Contact Center Show

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 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

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