPS The importance of standardized measurement How to interpret and act on KPI data Practical tips for contact center improvement  resources Contact Center Metrics Best Practices - https://example.com/contact-center-metrics Net Promoter Score (NPS) Explained - https://example.com/nps-explained Standardizing Contact Center KPIs - https://example.com/kpi-standardization