Media planning breaks down when processes rely too heavily on individual ways of working.
A Brussels-based media and advertising company faced this exact situation. As campaign complexity increased and data requirements grew, their planning model could no longer keep up.
They needed a structured way to manage workflows, align teams, and prepare for AI-assisted planning—without slowing down execution.
Challenges
The organization relied on experienced planners to drive campaigns end to end. While this worked in the early stages, several issues started to appear as volume and complexity increased.
Planning inputs were scattered across multiple tools and files. Teams spent a significant amount of time consolidating data manually. Version control became difficult. Traceability was limited.
Deliverables also varied depending on who was leading the campaign. Templates were not consistently applied. Review criteria differed from one case to another. This created rework and made it hard to compare outputs across campaigns.
Governance added another layer of friction. Responsibilities overlapped. Decision points were not always clear. Iteration loops extended planning cycles and reduced responsiveness.
At the same time, expectations were rising. Clients required more data-driven media planning, faster turnaround, and higher consistency across campaigns.
The company needed a new foundation. One that could support growth without increasing operational complexity.
Our Approach
We started with a full assessment of the existing Media Planning process.
The objective was simple: create a shared understanding of how work actually happened, then redesign it with the right level of structure and control.
The work was carried out over six weeks through a series of focused workshops and analysis sessions.
First, we mapped the full workflow—from brief to reporting. This included activities, roles, tools, decision points, and iteration loops. The goal was to make bottlenecks visible and quantify manual effort.
Then, we analyzed which tasks created value and which ones did not. This helped identify where Workflow Automation could reduce time spent on repetitive actions such as data consolidation.
From there, we worked with stakeholders to define a target operating model.
This new model introduced:
- Standard lifecycle stages across all campaigns
- Clear handoffs between teams
- Governed templates for each step (brief, insights, strategy, scenarios, budgets, reporting)
- Defined quality gates to ensure consistency
We also aligned the process with client interactions using a service blueprint. This clarified how internal activities supported each customer touchpoint and reduced coordination issues between teams.
Finally, we designed an implementation-ready architecture to support the new model.
- Odoo was selected as the workflow backbone to orchestrate processes
- SharePoint was structured as a central repository for versioning and knowledge reuse
This setup created the conditions for AI-Assisted Planning, embedded directly into controlled workflow steps rather than added as a standalone tool.
The Results
The impact of this work is visible at several levels.
Planning is now based on a standardized lifecycle. Teams follow the same structure across campaigns, which improves consistency and makes outputs easier to compare.
Ownership and responsibilities are clearly defined. Handoffs are explicit. This reduces ambiguity and limits rework.
The introduction of a workflow backbone and structured repository improves traceability. Each step is documented. Versions are tracked. Planning knowledge can be reused instead of recreated.
The organization also gained a clear view of its maturity level.
A five-dimension assessment showed a progression from 1.8 to a target of 3.8. This gives a concrete reference point to prioritize future improvements and track progress.
Most importantly, the company now has a foundation for AI-Assisted Planning.
AI use cases can be introduced step by step within a governed process, supporting planners without creating additional complexity or risk.
What this means for organizations managing complex Media Planning
Many organizations face similar challenges when scaling their Media Planning activities.
- Processes depend on individual experience rather than shared standards
- Tools are not connected, leading to manual work and data inconsistencies
- Governance is implicit, which creates delays and rework
- AI initiatives are considered, but there is no structured environment to support them
Process Governance and Workflow Automation are what allow teams to move faster while maintaining control.
When combined with a clear architecture, they also create the conditions for AI to be used in a practical and controlled way.
Need Some Help?
We work with organizations that need to structure their operations before scaling them.
In this case, the focus was Media Planning. In other contexts, it can be product development, data workflows, or operational processes.
The approach remains the same:
- Understand how work is done today
- Make inefficiencies visible
- Design a target model with clear rules and ownership
- Build the right technical foundation to support it
- Introduce automation and AI where it brings real value
This is how Process Governance becomes a lever for performance.
If your teams are facing similar issues with Workflow Automation, Media Planning, or AI-Assisted Planning, the first step is often the same: bring structure to what already exists.