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

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

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

Shantanu Das, General Manager & Global Head of Customer Service, Primary Sales, and Scam Prevention at Wayfair, manages one of the largest customer service operations in e-commerce — a 3,000-person global team at Wayfair — and his approach to AI implementation challenges conventional wisdom about automation versus human agents. His methodology starts with three distinct value creation modes: personal productivity improvement, real-time insight generation, and complete workflow reimagination.  The breakthrough insight, he tells Ashish, came when he challenged his team to design systems as if no humans were involved, then strategically layered human expertise back into the process. This approach led to fundamental changes in how Wayfair delivers customer coaching, moving from monthly performance reviews to real-time feedback that happens in the moment of interaction. Topics Discussed: The three-pillar framework for AI value creation: personal productivity, real-time insight generation, and complete workflow reimagination rather than incremental improvements. How reimagining coaching workflows without humans in the process led to real-time feedback systems that replaced traditional monthly performance reviews. The spectrum approach to balancing automation and human agents based on customer preference and complexity rather than forcing channel adoption. Building "genius agents" who leverage AI to perform the work of three people while maintaining personalized customer experiences and human judgment. The evolution toward agentic AI systems where specialized agents handle different business functions and communicate with each other to enhance outcomes. Why continuous learning and rapid experimentation matter more than waiting for perfect system integration when implementing enterprise AI initiatives. Choosing technology partners based on willingness to innovate and adapt rather than just technological capabilities or established market presence. Navigating build-versus-buy decisions in tech-first organizations through joint business and technology team evaluation of market solutions versus internal development. The six high-impact areas for AI application in customer experience: 360-degree customer feedback, real-time agent assistance, conversational virtual assistants, high-ROI solution optimization, workforce management, and quality coaching. Listen to more episodes:  Apple  Spotify  YouTube

Managing millions of daily customer interactions at AT&T, Head of Retail Technology, Contact Center Platforms, GenAI Product & Engineering Deepak Sharma, has learned that successful AI transformation requires building AI-ready infrastructure before chasing AI features. His dual-lane framework separates quick wins like agent assist and call summarization from foundational data pipeline work that enables sophisticated AI at enterprise scale. His most compelling example, he tells Ashish, involves digital avatars that create three-way interactions between customers, human agents, and AI, delivering experiences customers actually prefer over traditional service. Successful AI adoption happens when solutions are simple enough to feel like magic rather than technology requiring extensive training. Topics Discussed: The infrastructure requirements for creating truly omnichannel customer experiences that work across retail stores, contact centers, and digital channels at enterprise scale. A dual-lane approach to AI transformation that separates quick wins like agent assist and call summarization from foundational data pipeline and orchestration work. Digital avatar implementations that enable three-way interactions between customers, human agents, and AI to create superior customer experiences. Prioritization frameworks for managing thousands of AI use cases across large enterprises while balancing feasibility, time to market, and business impact. The critical role of expectation management and stakeholder alignment in AI transformation, treating it as business process transformation rather than technology implementation. Change management strategies that work at scale, including making AI solutions simple enough that extensive training programs become unnecessary. Why AI should be invisible in successful implementations, embedded seamlessly into existing workflows rather than presented as separate AI-powered features. The importance of understanding frontline worker needs by directly observing contact center and retail store operations rather than making assumptions about problem-solving. Listen to more episodes:  Apple  Spotify  YouTube

The gap between AI promise and contact center reality is often measured in months of failed adoption and frustrated executives. Tyler Orrell, VP of Contact Center Operations at QuinStreet, tells Ashish how they developed a surgical approach to AI that focuses on business impact over technological sophistication. His framework for identifying the 6-7 behaviors that actually drive outcomes, rather than automating entire QA processes, offers a masterclass in strategic AI implementation.  Tyler's contrarian vendor selection advice — never use the vendor's RFP form and resist "upper right quadrant" safe choices — challenges conventional procurement wisdom. His insight that insurance agents function as simultaneous consultants, salespeople, troubleshooters, and empathizers within single conversations explains why AI replacement timelines are more complex than most predictions suggest. Topics Discussed: The evolution of contact center agent roles from single-function responders to multi-faceted consultants, salespeople, troubleshooters, and empathizers, and why this complexity affects AI replacement timelines. Strategic AI adoption frameworks that focus on surgical implementation of specific business-driving behaviors rather than comprehensive automation of existing processes. Advanced auto-QA methodologies that score 100% of interactions while maintaining agent trust through accurate transcription and scoring that agents can verify and understand. ROI measurement discipline for AI tools, including the challenge of maintaining visibility into improvements after initial implementation and the importance of continuous optimization cycles. Executive communication strategies for AI initiatives that emphasize business impact over technological features, focusing on speed-to-competency for agents and real-time coaching capabilities. Vendor selection frameworks that prioritize objective RFP processes testing specific business unit needs over sales presentations, with considerations for risk tolerance between established and disruptive technologies. Quality assurance transformation from traditional 8-15 calls per month scoring to comprehensive conversation intelligence that enables within-hour coaching and process corrections. Implementation best practices for AI tools that require organizational buy-in from both executive leadership and front-line agents, with emphasis on communication and change management processes. Listen to more episodes:  Apple  Spotify  YouTube

As AI automation grows in customer experience, the most forward-thinking organizations aren’t replacing humans, they’re redefining how humans and AI work together. In this insightful conversation with David DeMarco, SVP of Business Technology at Carta, on AI CX Innovators, Ashish explores why increased automation actually makes quality assurance more crucial and how ”white space mining” can uncover the 20% of issues driving 80% of CX improvements. David also shares Carta’s strategic approach to channel selection, giving customers choice in how they engage while reserving human expertise for complex equity and valuation discussions. He also details their innovative AI workers program that’s transforming coaching and sentiment analysis without complex rubrics—simply uploading a document with expectations generates comprehensive coaching plans across agent interactions. Topics Discussed: - The counterintuitive relationship between automation and quality assurance, where increasing AI implementation actually makes QA more essential for ensuring accurate responses and uncovering valuable voice of customer insights rather than diminishing its importance. - Implementing human-in-the-loop strategies for critical financial conversations to maintain oversight in high-value interactions where errors could have significant consequences, while allowing automation to handle straightforward inquiries. - Mining the white space in conversational data through automated concern mining to extract insights from the majority of customer interactions that receive no formal reviews, identifying patterns that drive 80% of CX improvements. - Translating conversational intelligence into product roadmap priorities by contextualizing data for product teams with supporting evidence that demonstrates the significance of customer pain points requiring development attention. - The three-part framework for CX leadership success in the AI era that begins with data literacy to understand patterns, develops storytelling skills to gain cross-functional buy-in, and builds change management expertise to implement effective solutions. - Strategic channel selection methodology that empowers customers to choose their preferred support avenues while purposefully reserving human touchpoints for complex financial conversations requiring trust and consultation. - Leveraging ongoing vendor dialogues as an innovation catalyst, continuously exploring new technologies to assimilate ideas and identify emerging solutions even before purchasing decisions are made. - Implementing specialized AI workers for CX functions including a support coach that automates coaching with no formal rubric required, and a sentiment insights worker that performs multi-step analysis on conversational data. - Creating document-based coaching automation that eliminates complex scoring frameworks by allowing teams to simply upload expectations documents that AI transforms into comprehensive coaching plans across agent interactions.

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

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.