Brock Thompson spent 22 years in digital insurance and financial services, starting as a call center agent and eventually becoming VP of Customer Fulfillment at LendingTree, where he oversees inbound and outbound calls, SMS programs, calls technology, and the company's AI voice division. Having sat on both sides of the phone shapes how he thinks about where technology belongs in high-stakes consumer conversations.

In this episode, he gets specific about how LendingTree built and justified their first consumer-facing AI voice product, how they brought skeptical call teams along, and why their approach to AI adoption looks more like a marathon than a sprint.

Topics Discussed:

• The three-part "stickiness" framework: emotional, technological, and financial

• Starting the AI business case with one linear, fully measurable conversation path

• "Gut drives the test, data drives the decision" as a buying and build philosophy

• Why legal and compliance are the first call, not the last, in any AI rollout

• The means, motive, and opportunity framework for training human agent empathy

• Where human agents still outperform AI in financial services conversations

• Reframing AI to call teams as reallocation of effort, not elimination of roles

• Evaluating vendors by whether they understand your actual problem, not just their product

• The rule of one: prove it once, measure it once, then scale

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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

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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

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Zach Greco runs a 100-agent fully remote contact center at Floor & Decor 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

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David Melendez process-mapped Instructure's entire onboarding flow and realized they were operationally optimized for the wrong outcome: getting customers keys to their purchase rather than setting them up to renew. The team had confused access provisioning with value delivery—a distinction that becomes critical when services drive the onboarding motion for a 50%+ market share LMS provider.

David, Sr. Director of Customer Experience Strategy & Operations, brings a method from his Alteryx days: identify the three customer actions that correlate with above-average renewals, then architect everything to make those frictionless. At Alteryx it was community membership, connecting to data, and running a workflow. At Instructure, he's rebuilding to answer that question before pointing AI agents at consultant workflows.

His approach to AI adoption counters the vendor pressure: inventory what's already available in your stack (Gainsight's Atlas, Staircase AI), instrument proper CSM role definitions in those systems, then automate only the repeatable service consultant tasks with clean data inputs. No foundation means AI accelerates broken processes.

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Implementing conversational AI revealed an unexpected fault line in their customer base for Laura Turner, Head of CX at S&S Activewear. Despite assumptions about how different customer segments would respond to automation, the split came down to something simpler: some customers want speed through self-service, and others insist on human connection regardless of request complexity. This insight forced a strategic pivot away from predetermined customer journeys toward self-selection, allowing customers to choose their own path while educating them on faster options. 

The lesson extends beyond technology implementation to the foundation of successful CX programs: employee experience determines customer experience, and teams can't deliver exceptional service without proper training, tools, and processes. For example, when Laura implemented a Customer Integration Pulse survey to track sentiment through major system transitions, the real-time feedback revealed missed features critical to one customer base, enabling quick adjustments and transparent communication about roadmap additions. 

Her approach to AI readiness challenges the myth of perfect data prerequisites. Instead of waiting for pristine datasets, she advocates identifying manageable friction points where automation drives measurable outcomes. 

Topics Discussed:

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AG1 scaled from one SKU to nine in 18 months while maintaining 98-99% CSAT. Leala Francis reveals how she uses conversational AI to spot sentiment shifts before purchasing behavior changes, then adjusts supply forecasts mid-cycle. Her organizational design—consolidating customer happiness, insights, product commercialization, and packaging innovation under one role—creates the speed needed to launch four products without issues while keeping the feedback loop tight enough to catch problems when they're still "in the ones and twos."

The counter-intuitive insight: automation isn't about cost reduction at AG1. It's about relationship depth. By handling routine requests through AI agents that access full customer history and make personalized recommendations, human agents get freed up to send replacement shakers to customers they spot with old ones on social media. Leala's "birthday gift test"—would this interaction show we know the customer well enough to buy them the right gift?—defines whether personalization actually works at scale for a premium subscription brand expanding into retail.

Topics discussed:

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Most enterprise CX leaders assume you need a CRM to manage customer relationships at scale. Aurelia Pollet, Director of Customer Experience at CarParts.com, shares a different perspective. Her team handles 43 million annual customers across 1 million SKUs while currently operating without traditional CRM infrastructure, and she explores how this approach might become more common as AI eliminates the integration complexity that made CRMs necessary.

The core insight: AI can potentially coordinate data across telephony, email, chat, SMS, order management, ERP, and accounting systems without requiring a central hub to force these disparate systems to communicate. This could address the "left hand not talking to right hand" problem that has challenged enterprise customer service operations for decades. Instead of building expensive integrations between systems, AI could act as the intelligent layer that accesses and synthesizes information from multiple sources in real-time.

Aurelia's implementation methodology centers on transcript analysis rather than theoretical workflow design. By examining actual customer service interactions across all channels, her team identifies which requests involve information provision versus complex problem-solving requiring empathy and advisory skills. This data-driven approach enabled them to deploy Spark, their AI assistant with access to the same data as human agents, while preserving human intervention for high-stakes scenarios requiring financial planning or technical troubleshooting expertise.

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What happens when a telecommunications reseller transforms into a cloud contact center pioneer? Paul Jarman, Founder and ex-CEO at NiceInContact navigated six major technology waves, from premise to cloud, single-tenant to multi-tenant, voice-only to omnichannel, and traditional analytics to AI-powered automation.

Paul has learned why most AI implementations fail the "looks good vs. gets used" test. His framework for AI adoption focused first on agent efficiency wins like automated after-call work, then real-time analytics, before attempting full automation.

But Paul's most contrarian insight centers on market consolidation. While everyone debates which layer will dominate — CRM giants like Salesforce, contact center platforms, or AI-native companies — he predicts CRMs will lead through acquisition rather than innovation. His reasoning: they have the market cap and megaphone, but lack the stomach to deploy thousands of developers for multi-year contact center rebuilds.

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What happens when you serve every single customer — from students to enterprise — with the same white-glove experience? At Notion, it created a CX challenge that traditional automation couldn't solve, forcing them to rethink the entire human-AI balance.

Emma Auscher, Global Head of CX, tells Ashish how Notion's "no customer left behind" philosophy led to a counterintuitive discovery: implementing AI increased both automated resolution rates and human agent interactions simultaneously. Rather than typical deflection strategies, they're creating what Emma calls "everyday luxury" experiences.

Topics Discussed:

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