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