Anshul Gupta is the co-founder of Actively, a GTM superintelligence platform for revenue teams. After diving into AI research at Stanford during the early OpenAI days, Anshul decided to take his work and apply it to helping sales and marketing teams be more effective. He argues that while tools like Cursor and Claude Code have materially increased coding efficiency, go to market is lagging behind because of the nuance and complexity behind each business.
Actively serves as the brain and connective tissue that handles all of the underlying data that sales and marketing teams generate. They use first, second and third party data to create a single context window for each account that businesses can use to prioritize accounts, create relevant messaging and more.
In this conversation, we talk about the Actively thesis, which has important implications on how businesses should think about integrating AI into their GTM motion. We cover everything from how to philosophically structure the “brain” behind your GTM engine to data hygiene and the right GTM structure for AI transformation.
In this podcast, we discuss:
The concept of “GTM Super Intelligence” and why it matters
The “horseless carriage” problem: Why simply adding AI to legacy systems isn’t enough
Cognitive architecture: How and why to build systems that mirror the best sales reps’ processes
Why treating the account as a unit to track, store, and iterate on context creates a winning formula
Data hygiene and why “garbage in, garbage out” is a defeatist mentality
The evolving role of humans in go to market
The right org ownership & structure for AI transformation
Episode highlights:
Failure cases of adopting AI in GTM include trying to tack on AI to legacy systems (horseless carriage), using exclusively logic-based intent providers, and not investing in an iterative system.
First ask, “if we had one AE per account, how would the AE approach their role?” and then back into creating a system that gets as close as possible.
There’s no one-size fits all account prioritization or messaging framework. There are materially different, but equally viable ways to do sales and marketing into your top accounts (e.g. going bottoms up vs. tops down).
The garbage in, garbage out mentality is overly defeatist - if your reps have to deal with the data every day, then there’s no doubt layering in AI can improve your data’s impact.
The companies finding the most success with AI transformation in GTM are bringing the top internal representatives together across RevOps, AI, and SDR + marketing.
Anshul and Actively use poor cold outbound campaigns into their business as a signal and trigger to do their own outbound campaigns.
Where to find Anshul
Transcript details
(00:00) Introduction
(03:21) Anshul’s background and the founding of Actively
(06:20) The Actively thesis & product
(10:51) The failure cases that come from the smoke & mirrors AI GTM tools
(17:52) Buy vs. build when thinking through account prioritization and custom messaging
(23:44) Cognitive architecture
(27:50) How Anshul thinks about the next best account and the perfect message to send them
(33:00) Data hygiene & practical tips for improving data quality
(38:26) Non obvious ways that AI will show up in GTM
(40:59) Where humans will need to stay in the loop as AI continues to evolve
(44:57) The ideal org structure & collaboration for AI transformation
(50:07) Favorite underrated software tool, growth hack, & conclusion
For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai
