AI Is Already Resolving 90% of Customer Service Tickets - and It's Getting Smarter | Shashi Upadhyay, Zendesk
Episode
57 min
Read time
2 min
Topics
Fundraising & VC, Artificial Intelligence, Software Development
AI-Generated Summary
Key Takeaways
- ✓Automation rate benchmarks: Zendesk's best-performing customers achieve 70–90% AI resolution rates today. High-volume transactional businesses like e-commerce reach into the 90s quickly, while complex B2B scenarios still land at 30–40%. The baseline jumped from 10–20% to ~50% the moment reasoning models capable of multi-step action replaced simple chatbot search systems.
- ✓Resolution learning loop mechanics: When an AI agent solves a ticket, that successful trace gets converted into a reusable deterministic automation, bypassing LLM reasoning entirely on repeat queries. When it fails, the system studies the human agent's subsequent actions, runs simulations, and updates procedures via A/B testing—creating compounding improvement without manual retraining intervention.
- ✓Deterministic guardrails for enterprise reliability: LLM creativity must be constrained once customer intent is identified. For a return or refund workflow, Zendesk routes to deterministic code the moment the issue type is confirmed—e.g., refunds under $50 auto-approve, over $50 escalate to human. This architecture prevents the class of AI errors seen in publicized enterprise failures.
- ✓Forethought's auto-procedure generation accelerates go-live: Rather than requiring customers to manually author prompts and workflows, Forethought's system reads the prior year of support tickets, classifies them, and auto-generates step-by-step resolution procedures. This compresses implementation timelines from months to days and surfaces institutional knowledge that organizations have never formally documented anywhere.
- ✓Pricing model shift signals market direction: Zendesk has moved from seat-based SaaS pricing to resolution-based pricing—charging only when a customer issue is actually resolved. This model, adopted faster than larger incumbents, aligns vendor incentives directly with outcomes and serves as a practical signal for enterprises evaluating AI service vendors on accountability rather than access fees.
What It Covers
Zendesk's Head of Product and Engineering Shashi Upadhyay explains how agentic AI now resolves 70–90% of customer service tickets, how the company's resolution learning loop enables self-improving agents, and why adoption barriers—not technology limits—determine how fast this transformation reaches consumers globally.
Key Questions Answered
- •Automation rate benchmarks: Zendesk's best-performing customers achieve 70–90% AI resolution rates today. High-volume transactional businesses like e-commerce reach into the 90s quickly, while complex B2B scenarios still land at 30–40%. The baseline jumped from 10–20% to ~50% the moment reasoning models capable of multi-step action replaced simple chatbot search systems.
- •Resolution learning loop mechanics: When an AI agent solves a ticket, that successful trace gets converted into a reusable deterministic automation, bypassing LLM reasoning entirely on repeat queries. When it fails, the system studies the human agent's subsequent actions, runs simulations, and updates procedures via A/B testing—creating compounding improvement without manual retraining intervention.
- •Deterministic guardrails for enterprise reliability: LLM creativity must be constrained once customer intent is identified. For a return or refund workflow, Zendesk routes to deterministic code the moment the issue type is confirmed—e.g., refunds under $50 auto-approve, over $50 escalate to human. This architecture prevents the class of AI errors seen in publicized enterprise failures.
- •Forethought's auto-procedure generation accelerates go-live: Rather than requiring customers to manually author prompts and workflows, Forethought's system reads the prior year of support tickets, classifies them, and auto-generates step-by-step resolution procedures. This compresses implementation timelines from months to days and surfaces institutional knowledge that organizations have never formally documented anywhere.
- •Pricing model shift signals market direction: Zendesk has moved from seat-based SaaS pricing to resolution-based pricing—charging only when a customer issue is actually resolved. This model, adopted faster than larger incumbents, aligns vendor incentives directly with outcomes and serves as a practical signal for enterprises evaluating AI service vendors on accountability rather than access fees.
Notable Moment
Upadhyay estimates the global market is only about 5% through the AI customer service transition—despite headline automation rates of 90%. He attributes the gap not to technology limits but to change management, purchasing cycles, and implementation timelines, which he describes as improving only incrementally, never by an order of magnitude.
Episode Transcript
From the public's point of view, this can't happen fast enough. EPO was a revolution. Like, this drudgery that you can outsource to lower cost markets with higher unemployment. But now is that kind of come back to the enterprise because you don't need those big call centers any longer. I really do think that there's a golden age of service ahead of us. I'm very hopeful that'll happen. 90% of problems solve by AI and solve instantaneously anytime of the day. The pace of technology is far, far exceeding the pace of adoption. And the pace of adoption is gated so much more by people working together, how companies run their processes, just how the purchasing cycle works, how the implementation cycle works. Those are, like, laws of physics. They only improve by a factor of 10, never. Can we start by having you introduce yourself? So I'm Shashi Upadhyay. I'm the, I run product and engineering, including AI at Zendesk. Been here for fifteen months. Prior to this, I was, a part of the Google Ads organization, the Ads product organization. I was running all the enterprise, ads products there, for several years. And prior to that, I started a company called Lattice Engines, which was a pioneer in using machine learning and AI for, CRM use cases, so marketing and sales primarily. And that company had been acquired right down and Bradstreet and then taken public in 2019. So I've spent, like, last twenty years of my career on, machine learning and AI as applied to, CRM problems, different stages of the customer journey. And before that, I was trained as a physicist, and I've been a management consultant for a few years. Let's talk about the acquisition. You guys first of all, I think most digital knowledge workers, who are likely, my audience, know who Zendesk is. And you've recently acquired an agentic platform or agentic AI company called Forethought. And you've talked about, how Forethought brings self improving AI into the Zendesk portfolio. I'm most interested in what you mean by self improving. But before we get to that, explain what forethought does, why Zendesk acquired it, how it fits into Zendesk's offerings. Yeah. So let's start from, like, and I'll kinda remind ourselves situate ourselves a little bit in what's happening in the world of service. Right? So, Zendesk was, founded, you know, almost, I think, seventeen years ago, originally as a software as a service company focusing on the customer service problem. And the big innovation that Zendesk made at the time was mainly around, usability. So, like, how do you how do you sort of launch a system very easily that's beautiful, you know, very pleasing to the eyes, and and and a place where, agents human agents would want to spend their whole day in. Right? Hence, the name Zendesk, and and it built on that or a very successful run through public and through COVID. We went, private in 2022, …
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“Zendesk's Head of Product and Engineering Shashi Upadhyay explains how agentic AI now resolves 70–90% of customer service tickets, how the company's resolution learning loop enables self-improving agents.”
“Forethought's auto-procedure generation accelerates go-live: Rather than requiring customers to manually author prompts and workflows, Forethought's system reads the prior year of support tickets, classifies them, and auto-generates step-by-step resolution procedures.”
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