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

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

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

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.

• 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

• 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

• 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

• 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

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.

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

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.

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.

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.

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.

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.

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.

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.

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.

• 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.

• 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.

• 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.

• 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

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.

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