Published by Amas Tenumah & Bob Furniss
This is the public square for all things contact center. This is where the world's best Call & Contact center professionals come to get better at delivering a great experience for customers. Your contact center mentors - Amas Tenumah & Bob Furniss
Listen on Apple Podcasts24 min
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
19 min
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.
19 min
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
18 min
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 findings Why task automation doesn't automatically eliminate jobs The difference between transactional and relational service Real-world lessons from agentic AI deployments Rising escalation rates with AI interactions The hidden cost of token consumption Why customers treat bots differently than humans The future role of human agents How leaders should rethink customer service strategy in an AI-first era
20 min
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.
28 min
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
20 min
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"
15 min
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
24 min
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
18 min
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
18 min
Summary In this conversation, Amas Tenumah and Bob Furniss discuss the complexities of performance management, particularly focusing on forced distribution and its implications on employee evaluation and coaching. They explore alternative approaches to performance evaluation that prioritize individual performance over comparative scoring, emphasizing the importance of quality conversations in coaching. The discussion highlights the detrimental effects of scoring systems on employee morale and the need for a shift in focus towards meaningful feedback and development. Forced distribution can hinder team dynamics and employee morale. Performance should be evaluated based on individual contributions, not relative rankings. Coaching conversations should focus on quality and empathy rather than scores. Removing scores from evaluations can lead to more productive discussions. HR policies often prioritize consistency over individual performance nuances. Employees are aware of compensation disparities and may leverage offers from competitors. Quality conversations can improve coaching outcomes significantly. The focus should be on the overall experience rather than just numerical scores. Feedback mechanisms should be separated from compensation discussions. A shift in focus can lead to better employee engagement and performance.
21 min
In this conversation, Amas Tenumah and Bob Furniss discuss the intricacies of performance reviews, emphasizing the importance of coaching and effective feedback. They introduce the YMCA methodology as a framework for coaching conversations, highlighting the need for ongoing dialogue and employee ownership in the performance management process. The discussion also touches on the significance of crucial conversations in fostering a productive work environment. Takeaways Performance reviews should not contain surprises for employees. Coaching is essential for success in contact centers. The YMCA methodology helps structure coaching conversations. Effective feedback requires understanding the employee's perspective. Managers should focus on building relationships through dialogue. Crucial conversations are necessary for employee development. Setting clear expectations is vital for performance management. Follow-up is essential to ensure accountability and progress. Employees should feel empowered to own their performance issues. The coaching framework can be applied in various contexts, including personal relationships. Sound bites "We need you there at nine o'clock." "I never fired anyone in my entire career." "It works for your kids too." Chapters 00:00 Winter Weather and Performance Reviews 01:07 The Importance of Coaching in Performance Reviews 04:05 Effective Feedback and Coaching Frameworks 06:01 The YMCA Methodology for Coaching Conversations 11:32 Calibrating Expectations and Actions 17:01 Crucial Conversations and Employee Ownership
21 min
Summary In this conversation, Amas Tenumah and Bob Furniss discuss the implications of AI in quality assurance within contact centers. They explore the benefits of AI, such as increased coverage and trend spotting, while also addressing concerns about accuracy and the potential for AI to replace human interaction. The discussion emphasizes the importance of using AI to enhance human capabilities rather than eliminate them, and the need for effective coaching and data utilization to improve agent performance. Main Content: Understanding AI in Quality Assurance The podcast opens with a light-hearted discussion about the weather, but it quickly shifts focus to a pressing topic: the use of AI in quality assurance. Amas and Bob agree that deploying AI in this area can be beneficial, especially regarding monitoring agent