WPP cut campaign deploy time from months to days with Google Cloud
WPP runs hundreds of agencies globally. That scale created a data fragmentation problem that blocked any serious AI deployment: marketing data was siloed across agencies, making it nearly impossible to serve models cleanly or securely.
The fix was a joint engineering effort with Google Cloud to build a unified data backbone. The results, as reported by WPP, are concrete: creative and strategy time dropped from four weeks to three hours, production efficiency improved 70%, content volume grew 33x, and campaign return on investment increased 2.8x.
How the stack is built
Raw data from partners lands in dedicated Google Cloud Storage buckets. From there, Managed Service for Apache Spark runs custom Scala and Spark jobs to cleanse and normalize inputs into standardized cohort definitions keyed on five dimensions: age, gender, geo, product, and interest. Kubeflow handles pipeline orchestration on top of a serverless architecture, which kept the engineering team focused on transformation logic rather than cluster management.
Google Cloud Storage and BigQuery are centralized into shared data projects, with IAM controls applied at the individual bucket and dataset level so teams only access what they’re authorized to see. The processing framework, written in type-safe Scala, enforces full source traceability so every data point feeding the AI models can be traced back to its origin.
Deployment and security
The platform engineering team built reusable GitLab CI/CD templates covering Cloud Run workloads, batch pipelines, multi-stage deploy workflows, and event-driven Cloud Run Functions. Wiz security scanning runs at the pre-push phase to catch vulnerabilities before code merges. Google Cloud Identity-Aware Proxy enforces zero-trust access across internal applications.
Container images are built and tested in development, then promoted directly to production without rebuilding. This eliminates configuration drift between environments. The same CI/CD templates also support progressive traffic migration, routing a small percentage of traffic to new revisions before a full rollout.
The operator takeaway
This is enterprise-scale infrastructure, but the pattern applies at smaller scale too. The core insight: fragmenting AI skills into too many siloed systems kills your ability to serve models reliably. Centralizing data first, then building modular processing on top, is what made the downstream AI gains possible. The business results came after the plumbing was solid, not before.
I’m founder of BizStack at Cagri Sarigoz LLC and a passionate advocate for entrepreneurs.
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