Quick Summary: Adopting AI for customer discovery helps CS teams scale open-ended conversations, especially with long-tail accounts. Get Bob London’s tips on piloting AI conversation agents, asking empathetic questions, capturing stories, and using your insights to guide product, strategy, and ICP.
As a customer leader, you know that two things have always been true about strategic, open-ended customer conversations.
One: they’re incredibly valuable, especially when the customer does 75% of the talking. You get to hear what truly matters: their priorities, their challenges, their ideal outcomes.
Two: they’re virtually impossible to conduct and leverage at scale. When you multiply those hours of preparation, careful listening, note-taking, and analysis by dozens of accounts or more, you instantly outstrip any CS team’s bandwidth.
Until now, that is, says Bob London. The potential of AI for customer discovery conversations has turned Bob, founder of the “Radically Authentic Discovery” method, from an AI skeptic to an AI advocate. In his recent ChurnZero webinar—which you can watch in full below—he explained why, and how, AI is about to change the way you conduct and learn from customer conversations forever, and how to get started.
The transformative power of AI for customer discovery conversations
In 2014, Bob recalls, it took 50 hours to interview 10 customers, synthesize the notes, and deliver insights. Today, with AI note takers and report generators, that time is closer to 11 hours.
The next leap, says Bob, will be AI agents that can handle thousands of conversations without human fatigue or bias.
“What AI is very good at,” he says, “is not getting distracted or multitasking, not having ‘happy ears’, not feeling busy or overwhelmed. It never gets tired. It doesn’t have unconscious bias. Sometimes, AI does a better job than humans—including me—of uncovering opportunities and risks.”
Most customer leaders are already on board—here’s why.
Bob interviewed 22 CS executives in his network to ask how comfortable they’d be—out of 10—with an AI agent running open-ended customer conversations. The result: an average of 8.2 out of 10.
“When you’re in leadership,” says Bob, “you hear every day from the board and C-suite that you need to cover more accounts; you need to add value without adding people. Leaders know they have to look at it.
Every CS leader Bob spoke to pointed to the same starting point: the “long tail” of customers who don’t currently get much human attention.
“The loudest and most consistent answer was: long-tail, low-touch,” he says. “These are customers we’re not talking to at all. If AI can go beyond surveys and have real open-ended conversations with them, that’s a huge opportunity.”
Testing agentic AI tools for customer discovery
Bob tested his idea using a platform called Perspective AI, one of several emerging tools for agentic research, to survey customer leaders on how they feel about using AI agents to get customer feedback through conversational AI”
He used the platform to quickly generate a conversation-ready agent, producing a full flow of questions, previewing and editing the agent’s prompts, and adding context-sensitive follow-up tips to encourage storytelling and surface concrete examples.
Bob refined the flow by adding two mandatory questions:
- Start: “What’s the hardest part of your job?”
- End: “What was your experience like interacting with this AI agent?”
He also instructed the agent to use follow-up questions naturally, not robotically, and gave participants the option of responding by voice, making the experience feel more like a real conversation than a survey. Finally, the platform provided a link for Bob to share.
“Many participants preferred the voice option, describing it as easier and more natural than filling out a survey,” says Bob. “They said it felt more like a real conversation and gave them space. Talking to an AI agent is customer-friendly.”
Remember: garbage in, garbage out.
AI can analyze and connect dots with superhuman speed. But the quality of those insights depends entirely on the quality of the input.
“If you have boring, check-the-box customer conversations, there’s nothing interesting for the AI to study,” says Bob. “But if you ask deeper, empathetic questions—what I call radically authentic discovery—the AI has rich material to synthesize. That’s when you get value.”
The big opportunity: CS strengthens its strategic role
Beyond retention and expansion, Bob sees a broader mandate for CS: to become the company’s ears with voice-of-customer insights that influence the company’s roadmap, positioning, and strategy. AI is a multiplier for this role.
“CS talks to customers all day,” says Bob. “Not just to keep them from churning, but to hear what’s important to them. When you do that—and when you scale those conversations with AI—you become the ear of the vendor. You’re the function that refines the ICP, drives innovation, and detects market shifts. That’s board-level impact.”
Best practices for using AI in customer discovery
For human-led conversations:
Start conversations with big-picture “you” questions about the customer’s business priorities, not your product.
Use silence intentionally—pause and let customers fill the space with more detail, rather than jumping in.
Encourage storytelling and concrete examples so the AI has richer material to analyze.
Capture and synthesize insights from everyday CS conversations, not just formal interviews.
Share themes broadly across your organization: product, sales, and the C-suite need to hear what customers are saying.
For AI agent-led conversations:
Gauge openness within your own leadership team. Ask them to rate their comfort level like Bob did, and surface the results.
Start small by piloting AI with a subset of customers—don’t wait for full organizational buy-in.
Start with low-touch customers, replacing or supplement basic surveys for long-tail accounts with AI-driven conversations.
Set clear expectations by letting customers know upfront when and how they’ll be contacted by an AI agent.
Use AI feedback to detect patterns in the long tail, then decide which insights should be escalated to human-led follow-up.
Share findings from pilots upward; leaders are already expecting AI to help scale coverage.
Position CS not only as the team preventing churn, but as the function fueling innovation with customer intelligence.
Want to learn more from Bob London?
You can catch Bob’s session on improving AI outcomes with deeper customer conversations on day one of ZERO-IN 2025 this October.
Can’t make the trip? Take Bob’s class on how to speak your customers’ love languages instead.