performance. One of the primary advantages they mention is the ability to achieve 100% call coverage. Traditionally, QA teams may only review a small percentage of calls, leading to inaccurate assessments of agent performance. With AI, contact centers can analyze every call, providing a more accurate picture of quality and performance. Spotting Trends and Gaining Insights Another significant benefit of AI mentioned in the podcast is its capability to spot trends in customer interactions. Bob highlights the importance of understanding call spikes, such as the recent increase in calls related to a coupon offer. AI can analyze large data sets quickly, allowing managers to respond to customer needs more effectively. This capability not only improves the customer experience but also empowers managers to make informed decisions based on real-time data. The Risks of Relying Solely on AI While Amas and Bob are enthusiastic about the potential of AI, they also express concern over its limitations. One critical issue is the accuracy of AI assessments. Amas warns that AI systems are often trained on human data, which can lead to discrepancies in scoring calls. He emphasizes the need for a human touch in QA processes, suggesting that AI should assist rather than replace human judgment. Without human oversight, there's a risk that AI can misinterpret nuances in customer-agent interactions, leading to misguided conclusions. The Importance of Human Interaction The conversation takes a deeper turn as they discuss the nature of customer service as a human interaction. Bob argues that technology should enhance the capabilities of QA teams, not eliminate them. He points out that while AI can streamline processes, it cannot replicate the empathy and understanding that a human agent brings to a conversation. The hosts advocate for a balanced approach where AI tools are used to support agents rather than replace them, ensuring that customer experiences remain positive and personalized. Conclusion: In conclusion, while AI presents exciting opportunities for enhancing quality assurance in contact centers, it is essential to approach its implementation with caution. Amas and Bob remind us that technology should complement human skills and insights rather than undermine them. By finding the right balance, organizations can leverage AI to improve performance while maintaining the human touch that is vital in customer service. Key Takeaways: 1. AI can enhance quality assurance by providing 100% call coverage and spotting trends in customer interactions. 2. The accuracy of AI assessments can be problematic; human oversight is crucial in the QA process. 3. Customer service is fundamentally a human interaction, and technology should support, not replace, human agents. Tags: AI, Quality Assurance, Contact Centers, Customer Service, Technology, Human Interaction, Trends in Customer Experience, Agent Performance, Podcast Insights
17 min
Summary In this episode, Amas Tenumah and Bob Furniss delve into the current state of Software as a Service (SaaS) and its intersection with artificial intelligence (AI), particularly in the context of contact centers. They discuss the recent downturn in stock prices for major SaaS companies like Salesforce and ServiceNow, attributing this to Wall Street's skepticism about the actual impact of AI on these platforms. Amas expresses concern that the hype surrounding AI is outpacing the reality of its implementation, suggesting that many companies are not yet ready to fully embrace AI-driven solutions. Bob echoes this sentiment, emphasizing the importance of expertise and experience in successfully implementing these technologies. AI hype is ahead of customer readiness. Wall Street is skeptical about SaaS companies' future. Vibe coding may not replace the need for expertise. Experience in implementation outweighs potential of new tech. Both extremes of AI adoption are currently inaccurate. Sound bites "Service now stock hasn't been this cheap in like four years." "There's two different stories going on here." "Both extremes are wrong today." Chapters 00:00 Introduction and Current Market Overview 00:53 The Impact of AI on SaaS Companies 03:42 Building vs. Buying: The New Paradigm 07:18 Navigating Contract Renewals and New Technologies 10:49 The Future of AI in the Contact Center Industry 13:38 Conclusion and Key Takeaways
18 min
