Rebuilding Customer Success for the AI Era: Lessons from a VP of Customer Success A conversation with Chad Gorman. Executive Summary This article examines how customer success leaders should rethink AI adoption, using insights from an in-depth conversation with Chad Gorman, VP of Customer Solutions and Success (North America) at LivePerson, on the Revenue Lounge Podcast hosted by Randy Likas. Rather than focusing on automation or AI features, Gorman argues that AI-ready customer success is fundamentally about visibility, data discipline, and relationship intelligence. Readers will learn: Why most AI initiatives in customer success fail before deployment  How unifying fragmented CS data is a prerequisite for AI or automation What an effective early warning system looks like  Why engagement and relationship depth are stronger leading indicators than product usage alone How AI can expose relationship “white space” across complex buying committees Where buy vs build decisions actually differ across enterprise and mid-market segments How to embed AI into CSM workflows over-relying on automation Which metrics matter when measuring AI’s impact on retention, risk, and productivity Why the real promise of AI in customer success is reclaimed time for strategic customer work Facebook Twitter Youtube From Call Centers to Customer Outcomes Gorman’s perspective is shaped by an unusual career arc. He started in contact center operations, moved into IT at DirecTV, and then crossed over to the vendor side after a colleague recruited him to Splunk. “I didn’t even know what a CSM was,” he admits. “But once I saw how customer success could be built as a scalable engine, I was hooked.” — Chad Gorman From Splunk, he went on to lead global cloud customer success at VMware, before joining LivePerson, where he now oversees customer success and professional services across North America. That mix of operator, builder, and enterprise leader shows up in how he thinks about AI. Practical. Outcome-driven. Skeptical of hype. ​​AI Adoption Fails Before Deployment Most AI initiatives stumble long before a model is ever deployed. According to Gorman, the real friction points show up earlier in the buying and approval cycle. The Hidden Gates to AI Adoption Governance reviews and AI councils Legal, compliance, and security documentation Industry-specific scrutiny, especially in financial services Undefined success metrics “You can sell software all day long. But if you are not there to shepherd customers through governance, compliance, and approval gates, adoption will stall.” — Chad Gorman Customer Success Has Become Revenue Insurance In volatile markets, customer success is no longer a post-sale support function. It is a revenue protection layer. That shift forces CS leaders to answer harder questions: Where is risk building right now? Which accounts look healthy but are quietly disengaging? Where is expansion hiding in plain sight? The answer, Gorman says, is an early warning system built on stitched data. https://youtu.be/sDdV747jBJA?si=fVE8O2bqTcf2yTeN The Anatomy of an Early Warning System Gorman is blunt about the prerequisite. “Data is non-negotiable. Full stop.” — Chad Gorman Before AI enters the picture, organizations must understand what their book of business actually looks like. Engagement Is the Most Underrated Risk Signal Product usage is table stakes. Engagement is the differentiator. Gorman emphasizes that many churn events are preceded not by usage decline, but by relationship decay. “If engagement drops and you do not notice, you end up ghosted and surprised later.” — Chad Gorman What Engagement Actually Means Engagement is not email volume or meeting counts alone. It is relationship depth across the buying group. Who shows up to meetings? Who stopped showing up? Which roles are missing entirely? Who influences decisions but never engages directly? This is where Gorman believes AI has its most immediate impact. Relationship Intelligence: Where Art Meets Science Gorman describes relationship intelligence as the intersection of human judgment and system-derived insight. “We think we know our accounts. AI shows us the white space we missed.” — Chad Gorman AI-Assisted Relationship Mapping AI can analyze: Calendar data and meeting attendance Email and collaboration patterns Role changes and stakeholder turnover Sentiment from meeting notes and transcripts At LivePerson, Gorman’s team increasingly relies on workspace-level intelligence using Google Gemini to surface patterns across meetings, documents, and communications. You can literally ask, ‘Who used to attend and no longer does?’ and get an answer.” — Chad Gorman Buy vs Build Is No Longer Binary Enterprise customers increasingly want flexibility. Some bring their own LLMs. Others rely on vendor-provided AI. Most land somewhere in between. Enterprise: Build and bring your own models Upper mid-market: Hybrid Down-market: Out-of-the-box AI The common denominator remains the same: clean, structured, accessible data. Embedding AI Into CSM Workflows Even the best insights fail if CSMs do not trust them. Gorman stresses three adoption levers: Data transparency Always link insights back to source systems. Prescriptive guidance Do not just flag risk. Recommend next steps. Respect experienceAI should augment gut instinct, not override it. Measuring AI Impact in Customer Success AI success is not measured by novelty. It is measured by outcomes. What’s Next: Agentic AI and Time Reclaimed The next wave, according to Gorman, is not better summaries. It is execution. The thing CSMs hate most is admin. AI agents that actually do the work change everything.” — Chad Gorman Examples include: Auto-generated QBRs with live data Scheduled reporting without manual pulls Automated follow-ups and task execution The payoff is not speed. It is reclaimed time for strategic customer work. Leadership Lessons From the Field When asked what advice he would give his younger self, Gorman’s answer is simple. “Know your book. Be curious. Admit what you do not know.”” — Chad Gorman Growth mindset Curiosity within and beyond the “box” Meticulous organization Executive presence Tight partnership with the AE  Want to hear more stories from revenue leaders? Subscribe to The Revenue Lounge podcast to never miss an episode! More Resources