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Publicis Sapient Reinvented Marketing to Grow Faster Without Increasing Spend

Rebuilding marketing around tasks, workflows and AI-enabled operations.

Intro

Publicis Sapient set out to answer a simple question: How do you grow faster than the market without spending more?

What started as experimentation with AI tools quickly revealed a bigger truth. Faster execution alone wouldn’t create meaningful change. To unlock real growth, the team had to rethink how marketing work actually gets done.

The Problem

Our marketing team was working hard, but the system around them was slowing everything down.

Campaigns required more than 50 handoffs across teams, from content and analytics to operations and brand. Launches could take up to 20 days. Even small changes meant navigating complex workflows and approvals.

AI tools were already in use, but they weren’t changing outcomes. People were completing the same tasks in the same order, just a little faster. Those gains at the individual level didn’t translate into meaningful impact across the organization.

Too much time was going into coordination, approvals and managing the process of getting campaigns out the door. That limited how much the team could actually deliver.

The Solution

What started as experimentation with AI tools quickly revealed a bigger issue. People were using the tools, but they weren’t changing how the work itself got done. Campaigns still moved through the same workflows, approvals and handoffs. Individual tasks became faster, but the system around them stayed the same.

That realization changed the approach. Instead of focusing on tools first, we focused on the work itself:

  1. Experiment with technology: Teams spent six months testing AI tools and building early automations to understand where the technology could help and where it fell short. One early attempt at a fully autonomous campaign management agent exposed an important lesson: Automation alone wasn’t enough. Without the right context and human input, quality suffered. The exercise was still valuable because it helped build familiarity with the technology across the organization and created a foundation for broader change.

  2. Map the work: The team documented more than 700 marketing tasks through 15 cross-functional workshops spanning content, social, analytics, communications, product marketing and other functions. Each task was evaluated based on whether AI could handle it, support or if it required human judgment. That shifted the conversation away from roles and toward the work itself, helping teams identify where AI could create the most impact. The exercise also helped leaders see how teams were actually spending their time. Across the organization, 70 to 80 percent of tasks were identified as candidates for automation or augmentation, creating opportunities to redirect effort toward higher-value work.

  3. Let marketers build: Rather than relying on a central technology team, marketers built and refined the assistants themselves. The people closest to the work shaped how the tools operated, embedding real context and expertise directly into the system. That also drove stronger adoption because teams were building tools for their own workflows, not adapting to someone else’s process. High-performing practitioners played a critical role in designing and evaluating the assistants, helping ensure the tools reflected real-world expertise and delivered useful outputs from the start.

  4. Redesign and connect workflows: Those assistants were orchestrated through Bodhi and connected into larger workflows. With that foundation in place, the team redesigned how campaigns moved from planning to execution. Processes that once involved more than 50 touchpoints across teams were streamlined into connected workflows with human checkpoints only where judgment mattered most. Campaign timelines that had previously taken 20 days were reduced to as little as three to five days. The work extended beyond marketing. Operations, web, social and brand teams also adapted their processes, helping eliminate unnecessary handoffs, approvals and delays across the campaign lifecycle.

  5. Redefine roles: As the workflows changed, roles changed with them. Marketers spent less time coordinating execution and more time focused on higher-value work like storytelling, decision-making and orchestrating campaigns across channels. Campaign managers became journey orchestrators. Social teams shifted their focus from publishing content to creating it. New role expectations emphasized storytelling, data fluency and orchestration, reflecting the skills needed in the new operating model.

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Marketing transformation isn’t about adding AI to the way we already work. It’s about rethinking the work itself—what should be automated, what should be accelerated and where human judgment creates the most value. At Publicis Sapient, we’ve used AI as a catalyst to redesign how marketing operates, helping our teams move faster while focusing more of their energy on the strategic, creative and intuitive work only people can do.

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— Teresa Barreira

CMO at Publicis Sapient

The Impact

By changing how work actually got done, the team started to see measurable impact across both performance and output:

Operational improvements:

  • 50% faster time to market
  • 40% increase in marketing capacity

Workflow transformation:

  • Campaign timelines reduced from 20 days to 3-5 days
  • 3-4x increase in campaigns running at the same time

Growth impact:

  • 7x increase in lifecycle programs
  • 25-35% improvement in lead conversion
  • 20% increase in qualified opportunities
  • 20x increase in creative testing
  • 15-25% faster funnel velocity at the same spend
  • 10-20% lift in conversion from more personalized messaging

But the biggest shift wasn’t just speed or efficiency.

The team created capacity.

With less time spent on manual work and slow processes, they could launch campaigns that previously weren’t possible. That new capacity became the engine for growth, allowing Publicis Sapient to move faster than the market without increasing resources.

This transformation shows that AI doesn’t create advantage by speeding up existing work. It creates advantage by making entirely new ways of working possible.

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