Name: Mudit Garg | AI for Go-to-Market Festival | San Francisco
Start: 2027-01-21T07:00:00.000Z
End: 2027-01-21T07:00:00.000Z
Location: TBA

Speakers Agenda Why attend Partners Venue

Locations ▼

Get invited

Request to partner

Call to action

Your text goes here. Insert your content, thoughts, or information in this space.

Button

Back to speakers

Mudit

Garg

Head of Customers and GTM Operations

Alation

Mudit Garg is a go-to-market operator with nearly 20 years of experience building and scaling enterprise software businesses. He has led sales, marketing, customer success, partnerships, and global operating functions across high-growth startups and established technology companies, including Alation, Yellow.ai, McAfee, 8x8, SAP, Deloitte, and D2L. Mudit has helped organizations drive growth through stronger commercial execution, operational discipline, and customer value realization - including contributing to D2L’s IPO journey and supporting a successful acquisition. At Alation, he works at the intersection of AI, data, and revenue operations, focused on turning AI capabilities into practical go-to-market motions, measurable customer outcomes, and durable adoption. He holds an MBA from Carnegie Mellon University and an engineering degree from NIT Surathkal

Button

21 January 2026 11:15 - 12:00

Panel: Clean CRM data is no longer enough for agents that need context to make good decisions

When an AI tool gives a bad answer, the instinct is to blame the model or the vendor. More often the culprit is sitting in the CRM, in the form of duplicate accounts, missing fields, conflicting definitions and data spread across systems that were never designed to talk to each other. This panel gets into the unglamorous work that makes AI useful, from cleaning and connecting siloed data to deciding which sources to trust. Expect real examples from organisations of very different shapes and sizes, and frank views on what to fix first when you can't fix everything at once. Key takeaways: - The most common data problems that undermine AI tools, and how to diagnose them - How to prioritise data fixes that will have the biggest impact on AI results - Practical ways to connect siloed data for better planning, segmentation and prioritisation