AI CX Innovators

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Join us as we bring together enterprise CX leaders and innovators to discuss how AI is reshaping the future of CX, explore emerging opportunities, and share insights on where the industry is headed.

  1. From 40-minute wait times to under 60 seconds without adding headcount | Allan Harari

    5. FEB.

    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  Spotify  YouTube

    27 Min.
  2. Case age over handle time: The metric that actually improved NPS, CSAT, and customer spend | Zach Greco

    22. JAN.

    Case age over handle time: The metric that actually improved NPS, CSAT, and customer spend | Zach Greco

    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 Listen to more episodes:  Apple  Spotify  YouTube

    46 Min.
  3. The 3-action formula that predicts above-average customer retention | David Melendez

    04.12.2025

    The 3-action formula that predicts above-average customer retention | David Melendez

    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. Topics Discussed: Process mapping onboarding to separate access provisioning from value delivery outcomes Alteryx's three-action renewal correlation framework applied to new context Vendor AI capability inventory before building or buying new tooling Instrumenting CSM roles in Gainsight before deploying Atlas and agent features Service consultant workflow automation as first AI deployment target Hiring for specific learning goals rather than general curiosity claims Automating yourself out of the same job annually as ops team standard Strategic thinking and business partnership as the non-automatable skill layer Listen to more episodes:  Apple  Spotify  YouTube

    27 Min.
  4. S&S Activewear’s Laura Turner on Technology Adoption without Perfect Data Infrastructure

    20.11.2025

    S&S Activewear’s Laura Turner on Technology Adoption without Perfect Data Infrastructure

    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: How customer self-selection proved more effective than AI-driven segmentation when implementing conversational AI across a diverse B2B customer base. The operational complexity of maintaining next-day shipping SLAs for 90% of the continental US while processing millions of garments annually. Using Integration Pulse surveys to track sentiment through major system transitions and quickly address feature gaps during mergers. Why employee experience forms the foundation of customer experience, including proper training, tools, and process enablement. The practical approach to AI readiness that focuses on identifying manageable friction points rather than waiting for perfect data infrastructure. Why every AI investment must tie directly to cost reduction or revenue increase rather than deploying technology for its own sake. How AI functions as a task replacement rather than workforce replacement, elevating human work to focus on complex problem-solving requiring judgment. Implementing CX governance strategies across newly merged organizations through representatives who champion customer-centricity. Building customer-centric culture by connecting every employee role to customer impact, from warehouse operations to finance teams. Listen to more episodes:  Apple  Spotify  YouTube

    33 Min.
  5. The birthday gift test: How to measure if your AI personalization actually knows customers

    06.11.2025

    The birthday gift test: How to measure if your AI personalization actually knows customers

    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: Consolidating customer happiness, insights, commercialization, and packaging under one leader to enable four-for-four product launches Reading sentiment on new flavors to increase forecasts before consumption behavior manifests in sales data Tracking "my wife and I" language patterns as leading indicators of household penetration and organic growth Applying the "birthday gift test" to measure whether AI personalization demonstrates genuine customer knowledge Monitoring conversations for customers mentioning travel to proactively shift them to travel packs before they pause Catching product and experience issues "in the ones and twos" using AI before they reach statistical significance Coaching teams to translate customer observations into strategic points of view that win cross-functional alignment Reframing AI adoption internally as unlocking "career-defining" strategic work rather than replacing reporting tasks Listen to more episodes:  Apple  Spotify  YouTube

    30 Min.
  6. How CarParts.com Serves 43 Million Customers: Why Traditional System Integration May Become Obsolete

    03.10.2025

    How CarParts.com Serves 43 Million Customers: Why Traditional System Integration May Become Obsolete

    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. Topics Discussed:  Current customer service architecture serving 43 million annual customers across 1 million SKUs without CRM systems AI coordination exploring alternatives to traditional system integrations between telephony, email, chat, SMS, and ERP systems  Call transcript analysis methodology for mapping information-provision versus empathy-required customer interactions Spark AI deployment with agent-level data access for 24/7 order tracking and account management Dynamic journey mapping replacing static customer experience documentation with real-time touchpoint visualization Cross-functional collaboration framework applying customer service methodologies to internal team management Strategic project prioritization balancing customer value delivery with quarterly company financial objectives AI guardrail implementation after unauthorized order cancellation and refund attempts by autonomous agents Listen to more episodes:  Apple  Spotify  YouTube

    28 Min.
  7. NICE inContact’s Founder and ex-CEO Paul Jarman on Micro-Understanding for Technology Leadership

    18.09.2025

    NICE inContact’s Founder and ex-CEO Paul Jarman on Micro-Understanding for Technology Leadership

    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. Topics Discussed: AI evaluation framework distinguishing between solutions that "look good" versus systems customers actually deploy and use Vendor assessment process for self-service companies revealing enterprise readiness gaps and integration challenges CRM consolidation prediction through acquisition rather than internal innovation due to development resource requirements Agent efficiency automation starting with after-call work and real-time analytics before attempting full workflow replacement Market valuation challenges for CCaaS companies facing decelerated growth and investor uncertainty about competitive threats BPO transformation difficulties using railroad-to-airline analogy explaining why service providers struggle becoming AI innovators Bank branch automation parallel predicting agent role evolution with routine task automation but persistent human judgment needs Listen to more episodes:  Apple  Spotify  YouTube

    49 Min.
  8. Notion's Emma Auscher on Creating "Everyday Luxury" AI Experiences

    14.08.2025

    Notion's Emma Auscher on Creating "Everyday Luxury" AI Experiences

    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: Transforming CX from reactive cost center to proactive innovation leader using AI-driven behavioral data and engagement analytics. Implementing "no customer left behind" support philosophy that serves students and enterprise clients with equal white-glove treatment. Building Voice of Customer programs that span sales, success, product, and research teams with cross-functional data integration. Balancing human-AI interactions where both automated resolution and human agent engagement increase simultaneously through strategic task allocation. Creating "everyday luxury" AI experiences that prioritize customer value enhancement over traditional deflection metrics and cost reduction. Managing global CX operations across 80% international user base using regional hubs with culturally-adapted strategies and multilingual teams. Developing knowledge management systems as foundational AI use case requiring dedicated content creation and documentation roles. Building CX career progression paths that upskill support agents into product management, engineering, and strategic operations roles. Listen to more episodes:  Apple  Spotify  YouTube

    28 Min.
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Join us as we bring together enterprise CX leaders and innovators to discuss how AI is reshaping the future of CX, explore emerging opportunities, and share insights on where the industry is headed.

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