On CX Innovators, we sit down with enterprise CX leaders and innovators to unpack what’s driving exceptional customer experience today. We explore strategy, technology, operations, and the human side of CX: and where the industry is headed next.
Means, motive, and opportunity: why LendingTree uses a crime framework to train CX empathy
Brock Thompson, VP of Customer Fulfillment at LendingTree, shares how a crime framework trains CX empathy and guides AI adoption in financial services.
26 Feb 2026TRANSCRIPT
How one IVR change eliminated 2.7 million inbound calls a year
Wes Dudley has spent over 30 years leading CX at scale, including JCPenney, Mitchell Gold + Bob Williams, and Broad River Retail, the largest licensee of Ashley Furniture. He moved ticket resolution from nine days to eight hours, eliminated 2.7 million inbound calls annually with a single IVR change, and built a 750-seat contact center during COVID while call volume jumped from 16,000 to 115,000 calls a day overnight.
This conversation covers the specific decisions behind those numbers, including how he sequences people, process, and technology investments, when to keep humans in the loop on high-stakes interactions, and what he looks for in a technology partner after seeing both sides of good and bad ones.
Topics Discussed:
Why AI without a defined plan consistently underdelivers
Moving ticket resolution from 9 days to 8 hours through routing, not technology
Eliminating 2.7 million calls annually with one IVR implementation
Three-tier triage model built to absorb a 7x overnight call surge during COVID
PPT framework for sequencing people, process, and technology decisions
How the nature of high-emotion purchases should shape your AI and human handoff design
CX All-Stars identity program and its role in remote team retention
What Wes looks for in a technology partner beyond price and product
Listen to more episodes:
Apple (https://podcasts.apple.com/us/podcast/ai-cx-innovators/id1802013873)
Spotify (https://open.spotify.com/show/0XpZj9bW7OkgXYgRX4DOTJ?si=14589b77ec1d4d00)
YouTube (https://www.youtube.com/playlist?list=PL6T5PUa9NwVKi275ahocKKL1ZG341d2-u)
5 Feb 2026TRANSCRIPT
From 40-minute wait times to under 60 seconds without adding headcount | Allan Harari
Allan Harari cut Comerica Bank's contact center wait times from 20-40 minutes to under 60 seconds without hiring additional agents. The transformation required rebuilding foundational infrastructure first—workforce management systems providing real-time data instead of monthly reports, quality assurance platforms generating actionable insights, and AI deployed as agent augmentation rather than replacement. His three-year roadmap prioritized operational discipline over technology shortcuts, recovering 10% capacity through schedule optimization before any AI implementation.
At USAA, he led a specialized team handling 40,000+ loss-of-loved-one calls monthly for military families, creating direct experience with where human judgment remains non-negotiable versus where AI accelerates outcomes. His vendor selection framework cuts through sales pitches: define the exact problem, know what success looks like, then ask questions exposing actual delivery capabilities. By choosing no-code solutions managed by frontline staff who understand the problems daily, he avoided the overhead trap of building custom solutions from component pieces.
Topics discussed:
Cutting average handle time from 11+ minutes to 7 minutes through technology and contact center hygiene
Recovering 10% capacity by reducing lunch breaks from 60 to 30 minutes with proper scheduling
Eliminating 2.5-minute gaps between calls by fixing telephony auto-in state configuration
Deploying auto-summarization reducing after-call work to 3 seconds instead of manual note-taking across 20 systems
Maintaining 92% CSAT despite 20-40 minute wait times through customer loyalty, then improving speed without sacrificing quality
Leading 40,000+ monthly loss-of-loved-one calls at USAA requiring human empathy for military families accessing critical benefits
Selecting no-code AI platforms allowing frontline staff to design solutions versus hiring engineering armies
Using "box of Legos" vendor evaluation: pre-built capabilities you assemble versus raw components requiring custom development
Defining top three problems keeping you up at night before engaging vendors to avoid broad, unfocused implementations
Building AI literacy by teaching proper prompting techniques rather than expecting plug-and-play magic
Listen to more episodes:
Apple (https://podcasts.apple.com/us/podcast/ai-cx-innovators/id1802013873)
Spotify (https://open.spotify.com/show/0XpZj9bW7OkgXYgRX4DOTJ?si=14589b77ec1d4d00)
YouTube (https://www.youtube.com/playlist?list=PL6T5PUa9NwVKi275ahocKKL1ZG341d2-u)
22 Jan 2026TRANSCRIPT
Case age over handle time: The metric that actually improved NPS, CSAT, and customer spend | Zach Greco
Zach Greco (https://www.linkedin.com/in/zachgreco/) runs a 100-agent fully remote contact center at Floor & Decor (https://www.flooranddecor.com/?srsltid=AfmBOoqWE5qwrtHo2t11VKHKqbP1bgu5QTPlwWDkqpFT1nQyo6bD2Fj_) with under 10% annual turnover. His operational philosophy starts with one question before deploying any technology: "What's in it for them?" This applies whether rolling out AI knowledge bases, CRM workflow changes, or new telephony systems. By training their chatbot exclusively on indexed website content, his team eliminated the hallucination problem while creating a clear feedback loop—when the AI gives wrong answers, they know exactly which source page needs fixing.
