Cross-Function AI Enablement Unlocks The Productivity Gains Most GTM Teams Are Missing

The pressure to adopt AI across revenue organizations has become universal. CEOs want it, boards expect it, and LinkedIn celebrates it, but beneath the surface, most GTM teams are caught in a gap between mandate and execution. They're told to use AI to do more with less, yet they lack the enablement, the training, and often the cultural permission to learn how to use it well. The ROI that does exist tends to be soft and difficult to quantify, and the most impactful use cases remain largely invisible because they aren't the ones generating attention on social media.

Brandon Redlinger sees the pattern up close. He's a Fractional VP of Marketing for B2B SaaS and AI firms and author of the Stack & Scale newsletter, with past experience that includes leading demand generation and growth at companies like Chili Piper, Crosschq, and Demandbase. His work gives him a direct view of how automation shows up in day-to-day execution, how different teams are navigating the AI transition, and where most of them are falling short.

"AI is expected everywhere in go-to-market right now, but most teams still don't have the time or enablement to actually learn how to use it well," Redlinger says. He notes that while teams say AI makes them more productive, companies expect them to take those saved hours and do more work, resulting in an endless loop of productivity optimization. 

• Avoiding the content trap: When most people think about AI in marketing, they think about content generation. Redlinger argues that's the wrong place to start. In his view, the highest-leverage applications are in operations, systems, and data, taking unstructured information from across the organization and making it usable. "The biggest value lies in taking data from different sources, a lot of it unstructured, making sense of it, and surfacing it so your people can make more intelligent, more informed decisions. Whether that's insights into target accounts, better campaign targeting, or more personalized outreach, that's where the real impact is."

• The unsexy high-impact use cases: He points to specific examples that rarely get attention, like using AI to analyze sales calls at scale and extract patterns, refreshing ICP documents that haven't been updated in years, or compressing onboarding by giving new hires AI-synthesized context instead of hours of raw call recordings. "It's not sexy. It's not external-facing. But it helps you as a marketing team do your job a lot better," Redlinger says. "It's harder to quantify and it's a less compelling LinkedIn post. That's probably why it doesn't get the spotlight."

If AI's operational value is real, why aren't more teams capturing it? Redlinger points squarely at leadership, incentives, and culture. The most common failure mode he sees is top-down pressure without top-down enablement. A CEO pushes the CMO to use AI. The CMO pushes the team. But nobody stops to define what success looks like, what's realistic, or how to build the capability. "A lot of times, the CMO just wants to keep their job, so they say yes to the CEO and turn around and put all the pressure on the team," Redlinger explains. "There's a much more productive way to approach it. Set expectations, push back in the right way, and have a reasonable conversation about what you're actually trying to accomplish and why."

• Competing compensation: Compounding the problem is misaligned compensation. If marketing is measured on leads and sales is measured on closed deals, the two functions will optimize in different directions regardless of what AI tools they share. "The closer we can get on compensation alignment, the better. The responsibility lies with the CEO because they're the ones deciding comp for both leaders," Redlinger says.

• The capability mandate: He also pushes back on the popular claim that marketing should be comped on revenue. "I think that's one of those things people like to say on LinkedIn because it's popular. But when you don't have control over how deals are worked after they become opportunities, it's a lot less reasonable. I've seen people say it and then not actually be comped that way themselves."

Redlinger sees the rise of dedicated AI roles within GTM organizations as one of the most promising structural developments. Rather than expecting every individual to self-teach, these hires serve as centralized resources who identify automation opportunities, set up tools, and prevent the siloed technology purchases that create more complexity than they solve. "I see more and more companies making this move," he shares. "They become the person who says, 'You want to automate that? I already set it up for another team.' Without that, you get siloed tool purchases where three teams buy the same thing and nobody knows." Redlinger asserts that leaving AI adoption to individual initiative works for top performers, but fails at scale. "Your best people are naturally going to go learn AI on their own, but you can't count on everyone doing that. It's too much to ask people to do their current job and learn how to use AI. The company has to invest in those resources."

Redlinger is candid about his prediction that this environment gets worse before it gets better. He foresees more tools, more pressure to show activity, and more noise as teams chase output metrics instead of focusing on alignment and outcomes. "People are focusing even less on alignment and culture because they have so much pressure on doing more activities. If you're doing more activities, that probably means you're using AI, so it looks right, but is it actually contributing to the business in a meaningful way?" He believes the teams that pull ahead will be the ones where leadership invests in culture, defines clear expectations, and treats AI adoption as a sustained capability-building effort rather than a tool rollout. "Culture starts at the top with the CEO. If you want to be successful, look at your own culture first. No one else is going to fix it for you."