General Assembly needed a more consistent and reliable way to plan, budget, report, and reconcile global marketing spend because spreadsheets, inconsistent taxonomies, and manual coordination across more than 45 agencies were slowing decisions and limiting financial control.
Reported impact
General Assembly gained a unified financial view of global marketing activity, reduced planning and reporting effort, and increased confidence and speed in budget and pacing decisions.
General Assembly worked with MINT and IBM to replace fragmented marketing-financial operations with a governed AI planning stack built on IBM watsonx.ai. The first step was a unified data model with standardized taxonomies and definitions across regions, channels, and business lines, eliminating the inconsistent local structures that had made comparisons unreliable. On top of that foundation, MINT implemented agentic AI workflows with watsonx.ai for media planning, budget pacing, scenario-based forecasting, and automated reporting and reconciliation. These governed workflows replaced spreadsheet-heavy manual coordination and created a single operational view of global budget status, enabling teams to spot risks, forecast outcomes, and react earlier. Because the system combines shared definitions, automation, and AI-assisted decision support, General Assembly can now scale marketing investment and evaluate performance globally without adding the same level of manual planning overhead.
Regional marketing and financial data is normalized into a unified model, watsonx.ai applies AI-assisted planning and forecasting workflows against that governed dataset, and the resulting budget and performance views feed global reporting, pacing, and reconciliation decisions.
“100% visibility into global budget and pacing”
“20% time savings in planning and reporting”
“By moving away from spreadsheets, inconsistent taxonomies, and manual reporting, we now operate with unified definitions”
“MINT implemented a unified data model with standardized taxonomies and definitions across all regions and business lines.”
“Using agentic AI built with IBM watsonx.ai, the solution enabled AI-supported media planning, scenario-based forecasting and proactive budget pacing.”
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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