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June 2, 2026 | Case Study | 3 minutes

Rx for Marketing Velocity

Reengineering the Pharma Marketing Operations Center with Agentic AI 

How a global pharmaceutical leader compressed go-to-market time, eliminated half its QC rework, and laid the foundation for an AI-native Marketing Operations Center — starting with a focused QA Assist proof of concept. 

 

30–40% 

Faster turnaround

50% 

Less QC rework

~60% 

QC cycle time cut

≥40% 

Developer effort saved
 

 

 

    

Client Snapshot

CLIENT 

Global Pharma Leader 

INDUSTRY 

Pharma & Life Sciences 

FUNCTION 

Commercial & Marketing 

PROJECT 

MOC Process Redesign & AI Acceleration 

CAPABILITY 

Agentic AI · Intelligent Process Automation 

CHANNEL 

Global Marketing Operations Center (eDetail and beyond) 

 

The Challenge

A leading global pharmaceutical organization operates a centralized Marketing Operations Center (MOC) that orchestrates content production and deployment across digital channels, agencies, and markets. Rising volumes, tighter timelines, and a fragmented tooling landscape were stretching the model — manual briefs, repetitive QC, and late-stage rework were eroding speed-to-market and producing inconsistent quality across regions. 

What was holding the MOC back

  • Fragmented workflows across briefing, build, QA, and deployment tools 
  • Manual QA/QC consuming ~30% of go-to-market time 
  • Inconsistent quality and compliance across markets, with repeated rework 
  • Limited real-time visibility into production status and bottlenecks 

 

Our approach ·  A 2026 transformation roadmap on three pillars

A current-state assessment of the MOC examined content operations across pre-production, production build, and quality control — across people, process, and technology. The output: a forward-looking transformation roadmap anchored on three strategic pillars, with QA Assist selected as the first lighthouse use case. 

 

01 

Content Owner Assist 

 

AI-augmented briefing and intake — structured prompts, smart metadata capture, and channel-specific guardrails that reduce ambiguity before a single asset is built. 

02 

Developer Assist 

 

Accelerators across the production build for global markets — reusable patterns, templated logic, and AI co-creation projected to cut developer effort by at least 40% at scale. 

03 

QA Assist 

 

Agentic QA/QC validating tokens, links, metadata, layouts, fonts, colors, and brand guidelines — automating up to ~70% of checks while enforcing consistent global compliance. 

 

Proof of Concept  ·  QA Assist for eDetail

QA Assist was chosen as the lead use case because it represents ~60% of current MOC effort. In a focused POC on the eDetail channel, 12 complex quality checks were automated across Fonts & Color, Quality & Layout, and Metadata — with technical feasibility indicating ~70% of checks are automatable at scale. 

 

Impact  ·  From Efficiency Play to Operating-Model shift

Beyond the headline numbers, the engagement created the operating muscle for an AI-native MOC: integrated systems delivering real-time tracking and stronger governance, standardized quality replacing variable manual review, and a clear transition framework moving mature markets into next-generation models — POD, DPM++, and Self-serve. 

 

Looking ahead  ·  Scaling to an AI-native MOC 

  • Scale QA Assist beyond eDetail to the full asset portfolio across priority markets. 
  • Activate Content Owner Assist and Developer Assist to compound efficiency across the production lifecycle. 
  • Migrate the POC from the Navikenz environment to the client environment and harden for production. 
  • Re-shape the people model — transitioning mature markets to POD, DPM++, and Self-serve operating models.