Most customer experience goals are meaningless. In this episode, Bob Furniss and Amas Tenumah dismantle the way contact centers set annual CX metrics and explain why leaders keep optimizing numbers that customers neither notice nor value. Using insights from a John Goodman article on CX goal-setting, the conversation exposes the disconnect between executives, customers, and frontline teams—and why automation, deflection, and "respectable" percentage improvements often make service worse, not better. This episode is about shifting from internally convenient metrics to customer-impactful outcomes. What You'll Hear Why CX goals are often chosen because they sound reasonable, not because they solve customer problems How executives chase a single "magic number" instead of understanding service complexity The fundamental incentive gap between customers and senior leadership Why customers and frontline agents are aligned—but executives aren't How automation and bots optimize company metrics while frustrating customers Where AI actually helps: analyzing volume, root causes, and systemic friction Why average metrics (ASA, AHT) distort reality and reward the wrong behavior How poor goal-setting punishes leaders who successfully automate the "easy" work The risk of letting someone else define your goals if you don't take control A real-world example of automation done right—and how bad metrics mislabel it as failure Key Takeaways Vanity metrics don't fix customer experience Deflection and containment may look good internally while actively harming trust CX leaders must own the narrative or be trapped chasing numbers they don't believe in AI should surface customer pain, not just reduce contact volume Goals should reflect customer outcomes, not executive convenience Resources Mentioned John Goodman's article on CX goal-setting (referenced in discussion) HOLD: The Suffering Economy of Customer Service by Amas Tenumah Available on Amazon Signed copies at waitingforservice.com Who This Episode Is For Contact center and CX leaders setting 2026 goals Executives relying on NPS, ASA, AHT, or deflection as proxies for success Practitioners tired of fixing the wrong problems Anyone responsible for explaining service performance to leadership
13 min
2025 predictions — graded AI-powered knowledge Bob's 2025 prediction: AI would dramatically improve knowledge in contact centers. Result: Early but mostly wrong. The technology moved, but the data did not. Knowledge bases were too fragmented, too dirty, and too poorly governed for AI to meaningfully improve frontline work. The industry instead spent another year chasing bots, automation, and surface-level "AI assistants." Grade: C+ The failure was not AI. It was the state of enterprise knowledge. Remote work reversal Bob's 2025 prediction: Work-from-home would shrink and revert toward pre-COVID norms. Result: Correct. Remote and hybrid work has fallen to within five percentage points of pre-COVID levels. Companies quietly reversed course not because it helped customers or employees, but because leadership never learned how to manage distributed teams. Hybrid was the worst of both worlds: frontline leaders juggling physical rooms, video calls, and dashboards without the training or structure to do any of it well. Grade: A Why remote work collapsed The reversal was not ideological. It was operational. Executives defaulted back to what felt controllable: physical presence. Organizations refused to do the hard work of re-engineering leadership, coaching, quality management, and accountability for a distributed workforce. They solved a people problem with proximity. Amas' prediction for 2026 Voice comes back. Digital channels absorbed most of the AI hype: chat, bots, messaging, and self-service. But customers never stopped calling. Voice is where frustration spikes, where trust is tested, and where automation breaks down. Amas' call: 2026 will be the year voice reasserts itself as the center of the customer relationship — and the CCaaS market will look radically different by 2027 because of it. Bob's prediction for 2026 Data becomes the bottleneck. AI will only become useful where it has access to clean, structured, reliable data. The industry rushed into AI before fixing the foundations: knowledge, case data, call logs, customer history, and operational context. 2026 will be the year contact centers slow down, audit their data, and rebuild the plumbing that AI actually runs on. No data. No intelligence. What the industry is claiming Analysts and vendors are promising three things for 2026: • Predictive and proactive service • Agent empowerment through AI • Fewer humans in contact centers Bob and Amas reject the third and remain skeptical of the first two without structural change. The hype assumes AI will replace labor. Reality says AI will expose how broken the systems around labor really are. Amas' 2026 wish Stop calling software "agents." For twenty years, "agent" meant a human being doing emotional, cognitive, and relational labor. Rebranding bots as agents erases the workforce and confuses accountability. Language shapes power. That battle matters. Bob's 2026 wish Focus on the employee. AI should not be used to replace people. It should be used to remove friction from their work: searching, documenting, switching systems, hunting for answers. Knowledge was always the real use case. The industry just skipped the hard part. Core takeaway 2025 proved that AI without data, governance, and human-centered design does not transform anything. It only adds noise. 2026 will reward the companies that stop chasing demos and start rebuilding the foundations: voice, knowledge, data, and frontline enablement. That is where the real disruption will come from.
