As AI expands what marketers can build, Slack CMO Ryan Gavin argues that the bigger opportunity is not simply efficiency, but what people do with the capacity they gain. He and McKinsey Partner Jeff Jacobs explore why marketers are becoming builders, how leaders can define jobs by outcomes instead of activities, why collaboration still matters as AI speeds iteration, and why a beginner’s mindset may be essential as organizations adapt.
The following transcript has been edited for clarity and length.
When marketers become makers
Jeff Jacobs: How is AI changing what it means to be a marketer?
Ryan Gavin: At the beginning of this year, I told my team to lay down their traditional job titles. Instead of defining themselves as data scientists, creators, or other specialized roles, I asked them to think of themselves as “marketing makers”—people who can build and solve problems beyond the traditional boundaries of their jobs.
One person on our team working on ad optimization was dependent on data scientists to provide the cohorts he needed to analyze performance. That bottleneck slowed him down. So he built a data science agent that could do much of the analysis, with humans validating the output.
That pattern is becoming more common: Marketers are moving from specialized roles toward being generalists and builders. When you approach your job believing traditional bottlenecks don’t have to exist, it changes what you can accomplish.
Define the job by the outcome
Jeff Jacobs: As traditional roles become less defined, how should leaders think about what their teams are accountable for?
Ryan Gavin: Throughout my career, I’ve used what I call a “single-sentence job description.” Define your job first by the outcome you’re trying to drive—not by the activity you perform.
If someone tells me, “My job is to manage a partnership,” I’d challenge that. The job might actually be to grow market share through partners. Managing a particular partnership could be the right way to achieve that today, but it may not be tomorrow.
That distinction is even more important now. Marketers need to define themselves by what they achieve for the business and the customer, not by the activities they manage.
Productivity isn’t the only goal
Jeff Jacobs: There’s a lot of discussion about AI and productivity. What are you seeing on the growth side?
Ryan Gavin: Leaders spend a lot of time thinking about what they’re investing in AI. But people remain one of the biggest investments for most organizations. So the bigger question is: What happens when those employees become significantly more productive?
That additional capacity can become a growth lever if people use the time they gain to create customer value, drive revenue, or reduce costs. That opportunity often gets overlooked when we talk about AI and productivity.
Technology alone won’t deliver it. Organizations have to make AI useful and intuitive for employees, not simply introduce new tools and expect people to change how they work overnight. Technology needs to fit naturally into the flow of work.
Why work remains human
Jeff Jacobs: As AI becomes more capable, what will remain uniquely human?
Ryan Gavin: The work is in the conversation. Bringing an idea to fruition is rarely a solo activity; it happens through collective iteration. It’s a team sport, and AI isn’t going to eliminate that.
What AI can change is the speed of iteration. Someone can turn an idea into a prototype quickly, then bring it to colleagues who add their expertise, challenge it, and improve it. The team can build together much faster.
That’s what we mean by “building in the open.” Instead of waiting until something is polished, teams can put early ideas in front of one another, gather feedback, and improve them almost immediately. We have a channel called Slack Unbaked for early prototypes: People share what they built, invite others to try it, and often update it the next day. AI accelerates the process, but people still shape the outcome together.
Lead with a beginner’s mindset
Jeff Jacobs: You’ve had a phenomenal career, including some big transitions along the way. What have those experiences taught you about navigating moments like this?
Ryan Gavin: I spent more than 20 years in big tech before leaving to join a start-up in the supply chain industry. A mentor told me, “If it’s a mistake, it’s a year, and then big tech will have you back with a loving embrace.”
I ended up staying for four and a half years, and they were probably four and a half of the best years of my career. The company wasn’t a financial success, but what I learned, the people I worked with, and the growth I experienced made me who I am as a leader today.
My advice now is to be comfortable having a beginner’s mindset and feeling a little unsettled. Acknowledge what you need to learn. Don’t pretend you know it all. Find people who know more than you do and learn from them.
Look at organizations at the forefront of AI and study how they work. Their pace may feel intimidating, but there are patterns and practices you can bring back to your own organization.
Start imagining what your company might look like when there are more agents than employees. How would work change? How would you prepare the organization for that? Be curious, stay open to what’s changing, and recognize that it may change more than we expect.
We don’t know exactly how quickly all of this will unfold. That makes curiosity and openness even more important. Things will change, and leaders will have an opportunity to learn and lean into that change.
