CX Innovators

Jose Jordan keeps a simple litmus test for automation. Payments and balance checks belong in self-service, while complex, multifaceted calls go to a human who can audit the account and explain what happened. As more gets automated, the calls that remain demand sharper, more knowledgeable agents.Jose, Director of Customer Service at Curacao, tells Rob Dwyer how mirroring a frustrated caller's words almost verbatim softens their tone, why a hands-on training week builds agent confidence, and how explaining the why behind every change earns real buy-in.Topics discussed:Mirroring a caller's words to de-escalate emotionA litmus test for what to automateA hands-on training week before agents hit the floorExplaining the why to build accountability without fearWhy service agents now act as account investigatorsDelegating instead of trying to be an atlasGiving teams room to make mistakes safelyBuilding credit access for underserved retail customersListen to more episodes: Apple Spotify YouTube

Drew Candres built support organizations where a single tweet on a Saturday night could move a market 10% and flood your queues while the rest of the world slept. Now as VP of Customer Experience at GameChanger, he's applying those same instincts to a completely different emotional context: parents trying to capture their kids' first home run. The contrast makes for one of the more grounded and honest conversations about what it actually takes to build CX operations that hold up when things go sideways.One of the sharpest observations in this episode: some of his highest CSAT scores, across every company he's worked for, came on outage days. When handled well, those days produced scores around 50% above the average. His argument is that customers already know things break. How you handle it is the only thing they're actually judging.Topics Discussed:Why containment rate is a meaningless metric and what it actually measuresManual support as the most expensive technical debt a scaling startup can carryThe two-step framework for deciding what to automate and what to fix firstRate of change in inbound volume as an early warning system no platform has fully solvedWhy data alone doesn't move product teams, and the storytelling structure that doesEmbedding proactive support into the build process through phased rollouts and power user cohortsWhy CX foundation work must come before executive headcount, not afterWhy emotional connection becomes the primary differentiator as AI compresses product advantages

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

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

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.

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.