His team discovered that case age—not first call resolution or handle time—was the metric that actually moved the business. Longer case resolution times correlated directly with higher costs, lower NPS, and reduced customer spend. By focusing operational improvements on shrinking case age, they improved outcomes across the board without needing a "silver bullet" technology replacement.
Topics discussed:
Case age reduction as the primary driver of NPS, CSAT, and customer lifetime value
Training AI chatbots exclusively on indexed company content to eliminate hallucinations
Achieving 10% annual turnover in fully remote operations through life-work balance prioritization
Agent-to-AI consultation model where bots query human agents mid-conversation without customer transfers
Technology adoption barriers in retail environments and the WIIFM (What's In It For Me) framework
Evaluating when AI automation fails: warranty claim diagnosis where misreading moisture damage costs thousands
Breaking down questionnaire friction that causes frontline workarounds and data quality issues
Multi-channel customer preference mapping for professional contractors versus DIY consumers
Listen to more episodes:
Apple (https://podcasts.apple.com/us/podcast/ai-cx-innovators/id1802013873)
Spotify (https://open.spotify.com/show/0XpZj9bW7OkgXYgRX4DOTJ?si=14589b77ec1d4d00)
YouTube (https://www.youtube.com/playlist?list=PL6T5PUa9NwVKi275ahocKKL1ZG341d2-u)
4 Dec 2025TRANSCRIPT
The 3-action formula that predicts above-average customer retention | David Melendez
David Melendez, Senior Director, shares his three-action formula for predicting customer retention by prioritizing value delivery over access provisioning, advocating for strategic AI implementation after optimizing existing systems in this interview.
20 Nov 2025TRANSCRIPT
S&S Activewear’s Laura Turner on Technology Adoption without Perfect Data Infrastructure
Laura Turner of S&S Activewear discusses how customer self-selection outperformed AI-driven segmentation and how employee experience impacts customer experience, advocating for manageable AI implementation and measurable outcomes in this interview.
6 Nov 2025TRANSCRIPT
The birthday gift test: How to measure if your AI personalization actually knows customers
Leala Francis from AG1 shares how conversational AI and a consolidated customer-centric organizational design enable proactive adjustments to supply forecasts and personalized customer experiences, ensuring genuine customer knowledge through the "birthday gift test."
3 Oct 2025TRANSCRIPT
How CarParts.com Serves 43 Million Customers: Why Traditional System Integration May Become Obsolete
Aurelia Pollet, Director of Customer Experience at CarParts.com, discusses managing customer relationships at scale without a traditional CRM, exploring how AI streamlines data across systems, potentially rendering complex integrations obsolete in this interview.
18 Sept 2025TRANSCRIPT
Former NICE’s CEO Paul Jarman on Micro-Understanding for Technology Leadership
Paul Jarman, Executive Chairman at Gigg, shares his insights on technology leadership and AI adoption, emphasizing agent efficiency and predicting CRM consolidation through acquisition in this interview.
14 Aug 2025
Notion's Emma Auscher on Creating "Everyday Luxury" AI Experiences
Emma Auscher, Notion's Global Head of CX, shares with Ashish how their "no customer left behind" approach creates "everyday luxury" AI experiences by balancing human and AI interactions.
5 Aug 2025
Wayfair's Shantanu Das on Three Key AI Value-Creation Modes
Shantanu Das of Wayfair discusses Wayfair's AI implementation, focusing on productivity, insight generation, and workflow reimagination, in an interview with Ashish, which leads to real-time customer coaching and agent empowerment.
18 Jul 2025
AT&T's Deepak Sharma on Why AI That Feels Like Magic Is AI That Works
Deepak Sharma of AT&T informs Ashish about building AI-ready infrastructure and utilizing digital avatars to improve customer experiences, emphasizing simplicity for successful AI adoption in this interview.
16 Jun 2025
QuinStreet’s Tyler Orrell on Challenging “Upper Right Quadrant” Thinking
QuinStreet's VP of Contact Center Operations Tyler Orrell informs Ashish about a surgical approach to AI implementation, focusing on business impact and challenging conventional vendor selection, in this interview.
27 May 2025
Carta’s David DeMarco on Preserving Human Touch for High-Stakes Financial CX
David DeMarco of Carta discusses how AI automation in customer experience requires a focus on quality assurance and human oversight for complex financial discussions, as Ashish interviews him.
24 Apr 2025
Wayfair’s Prasanna Chand on Using AI to Predict Customer Satisfaction
When only 20-25% of customers complete satisfaction surveys — and even those are primarily negative experiences — how can you truly understand your entire customer base? In this episode of AI CX Innovators, Prasanna Chand, Head of Data & Digital Transformation at Wayfair, reveals how they’re using AI to predict customer satisfaction scores with 85% correlation to actual survey results, providing a complete picture beyond the inherently skewed feedback pool.
