Team Operations
In my last three full-time roles, my focus was on operational efficiency for marketing. Operational efficiency is critical for marketing leaders, as it leads to lean teams producing high-quality work. Data from workflow tools allows marketing leaders to better design their marketing organizations as it provides insights into the volume of work each team manages, cycle times for completing tasks. Marketing leaders leverage workflow data to justify headcount, rebalance team structures, and identify learning and development.
Yet through my work on operational efficiency, I have found that the current technologies have not delivered the insights marketing leaders need to manage their organizational design. What I am most excited about with AI is providing marketing leaders with a complete view of their teams’ workflows. The marketing team’s organizational design might change, but the process by which the team executes will become critical. Strategy and analysis are the focus for planning for the future. and deployment. In this post, I will dive into how AI can help reduce the time teams spend on development and deployment tasks.
Workflow Tools Importance
As a recap, Generative AI is based on past data. Generative AI identifies combinations of data and the likelihood that those patterns will appear again. Generative AI performs so well at writing because of the large amounts of accessible data. Yet, Generative AI companies are sharing that their tools can help identify repetitive tasks, aka busywork, and complete them faster. I do believe generative AI can help marketing teams with team operation task development and deployment.
Currently, marketing departments have several operations teams functioning, including creative operations, marketing operations, and project management teams. All of these groups focus on improving the efficiency of how the marketing group requests the development and deployment of materials.
Each of these groups leverages workflow tools like Jira, Wrike, Asana, Workfront, and others to help us take a production request through build, iteration, and execution. The greatest challenge for workflow management tools is adoption by requestors and oversight from the operations teams.
The teams that are building want this information documented. The requesters do not want to fill out forms or remain in the tool to provide feedback and approvals. Bringing requesters into the tool also has challenges with rush requests, because marketing always has one.
That final push for end-of-month sales. That last-minute opportunity to sponsor a billboard at a discounted price. Now the manager needs to enter a ticket and hope that it’s assigned immediately. No thanks, says the requester.
Benefits of Workflow Tool Data
Using the workflow tool for requests requires users to have trust in the tool and to provide clear direction. The requester knows sending an email directly to the team to execute is faster; they know the email will be received, and work can start immediately. No need to assign the work, no need to confirm the request was received.
Because the workflow tool can be skipped, it is often challenging to ensure the collected data is complete. Workflow tools are used not only to help ensure that requests are collected and resources are appropriately assigned, but also to provide an audit trail of work in production and to require teams to execute on requests.
Skipping the workflow tool results in incomplete direction for the team developing and deploying. Maybe you need more resources on your team because demand has increased, and there are not enough resources to complete the work? Maybe the type of work requires a new skill set that your team does not currently have?
Marketing leaders are in a challenging position: they need to create operational efficiency, yet they need tools that teams trust and can adopt, making workflow processes a consideration for AI. When teams skip the workflow tool, marketing leaders lose visibility into request volume, delivery times, and resource constraints, leaving them without a clear picture of how their teams actually operate. Marketing leaders are under pressure to reorganize their teams, and have limited information on how to do this.
Identifying how to build AI into the development and deployment process
Before you dig into any technology, conversations across teams must be had about their work habits, what tools they use, and how they prefer to engage with others and receive requests. Understanding that focusing everyone into one tool never worked. Where are they, and how could we include forms where they are today? And yes, there will be some type of intake form. Information is needed on a request. How can you make those requests simple, with selects and minimum data entry? Could you offer your target audience as selects? Could you offer your channel, image size, and format as selections?
Do some investigation into past programs you ran, both normal requests and rush requests. Where did those happen, inside the workflow request tool, in email, or in instant messaging tools? See if you can review 10 to 15 requests end-to-end. This would help you confirm that the information provided in the conversations also matches what your teams are doing. This information also lays the foundation for your new configuration, revealing where consistent data points appear so you can build smarter field selects and eliminate redundant requests. You could also build back-end logic if a specific request always needs the same interactions. This audit becomes your baseline dataset — the foundation for data-driven conversations about team capacity, skill gaps, and whether your current org structure matches actual demand
What to consider when building the AI solution
Now that you have investigated, interviewed, and documented how the teams request development and deployments, it’s time to architect the solutions and build the tools. Because this will be a tool you build and own, documentation is critical. Once the system is created, it will need to be administered.
If the person who is building the tool leaves, other team members must be able to administer the tool in their absence. Documentation should include a flowchart that identifies each step, stage, group involved, and the technologies to be used along the way.
User requirements documentation, change controls, multiple administrator logins, quality assurance steps, and bug/fix documentation. No two organizations will use the same configuration of communication, project management, and reporting tools. You could be a Google House, so consider Gemini; Microsoft House, so consider Copilot; Slack dependent, or heavy into Claude; any of these options will work.
Build to where your team is and their habits. Development and deployment tools are consistent; it’s the human interactions that are unique. Reduce friction in human interactions for efficiency.
Post-deployment of the AI solution is improving marketing operations
To make this solution a success, Marketing leaders, specifically the CMO, must enforce its use. Adoption is critical. The new centralized tool handles requests ranging from rush requests to year-long projects can only succeed if it is required to be used.
Adoption is a multi-step process. Team advocates were part of the development; have them help with getting their team members on board with the change. Please note that if you work for a small organization, adoption will still need to occur, but it will be easier since the team is smaller. Continuous training is the next most important step. Make sure users understand how to engage with the new technology and feel comfortable and confident using it. Offer office hours, training guides, and training videos, as each team member might have different learning styles. Make it easy for everyone to learn the new tool and gain trust in the product.
The final stage is feedback and updates. The beauty of technology is its versions. If the team missed an assumption during the build, capture it, document it, and update the tool.
Marketing leaders and the admin team must monitor adoption. Reports such as submissions per user, resource allocation, request cycle times, compliance rates, requestor satisfaction, and team utilization need to be implemented. Having reports that speak to the tools adoption helps identify additional training and follow-up conversations to understand why the individual has low adoption.
Expected Outcome of AI solution for marketing operations development and deployment
Marketing leaders can walk away from leveraging AI to help with marketing operations, creating efficiency within the team, and potentially consolidating operations from a multi-operation team into a centralized marketing operations team. Marketing leaders will walk away from this process with data that provides insights into how best to structure the team based on workload, surfaces bottlenecks and training needs through team execution efficiency data, and brings requests to where teams are. If the marketing leader is under pressure to review organizational design, having this information will help argue for a centralized generalist or specialist.