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How WPP operationalizes platform and data engineering for AI marketing

What happened
Based on Google Cloud Blog · Aug 10, 2026

WPP has built an AI-driven marketing system, WPP Open, by partnering with Google Cloud to unify fragmented global data into a secure, scalable platform, enabling faster campaign deployment and measurable business gains.

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How WPP operationalizes platform and data engineering for AI marketing
Google Cloud Blog — Google
Key points
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Between chaotic levels of market fragmentation and economic volatility, marketing and communications agencies can no longer rely on the human intuition they’ve traditionally used to win clients and optimize their ad spend.
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WPP is replacing that guesswork with an AI-powered view of shifting market dynamics, giving brands predictive certainty that lets them invest with confidence while moving at the speed of the market.
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That’s the value of WPP Open, its agentic marketing system.
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But before it could begin applying sophisticated AI models to power those insights, WPP had to overcome a critical engineering challenge: the marketing data that made up the models was fragmented across hundreds of global agencies.
Key numbers
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WPP reported a 70% gain in production efficiency, a 33x increase in content volume, and a 2.
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8x rise in campaign return on investment.

WPP faced challenges deploying AI tools due to marketing data scattered across hundreds of global agencies, making efficient and secure AI deployment difficult. To address this, WPP partnered with Google Cloud to create a unified data backbone, standardizing serverless compute and data processing workflows. This enabled the company to deploy targeted marketing campaigns in days instead of months, improving operational speed and scalability.

WPP centralized its data infrastructure by adopting a service-based project structure, using Google Cloud Storage and BigQuery in shared projects while segregating compute workloads. Security was enforced at granular levels with identity and access management controls applied to individual buckets and datasets. Raw data from partners was organized into dedicated buckets, then processed using Managed Service for Apache Spark to cleanse and normalize information into standardized cohort definitions for AI models.

The platform’s core processing engine, built in Scala, ensures traceability and compliance by tracking data transformations and origins. WPP also implemented GitLab CI/CD templates to automate deployments, reducing cognitive load on teams and enforcing security standards. A 'build once, deploy many' methodology minimized configuration drift, while progressive traffic migration allowed controlled rollouts of new revisions.

The collaboration with Google Cloud delivered quantifiable business impact, reducing creative and strategy time from four weeks to three hours. WPP reported a 70% gain in production efficiency, a 33x increase in content volume, and a 2.8x rise in campaign return on investment. Enhanced security measures, including Wiz scanning and zero-trust access, further strengthened the platform’s reliability and performance.

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