AI CX Innovators

QuinStreet’s Tyler Orrell on Challenging “Upper Right Quadrant” Thinking

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.
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Carta’s David DeMarco on Preserving Human Touch for High-Stakes Financial CX

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.

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.
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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.