AI in GTM is often framed as a productivity tool: write the email faster, automate the workflow or increase the volume of outreach.
Harrison Rose, Co-Founder of Goodfit and Paddle, makes the case for a more fundamental shift.
His argument is that AI becomes far more valuable when it moves from executing tasks to making decisions. Harrison traces that thinking back to Paddle, where classification models helped identify relevant software companies more quickly and accurately than a manual research process.
He then looks at what today’s AI makes possible. By combining market data with past wins, losses, contract values and interactions, teams can begin to predict which accounts are worth pursuing and how to approach them.
Harrison explains how expected value can inform those choices and why GTM systems may increasingly decide who gets targeted, when, through which channels and with what level of spend.
This talk was recorded during the EUVC Summit & Awards Show 2026.
Highlights
- Why scaling old GTM workflows misses the bigger AI opportunity
- Why Harrison sees decision-making as AI’s core strength
- What Paddle’s early use of classification models revealed
- How AI can use more context than an individual rep
- How expected value can improve account prioritisation
- Why GTM strategy could become increasingly dynamic and machine-led
- What this shift could mean for the buyer experience
Timestamps
- (00:00) Intro
- (01:00) Why AI in GTM needs a different approach
- (02:15) The GTM problem Harrison faced at Paddle
- (03:25) Automating prospect research with classification models
- (05:00) What Paddle’s early use of AI revealed
- (06:10) Why automating bad GTM work does not make it better
- (08:05) Why decision-making is AI’s real strength
- (09:45) How AI can outperform traditional account mapping
- (11:10) Using expected value to prioritise accounts
- (12:50) Letting AI decide channels, spend and outreach
- (13:55) What programmatic advertising tells us about the future of GTM
- (14:35) The future of AI-led go-to-market
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