33 min
Amas Tenumah explains why customer service is not "broken" but intentionally designed to fail. Drawing on decades inside contact centers, historical research, and real corporate incentives, he argues that long waits, deflection, and automation-first strategies are features—not bugs. The conversation dismantles common CX myths, challenges executive complacency, and frames consumer behavior as the only force capable of triggering real change. Core Themes The Suffering Economy of Customer Service: When service is universally bad across industries, it's systemic. Incentives—not incompetence—drive outcomes. Why This Is a "How Dare You" Book: The indictment is aimed squarely at executives who treat service as a cost center while overfunding marketing narratives. Marketing Replaced Service as Trust Mechanism: Historically, service was marketing. Industrialized marketing severed that link, allowing companies to tolerate bad service and buy growth instead. Metrics That Poison Service: Deflection, containment, and avoidance KPIs reward companies for not talking to customers—while punishing leaders who try to deliver what customers actually want. Wait Times Are Engineered: Hold times are budgeted, modeled, and accepted. They are designed friction, not operational accidents. AI as Distance, Not Salvation: AI is currently deployed to protect companies from customers, not customers from friction. It scales avoidance unless incentives change. Executives Don't Experience Their Own Service: Many leaders despise customer service—just not their own. Forcing executives to call their own 1-800 numbers is revelatory and uncomfortable. The Revolt Is Consumer-Led: Change will not come from CX professionals alone. It comes when consumers punish bad service with their wallets and reward companies that respect their time. Notable Moments The opening story of the 1750 BC clay tablet complaint —the first recorded customer service grievance—reads like a modern Amazon review. The Chipotle refund anecdote exposes time theft: hours of customer labor to recover trivial amounts of money. The contrast between automation done for customers versus automation used to avoid them. Practical Takeaways For Consumers: Vote with your wallet. Pay slightly more. Wait one more day. Call customer service before you buy big-ticket items. For Service Leaders: If your CEO doesn't believe in service as value creation, your job is to change their mind—or change jobs. Data plus customer stories are the leverage. For Executives: Service is deferred revenue protection. Treating it purely as cost is strategic malpractice. Resources Mentioned Book: HOLD: The Suffering Economy of Customer Service — And the Revolt That's Long Overdue Signed Copies & Tools: waitingforservice.com Consumer scripts Cancellation guides Practitioner playbooks No email required
22 min
Summary The conversation explores the integration of AI in sales, focusing on how it enhances customer engagement and improves sales efficiency. Bob Furniss discusses the importance of using data to empower salespeople rather than reducing their numbers, emphasizing a customer-centric approach to AI in sales. Takeaways AI can enhance customer engagement in sales. The focus should be on empowering salespeople with data. AI is not just about reducing costs but improving efficiency. Sales strategies should prioritize customer needs. Data-driven insights can lead to better sales outcomes. AI can make sales calls faster and smarter. The role of AI is to support, not replace, sales personnel. Understanding customer needs is crucial in sales. AI tools should be designed to assist sales processes. The future of sales lies in the integration of technology and human touch.
