Where should a marketing leader start if their team isn't using AI yet?

Before evaluating any AI tool, audit how your team spends its time. Look for tasks that are repeatable (weekly or daily rather than quarterly), time-intensive (hours spent on research, drafting, or data work), and produce outputs your team already knows how to evaluate. These three criteria help you identify where AI can create immediate, visible leverage rather than chasing theoretical improvements.

Once you've identified candidates, assess which tasks are well-suited for AI. Content research, competitive monitoring, repurposing content into new formats, campaign summaries, and CRM enrichment are common starting points. They're concrete, bounded, and low-risk if the output needs refinement. With your priority tasks identified, you can evaluate tools based on how well they address your specific needs rather than chasing features you may never use. This problem-first approach also makes it easier to measure success because you have a clear baseline.

When you're ready to move forward, start small. Select two to four team members who are curious about AI and willing to experiment. Give them access, clear permission to learn, and dedicated time to explore. Your role during this phase is to stay close, remove blockers, and resist the urge to scale before the pilot produces something worth replicating. Document what works: which configurations, instructions, and review processes led to good results. That documentation becomes the foundation for broader adoption and ensures you're not starting from scratch when you bring in the next group.

Expand deliberately after the pilot demonstrates value. Each cycle builds organizational knowledge about how AI fits your specific context. Rushing to a broad rollout before you have that foundation typically leads to uneven adoption, frustrated team members, and tools that sit unused.