In B2B sales with multiple decision-makers, tracking individual MQLs is a "lazy metric" that misrepresents buying intent. Success depends on identifying and engaging the entire buying group. Marketing's goal should be to qualify the group, not just a single lead.
Adopt engineering methodologies like sprints, story points, and capacity dashboards for marketing operations. This provides the data needed to manage stakeholder expectations, prioritize requests transparently, and move the team from reactive order-takers to strategic partners with a defensible roadmap.
Marketers often treat Mops as order-takers for quick tasks. Instead, view them as strategic partners managing complex systems. This reframes the relationship from transactional to collaborative, acknowledging the intricate "plumbing" behind a simple request like an email send.
To stop incomplete requests, configure your ticketing system (e.g., Jira) to require all necessary information—like asset links and UTM parameters—before a ticket can be submitted. This forces stakeholders to do their upfront work and saves the ops team from chasing down details.
To avoid biased prioritization, structure Marketing Ops as an independent unit rather than placing it under Demand Gen or a sales-led RevOps team. This allows Mops to be a neutral hub, prioritizing projects based on their impact on total company revenue, not just one department's goals.
Mops teams become respected strategic partners when they stop passively accepting requests and start asking "why." By questioning the goal behind a task and suggesting better approaches, they demonstrate expertise and train stakeholders to treat them as advisors, not a fast-food drive-thru.
The best initial use for AI in marketing operations is automating high-volume, low-complexity "digital janitor" tasks. Focus AI agents on answering repetitive questions (e.g., "Why didn't this lead qualify?") and cleaning data (e.g., event lists) to free up specialist time for more strategic work.