AI for CX Summit – Day 1 Session 1 Recap

The AI for CX Summit opened with a high-impact panel discussion designed to help leaders cut through the noise around AI adoption. The session, titled “Opening Keynote Panel: Types of AI for CX, Your Starting Point”, brought together leading minds in contact center strategy and AI transformation to explore how organizations can align AI initiatives to real business outcomes—without losing sight of the people behind the process.

Moderated by Scott Logan, Chief Marketing Officer at AmplifAI, the discussion featured expert insights from:

• Sean Minter, Founder and CEO of AmplifAI
  
• John Heaps, VP of Channel and Partner Sales at Sanas
  
• John Paullin (JP), Director of Sales Engineering for CX and AI at Avant Communication

Who This Panel Is For

Whether you’re evaluating AI for performance management, quality automation, or conversational workflows, this session gave CX leaders a practical framework for knowing:

• Which type of AI fits where
  
• How to structure your AI strategy before selecting tools
  
• And how to build a strong ROI case without over engineering the rollout
  

5 Key Takeaways from the Opening Panel

1. Start with the Problem You’re Trying to Solve

Before evaluating vendors, before mapping workflows, before training models—leaders need to define the problem.

As Sean Minter put it:

“AI is not a magic bullet—it’s a tool. It only works if you know which business problem you’re solving and which data sources can support that.”

Many organizations get caught up in the AI hype cycle, chasing innovation without clarity. The panel warned against implementing technology in search of a use case. Instead, begin with a challenge that’s both well-understood and worth solving—whether that’s high handle time, agent churn, poor QA coverage, or inconsistent customer experience.

Too often, data lives in silos and no one has ownership of a unified view of performance. Until that’s resolved, AI won’t deliver meaningful returns.

2. Understand the Different Types of AI—and Where They Fit

Not all AI is created equal—and not all of it belongs in every moment of the customer journey. The panel focused on two primary use cases that, when combined, can dramatically elevate CX:

• Agent-facing AI: Tools like AmplifAI help supervisors identify coaching moments, drive performance improvement, and scale QA—automating the repetitive while surfacing high-impact interventions.
• Customer-facing AI: Sanas demonstrated how AI can enhance human communication, especially in offshore environments. Through real-time accent translation and bidirectional noise cancellation, it improves clarity without replacing the human agent—resulting in measurable lifts in CSAT and bookings.
  

By applying the right type of AI to the right use case, companies can remove friction, increase efficiency, and preserve what customers value most: human understanding.

3. Build ROI Models Around Time Savings

If there’s one thing every contact center wants more of, it’s time. Time to coach. Time to act. Time to improve.

JP highlighted this point with clarity:

“Time is the universal value driver.”

Instead of focusing solely on long-term metrics like CSAT or NPS—which, while important, can take months to shift—leaders should also evaluate how AI can reduce the time spent gathering data, listening to calls, or triaging coaching needs.

Sean Minter added that organizations often already have the data—they just don’t have it unified.

“You don’t need more data. You need to connect what you already have and point people to the right actions.”

The ROI becomes immediate when AI cuts hours of manual effort across supervisors, QA teams, and analysts—turning lagging data into leading decisions.

4. Define Success Criteria Before You Buy

Across the panel, one theme emerged loud and clear: AI success requires more than a software license.

The panel emphasized that leaders must:

• Define the exact problem to solve
• Establish quantifiable success criteria
• Ensure vendor accountability through hands-on implementation and ongoing support
  

Without these elements in place, organizations risk stalled rollouts, underutilized tools, and frustrated teams.

“You can’t set it and forget it,” said Heaps. “It’s not just about what the tech does—it’s whether you’ve scoped the challenge tightly enough to see meaningful movement in your KPIs.”

5. Combine AI with Human Intelligence for Long-Term Success

In the rush toward automation, there’s a growing concern: are we losing the human element?

Heaps addressed this head-on:

“Customers still want to talk to people. AI should support agents—not replace them.”

He shared a compelling case study: a global hospitality brand that saw a 50% increase in bookings and a 42% drop in call transfers by deploying Sanas to eliminate communication barriers between offshore agents and guests. The result wasn’t just improved efficiency—it was improved connection.

Meanwhile, Minter emphasized that as simple tasks are increasingly automated, what’s left for agents is more complex—and more valuable. AI’s role, then, is to help humans handle that complexity more effectively, not make them obsolete.

Expert Quote to Remember

“Get off the sidelines. Define your goals, get your data aligned, and take the first step. Companies that wait are already behind.”

— JP Paullin, Avant Communications

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