Building an AI-ready data foundation

As someone who’s spent my career at the intersection of analytics, data strategy, and customer success, I’ve witnessed firsthand how data transforms businesses. Today at RudderStack, where I lead client services, I’m more convinced than ever that data intelligence is becoming the defining competitive advantage in SaaS (and beyond).

I love leading this function for many reasons, but especially because it requires a strong approach to data strategy. Our team sits closest to the customer, engages across the entire lifecycle, and captures the richest, most diverse set of signals: product usage, support pain, business goals, sentiment shifts, and more. With the right data foundation, CS becomes the ideal proving ground for AI--and should be at the forefront of every company’s AI strategy.

Multi-channel data: the missing piece in customer understanding

Most companies today operate with fragmented customer insights. Product usage data lives in one system, support tickets in another, sales conversations in a CRM, and customer communications are typically scattered across email, Slack, and meeting transcripts.

This fragmentation creates critical blind spots in understanding customer behavior across your organization. Your product team analyzes usage metrics, but remains oblivious to frustrations expressed in support tickets. Your Engagement Managers capture insights from their champions, but miss vital context about emerging business initiatives their customers are launching. The result is a series of decisions based on incomplete information and countless missed opportunities to deliver meaningful value.

The companies winning today are those that have broken down these silos to create a holistic view of customer behavior and sentiment across all touch points. This is true regardless of industry.

Building a true customer 360

At RudderStack, we help companies build clean and robust data foundations, and I’m applying those same principles to my own post-sale operations. I’ve tried several tools like Salesforce, Gainsight, Planhat, all of which do help create a holistic view of the customer. But these platforms only perform as well as the data powering them.

That’s why we’re building something different: a comprehensive customer data view within Snowflake that integrates engagement signals across every touchpoint and channel.

This unified foundation brings together:

Product usage patterns and feature adoption metrics
Support interactions
Meeting transcripts
Customer metadata and firmographics

Simply having this data available at the customer and individual level is a game changer. But what good is data if no one can interact with it?

The impending efficiency revolution

The true transformation for IC’s (and leadership) happens when AI meets RudderStack's unified data foundation. Here’s what I’m building toward:

Customer summaries: Imagine walking into every customer interaction with real-time, AI-generated briefs that synthesize product usage, support history, sentiment, and past conversations.
Next best use case recommendations: AI will analyze adoption patterns across similar customers to recommend high-impact features each customer should explore next, based on their current implementation.
Proactive risk detection: With signals unified, AI can continuously monitor for subtle churn indicators like declining engagement, rising support requests, and missed onboarding milestones.
Smart account prioritization: Each week, the system will suggest which accounts need focus based on renewal timing, sentiment shifts, product adoption signals, and business context .

This vision will take time to achieve, but will enable the team to operate at a fundamentally higher level, focusing their time on strategic guidance instead of task mastering. And crucially, it requires the right foundation of three essential building blocks: