Specific case studies across the portfolio also told a dynamic story. At Netcraft, AI is rewriting their sales playbook with a standardized “meeting prep automation” template. Leveraging ChatGPT and Microsoft Copilot, sales reps have doubled proof-of-concept win rates and cut ramp time for new hires in half.

At Zenwork, sales engineers prototyped a custom API integration for prospective clients in days (not weeks), generating nearly $1 million in new pipeline over less than two quarters.

And at Bitly, the marketing team used AI-powered video generation tools like Veo 3 and KlingAI (combined with Claude for scriptwriting and Envato and ElevenLabs for voiceover) to slash production time from eight weeks to one, at 4% of prior cost…while seeing double-digit gains in engagement and conversion.

After the readout and presentations, most attendees ended up with a list of new tools to try. But they were also left thinking about where creativity, not automation, could drive the biggest gains.

The Value Shift: Fewer Sheldons, More Ricks

Tim Sanders, Chief Innovation Officer at G2, shifted the focus from what AI can do to what people will choose to do with it. “The potential of AI isn’t defined by how smart the systems get,” said Tim, “but by how fast humans adapt and apply them.”

In his keynote, Winning with Today’s AI, Sanders distilled the landscape of generative AI into what he called the economics of prediction. The ability to predict, in Tim’s thinking, is synonymous with intelligence – intelligence that’s been decoupled from judgment for the first time in human history. As the cost of prediction drops, he argued, the bottleneck shifts from processing power to human judgment. “And when prediction gets cheaper,” he said, quoting from Prediction Machines, “the value of judgment goes up.”

So with judgment – and taste – at a premium, many leaders need to rethink their hiring strategies. To illustrate his point, Tim shared an interview clip of Rick Rubin, the legendary music producer who’s worked with everyone from Aerosmith to Johnny Cash. In the clip, Rubin admits to Anderson Cooper that “I know nothing about music.”

Anderson Cooper: (LAUGH) You must know something.
Rick Rubin: Well, I know what I like and what I don’t like. And I’m decisive about what I like and what I don’t like.
Anderson Cooper: So what are you being paid for?
Rick Rubin: The confidence that I have in my taste and my ability to express what I feel.

“We need to hire fewer Sheldon Coopers, and more Rick Rubins,” Tim said, addressing the room of leaders. “Don’t hire for technical ability. Hire for taste.”

Winning by Design: Rethinking Growth Architecture

If Sanders provided the philosophical spark, the team from Winning by Design (led by Shari Johnston and Lauren Goldstein) brought the operational blueprint. Their session, Growth in the Age of AI, introduced a new framework for building what they called “intelligent revenue engines.”

Their model reframed growth around three levers:

1. Do More (increase inputs like leads or spend),
2. Do Better (optimize conversions), and
3. Do Smarter (design self-reinforcing growth loops).

In an era when “doing more” is hitting diminishing returns, the argument for “doing smarter” landed hard.

The session was built around the “Bowtie Model,” which replaces the classic marketing funnel with a holistic customer journey: awareness, education, selection, onboarding, retention, and expansion. Unlike the funnel, which ends at the sale, the Bowtie emphasizes recurring impact as the driver of recurring revenue.

Participants broke into small groups to workshop their own “growth architectures,” identifying friction points across the Bowtie and pinpointing where AI could improve conversion. To start the wheels turning, Winning by Design talked through an extensive list of options, categorized as either “Assistive AI” – tools that support human execution – or Agentic AI, systems capable of autonomously handling defined workflows like onboarding or renewal forecasting.

Winning by Design extended that idea into a practical roadmap for go-to-market teams, showing how companies progress from Assistive to Agentic to Orchestrative and eventually Autonomous AI models. But that framework came with guardrails. Each stage, they emphasized, requires parallel investment in human skills – from prompt engineering to change management – to avoid the “automation trap” of faster but shallower engagement.

This sentiment echoed Tim Sanders’ conclusion the evening before: while AI can make companies faster, better, and cheaper, it can’t make them more human. The differentiator – and the most durable moat – remains judgment, empathy, and originality.

Intelligence, Applied Together

The experiments shared in Scottsdale were a demonstration of collective momentum: leaders comparing notes in real time, learning what’s working across different go-to-market engines, and drawing inspiration from their peers’ experiments.

Our time together also served as a reminder that no one is figuring out AI in isolation. As teams keep sharing their results and lessons, applied intelligence becomes a shared asset — a living playbook that gets sharper with every new test, insight, and win.