Name: Bryanna Clancy | 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

Bryanna

Clancy

Marketing AI Strategy & GTM Engineering

Hex

Bryanna is building GTM Engineering as a discipline at Hex, designing the AI agents, systems, and analytics infrastructure that move marketing operations from manual to autonomous. Most B2B marketing teams have the data. Few have the systems to act on it. Bryanna builds both. At Hex, she leads Marketing Strategy and GTM Engineering, a function she is building from the ground up alongside Hex's first GTM Engineer. The team uses Claude, Clay, AI agents, and a connected data infrastructure in Hex to run GTM differently. Her recent builds include a Marketing Intelligence Hub powered by Claude, which gives her team a natural-language interface for managing all of their projects and work. She also built an AI-powered Hex data app that pulls account signals into one place so sales can prioritize and manage their book. Alongside these are Clay-powered signal workflows for account-based targeting and a brand voice content agent, built with Claude and GitHub, that keeps the team's content on-brand. Previously at Amplitude, Bryanna ran GTM strategy and analytics across marketing and PLG, using data to guide decisions during critical stages of scale.

Button

21 January 2026 09:45 - 10:15

Who owns AI in GTM? Building the operating model that turns scattered experiments into a shared capability

In many GTM teams, AI adoption looks like this. Marketing has its favourite tools and prompts, sales has built its own shortcuts, RevOps is quietly automating something else, and none of them know what the others have learned. AI was meant to connect the business, but left unmanaged it simply speeds up the silos that were already there. This session shows how to centralise AI know-how without slowing teams down, from setting shared standards and owning a common playbook to deciding who looks after what. It's a practical look at turning scattered experiments into a capability the whole GTM organisation can build on. Key takeaways: - How to spot the signs that AI is reinforcing your silos rather than breaking them down - How to set shared AI standards and a central playbook that teams will actually use - How to structure ownership so best practice spreads across functions