Should I assign someone on my team to track AI developments, or is that everyone's job?

Treating AI tracking as purely shared responsibility sounds democratic but often means no one does it well. Everyone stays surface-level because no one has dedicated time to go deeper. The alternative extreme, centralizing all AI responsibility in one person, creates a bottleneck and leaves the rest of the team dependent rather than capable.

The practical middle ground is a point person who filters and curates. This role does not require a full-time commitment or a new hire. Someone on the team spends a few hours each week monitoring developments, evaluating what might be relevant, and bringing forward what deserves the team's attention. They filter out the noise so others do not have to. They maintain awareness of what tools the team is using, what is working, and what gaps exist. They are the first call when someone has a question about AI options.

This works best when the point person has genuine interest in the space and credibility with the team. Assigning someone who sees it as a burden creates resentment and poor output. Look for the person who is already informally playing this role, already experimenting on their own, already sharing what they find.

Keep application distributed. The point person surfaces opportunities; team members decide what to test and adopt in their own workflows. A content specialist evaluates AI writing tools based on their actual needs. An analyst tests data processing capabilities against their real datasets. This distributed testing generates faster feedback than funneling everything through one person, and it builds fluency across the team rather than concentrating it.

Create a lightweight rhythm for exchange. The point person shares a brief update weekly or biweekly: what is new, what is worth watching, what the team should ignore. Team members share back what they have tried and what they have learned. This two-way flow keeps the point person informed about real needs and keeps the team informed about relevant developments.

Revisit the structure as your AI maturity grows. Early on, a single point person may be sufficient. As adoption deepens and AI touches more workflows, you may need distributed ownership by function, with the original point person coordinating across them. The right structure depends on your team's size, the breadth of AI use cases, and how central AI has become to your operations.