Prasanna takes Ashish through Wayfair’s journey implementing AI across their customer experience operations, from identifying critical issues within days of launching their loyalty program to helping agents self-coach through personalized insights rather than generic examples. With ChatGPT’s launch as the tipping point, he explains how Wayfair strategically separated which AI solutions to build versus buy, and why their partnership with Level AI has been transformative for users across the organization.
Topics Discussed:
- How Wayfair’s three-pronged approach to customer data analytics focuses on conversational insights, making business users more data-friendly without SQL knowledge, and creating an enterprise architecture that balances hyperscaler platforms with boutique vendor solutions.
- The tactical advantage of AI-powered analytics that discovered loyalty program issues within days of launch, bypassing the months-long traditional data warehouse reporting cycle and uncovering specific functional problems hindering customer adoption.
- Why AI-predicted customer satisfaction scores (achieving 85% correlation with actual surveys) solve the inherent bias problem when only 20-25% of customers complete surveys, but still don’t replace manual CSAT collection.
- Wayfair’s strategic bifurcation approach to AI implementation: building and extending homegrown systems for agent support while purchasing software for integration with third-party telephony, workforce management, and quality systems.
- How connecting journey analytics with conversation data enables FCR analysis to identify and reduce multi-contact scenarios, allowing teams to immediately see negative sentiment pathways and make targeted improvements.
- Three essential best practices for implementing AI transformation: educating stakeholders to manage resistance and expectations, selecting partners who can innovate at the market’s pace, and identifying use cases with quick ROI through plug-and-play implementations.
- The evolution from random sampling in quality assurance to holistic review capabilities, enabling personalized agent coaching with specific conversation examples rather than generic feedback, fundamentally changing how agents self-improve.
- Leveraging AI for language translation and virtual training to overcome language barriers in agent development, creating training in one language and delivering it through human-like virtual instructors in multiple languages.
Listen to more episodes:
Apple
Spotify
YouTube
13 Mar 2025
AI Adoption in CX Teams with Andy Yasutake, Leader at Edgevana, ex-AirBnB, LinkedIn, & eBay
AI isn't replacing humans in customer experience — it's transforming them. In our very first episode of AI CX Innovators, Ashish Nagar, Founder & CEO of Level AI, dives deep with inaugural guest Andy Yasutake, SVP and Global Head of Strategic Growth & Ventures at Edgevana.
As former architect of customer experience transformations at tech giants eBay, LinkedIn, and Airbnb and with over 25 years shaping how global brands interact with millions of customers, Andy presents his battle-tested strategies for leading multi-million-dollar AI initiatives, navigating organizational resistance, and implementing generative AI at enterprise scale.
From turning the 2020 pandemic into an opportunity for Airbnb's technology transformation to personally helping Brian Chesky deliver his vision of "11-star experiences," Andy shares candid insights few technology leaders have experienced across three waves of digital disruption.
Topics Discussed:
The challenges of managing data due to rapid AI technology evolution and how companies must adapt their strategies from multi-year implementations to iterative approaches delivering value in days and weeks.
The process of determining when to build in-house vs. partner with AI vendors, including a framework for distinguishing between "core" business differentiators and "contextual" systems already solved elsewhere.
How successful companies develop integrated product-operations roadmaps rather than treating AI as a technology to be shipped over the fence, with monthly iteration checkpoints aligned to business seasonality.
Why Airbnb deliberately delayed customer-facing GenAI implementations despite being partners with OpenAI and Microsoft, focusing first on internal learning while competitors rushed to market.
The complexities of calculating true GenAI implementation costs, including unexpected compute expenses many companies failed to factor into early business cases.
How CX organizations can move from cost centers to strategic drivers by using rich customer data to demonstrate direct impact on executive-level metrics and brand differentiation.
The organizational structure shift that doubled AI adoption rates at LinkedIn and Airbnb by moving product teams under operational leadership rather than central technology organizations.
Andy's "Iron Man vs. dystopia" vision for AI's impact on contact centers, where technology augments human capabilities rather than replacing them, enabling agents to handle significantly more complex issues with higher quality.
Inside a recent episode
Means, motive, and opportunity: why LendingTree uses a crime framework to train CX empathy
Published 12 Mar 2026 · Transcript excerpt
[…] Our our allegiance and alliance to to the front of the pack so as we lean into things like AI how do we best represent AI not just to the consumer and using that in in the wild today but also internally how can we make our our operations more efficient how can we use AI to help us learn more to automate certain things and over the course of the last year. I'm proud to say that that we've spearheaded in my division our first AI facing consumer product and while it's not you know at scale yet it is something that we are growing week over week and month over months we're getting great feedback from our consumers we're able to match very closely to the parity of of a live human product we're also terming a lot of things internally so we're able to to transcribe calls to look at sentiment to hear feedback look at the adherence to script that that our staff is. […]
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