Initial Situation

The retailer’s marketing department had grown organically over many years, driven by increasing demand by a rapidly expanding business. This growth resulted in fragmented technical systems, a sprawling operating model, and governance bottlenecks that had delayed the lead time between briefing and channel distribution.

Leadership wanted to understand how AI could help improve the campaign briefing and creation process. This included assessing their existing operating model, content supply chain and technology stack. Their objective was to design a best-in-class campaign process that enables smarter, faster, and higher quality campaigns at scale.

Our Contribution

• Conducted a structured process redesign phase, mapping existing workflows, team interactions and the in-use technology stack to establish a current-state baseline.
• Ran a discovery engagement with the retailer's marketing organisation to identify pain points and opportunity areas for AI intervention across the campaign lifecycle.
• Diagnosed three priority improvement areas: briefing iteration and version management, manual hand-offs and approval loops between teams, and capacity constraints on shared production resources.
• Delivered a parallel technology stack assessment, quantifying the extent of tool sprawl and identifying capability overlap across siloed, tactically adopted solutions.
• Evaluated candidate AI use cases against the identified pain points and prioritised two for delivery as an MVP: a Campaign Briefing Tool and an AI Content Studio spanning image, video and copy generation, together forming the foundation of the retailer's AI Marketing Suite.
• Designed a target operating model built around unified campaign pods spanning production and activation, replacing the prior siloed team structure.
• Defined a target technical architecture for a consolidated AI Marketing AI Suite, rationalising the fragmented tool landscape identified in the assessment phase.
• Built a business case quantifying estimated savings by campaign type, paired with a redesigned business process to maximise value capture from the AI Suite.
• Produced a phased rollout plan for the target operating model, including a recommended pilot approach, hiring plan, and detailed role descriptions for new and redefined positions.

Value Delivered

• The redesigned Suite and operating model are projected to increase operational efficiency by 66%, driven by the elimination of manual hand-offs, consolidated tooling, and streamlined approval pathways.
• Asset output capacity is projected to scale by a factor of 20, enabling a substantially higher volume of campaign assets to be produced within existing resource needs.
• The step-change in output capacity enables deeper campaign personalisation at scale, extending consistent, tailored messaging across a growing number of digital channels.
• Consolidation onto a unified Marketing AI Suite architecture addresses the tool sprawl identified in the technology assessment, reducing duplicated spend and closing capability gaps between previously siloed solutions.
• The shift to unified campaign pods removes structural bottlenecks in briefing version control and cross-team approvals, giving the retailer a scalable operating model to support future growth in campaign volume and diversification.
• A defined pilot, change plan and role architecture give the retailer a low-risk pathway to operationalise the new model, with the business case providing a clear basis for phased investment decisions.

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