Expert Mode: Fixing the Most Broken Part of CX—How AI Is Reinventing Customer Onboarding
This article was based on the interview with Featuring insights from Srikrishnan Ganesan, CEO and Co-Founder of Rocketlane by Greg Kihlström, AI and MarTech keynote speaker for The B2B Agility with Greg Kihlström podcast. Listen to the original episode here:
Customer onboarding is often the weakest link in the customer experience chain. It’s where excitement gives way to confusion, where expectations crash into execution, and where even the best sales can quietly slip into churn. Srikrishnan Ganesan, CEO of Rocketlane, believes onboarding is more than just a handoff—it’s the second sale. And how that second sale plays out often determines whether a customer sticks around or starts shopping for alternatives.
In this conversation, Ganesan unpacks why onboarding and implementation are still so inefficient, how AI and automation are transforming the work beneath the surface, and why speed alone isn’t enough—it’s about scaling trust.
The Second Sale: Onboarding Is Where Customers Decide to Stay
Ganesan’s perspective on onboarding is refreshingly blunt: closing the deal is just the first sale. Onboarding? That’s the second—and arguably more important—one.
“You’re not selling the product anymore. You’re selling what it feels like to work with your company,” he says
Too many organizations still treat onboarding as a box-checking exercise, relying on spreadsheets or generic project management tools. But according to Ganesan, this phase is where customers form their long-term opinions. If the experience feels chaotic or inconsistent, the damage is done—regardless of how strong the product is.
Rocketlane was built to tackle that problem, starting with customer onboarding and expanding into broader project-based service delivery. The goal? To make implementation not just faster, but more predictable, repeatable, and transparent.
AI and Automation: Building an Onboarding Co-Pilot
AI in onboarding isn’t just about reducing busywork—it’s about spotting risk before it becomes visible. That includes budget overruns, timeline slippage, scope creep, and even customer dissatisfaction. And much of this goes unreported, because teams naturally want to solve problems quietly.
“People try to be the hero and solve the problem on their own. But if a system flags issues early, leadership can intervene before it’s too late,” Ganesan explains
Rocketlane’s platform uses AI to surface these early warning signs and automate nudges and follow-ups. But the most transformative application, Ganesan argues, is their AI scribe. It listens to customer calls—requirements meetings, solutioning sessions, go-live discussions—and auto-generates tailored outputs: documents, emails, and next steps.
The key insight? Not all meetings produce the same kind of outcomes. By templating AI outputs based on meeting type, Rocketlane saves teams from repetitive tasks and gives them more time to focus on strategic problem-solving.
Human-in-the-Loop Isn’t Optional—It’s the Whole Point
For all the talk of AI replacing jobs, Ganesan sees a different dynamic: AI extends what humans can do, but it needs to be taught, checked, and guided.
“The human brings the deep context and verifies what the AI delivers. Then they iterate, teach the system, and automate more of the work over time,” he says
That cycle—teach, automate, verify, improve—is what creates what Ganesan calls “radical efficiency.” By shrinking the timeline of work, teams don’t just save time—they create capacity to do more projects, faster, for more customers.
And the payoff isn’t theoretical. Shorter timelines reduce perceived risk for new clients, which increases deal velocity and ups the appetite for implementation services. In other words: efficiency breeds demand.
The Real Barrier? Thinking Too Small
If there’s a recurring problem Ganesan sees in companies trying to adopt AI, it’s a lack of ambition.
“Too many teams focus on the low-hanging fruit. That’s a good start, but you won’t get to greatness with small, safe bets,” he warns
Instead, he advises leaders to aim higher: automate 70% of the onboarding workload. Assign ownership of AI experimentation to someone in the org. Carve out time and space for innovation—even if it means hiring or borrowing talent.
Leaders also need to let go of legacy tech mandates that constrain experimentation. Ganesan emphasizes the importance of flexibility: don’t lock your onboarding teams into systems that weren’t built for their workflows. The tools need to fit the job—not the other way around.
Measuring What Matters: Time Saved, Scope Gained
When it comes to ROI, Ganesan breaks it down into three buckets:
- Time saved – How much effort was reduced through automation?
- Timeline shrinkage – How much faster are projects going live?
- Scope expansion – What new work can teams do now that they couldn’t before?
“If AI lets you deliver governance for 100 projects instead of just the top five, what’s the impact on delivery speed and customer satisfaction?” he asks
It’s a shift from cost-cutting to value creation—how much more impact can you deliver with the same (or fewer) resources?
Conclusion
Customer onboarding is no longer a tactical step—it’s a brand-defining moment. And as Srikrishnan Ganesan makes clear, AI isn’t just streamlining the experience. It’s turning it into a competitive advantage.
The companies that win won’t be the ones who implement faster. They’ll be the ones who build deeper partnerships, surface issues before they escalate, and teach their systems to work as hard as their teams do.
Because onboarding isn’t just how you start the relationship.
It’s how you prove your customer made the right choice.
