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.