Contact Center Show

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 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