26 min
Summary In this conversation, Amas Tenumah, Bob Furniss and Brad Cleveland discusses the three levels of value that contact centers create: efficiency, customer satisfaction and loyalty, and strategic insights provided by AI. He emphasizes the importance of these levels in improving products, services, and processes. Takeaways there's three levels of value that contact centers create Level one is efficiency customer satisfaction, loyalty, if we do a good job it's the strategic insight that AI can provide it can still tell us, hey, here's a trend I'm seeing Here's an opportunity to improve products and services AI doesn't have to be perfect to provide value Strategic insights can drive business improvements Understanding trends is crucial for growth Contact centers play a vital role in customer experience Titles Unlocking Value in Contact Centers The Role of AI in Customer Service Sound bites "Level one is efficiency" "customer satisfaction, loyalty, if we do a good job" "it's the strategic insight that AI can provide" Chapters 00:00 Introduction to the Contact Center Show 00:27 AI and Its Impact on Customer Interactions 00:31 Future Jobs in the Contact Center Industry
22 min
Low-Cost, High-Impact CX Improvements The Power of Language: Transform "I can't" into "How can we" Shift from "I have to" to "We get to" Being "impeccable with your word" (inspired by The Four Agreements ) Words trigger emotional responses that shape customer perception Getting CX Buy-In Across Organizations The Alignment Problem: CX initiatives fail when metrics aren't shared across departments Success came when executives adopted the same CX metrics as the CX team Without shared goals, customer insights get shelved with "we'll get to it later" The Pilot Program Strategy: Start small before asking for big budgets Show proof of concept with intentional, measurable pilots Use success to rally and align different areas of the company Real Example - CX Day Success: Introduced first-ever CX Day celebration at 145-year-old engineering company Started small despite skepticism Now an annual tradition that continues after her departure Rethinking CX Metrics Beyond Traditional Measurements: NPS and effort scores are starting points, not endpoints "Satisfaction" is no longer good enough (it's the equivalent of "fine") New focus: Emotional altitude across every touchpoint The Emotional Impact: Brains constantly cycle between thinking and feeling Emotions create lasting imprints that shape brand perception Research shows: 12 positive experiences needed to overcome 1 negative Measure emotional highs to identify gaps and successes The Four Rs of CX Impact: Revenue Retention Reputation Referrals The Future of Contact Centers Human + AI Integration: Smart companies intentionally map where humans add value vs. where AI should handle interactions The answer is "both/and" not "either/or" Critical: Validate designs with real customers, not just internal teams The Contact Center Superpower: Contact center teams speak to more customers in a week than other departments do in a year This proximity to customers gives agents unique power to be organizational change agents Voice of customer insights should inform product development, marketing messaging, and more Words Matter in the AI Era: Example: Website offering "24/7 support, our guides are happy to help you" "Guides" for both humans and AI feels impersonal Naming and framing still matters The Power of Customer Voice The 10-Minute Video Story: A contact center leader captured customer feedback about a failed new product. At an executive meeting, he played a 10-minute compilation of customer complaints. The CEO's initial advisor said it was a career-ending mistake The CEO walked out during the video Result: CEO returned and said it was "the best 10 minutes anybody's ever played" and named him employee #1 Customer voice changed the company's trajectory The Validation Imperative: Internal perspective isn't enough Customer validation must be iterative, not one-time Can't use internal team as proxy for outside voice Both internal knowledge AND customer validation matter The Fundamental C-Suite Challenge When C-suite leaders aren't aligned in their meetings, misalignment trickles down through the entire organization. This is the root problem preventing effective CX implementation. Notable Stories The Morgan & Morgan Tattoo Story: Leadership promised to get company logo tattoos if the team hit an unprecedented conversion rate goal. When they achieved it, 40 leaders got matching tattoos - four tattoo artists came in for the day. The question became: "Is your brand tattoo-worthy?" Stacey's Podcast Origin: Bought her first microphone for under $50, then waited six months before taking it out of the box due to fear. That mic ignited her journey to intentionally sharing her voice through podcasting. Living Podcasting: At the conference, Stacey pulled out her mic to record a 10-minute session where Amas (who has Parkinson's) gave direct advice to her relative with a similar condition - creating immediate, personal value rather than secondhand communication. Takeaways for Contact Center Leaders Language shapes reality - Small word changes create emotional shifts in customer experience Demand metric alignment - CX can't succeed unless executives share the same measurements Start with pilots - Prove value small-scale before requesting major investment Leverage your proximity to customers - Use it to become the organizational glue and change agent Validate everything with real customers - Internal assumptions aren't enough Map the human-AI journey intentionally - Design where each adds value, then test with customers Bring customer voice to leadership - Sometimes the most powerful thing is making them listen directly Average isn't acceptable - Move beyond satisfaction to creating emotional highs