AI Campaign Factory
Reduced campaign production time by 70% while increasing creative output 10x through an end-to-end generative AI pipeline
Client & Context
Global CPG Brand
Large global consumer packaged goods company operating across 12 regional markets with 40+ SKUs and 8 digital channels. (Confidential; Fortune 500.)
Highly competitive FMCG market requiring rapid, seasonal campaign execution at scale.
Agency spend ~ $2.4M per major campaign cycle.
Need to compress creative-to-market timelines while maintaining brand consistency across regions and channels.
My Role — Creative Technology Lead
Owned solution architecture, end-to-end AI pipeline design, prompt engineering strategy, and cross-functional technical leadership.
Acted as the technical bridge between creative and engineering teams.
Team & Timeline
2 ML Engineers, 1 Full-Stack Developer, 2 Senior Art Directors, 1 Copywriter, 1 Project Manager
Discovery & Architecture (4 weeks), Development (8 weeks), Pilot (4 weeks), Validation & Iteration
Business Challenge
The client's campaign production process was a bottleneck strangling market responsiveness. A single seasonal campaign required 4-6 weeks from brief to deployment, with creative assets passing through 7 approval stages and 3 external agency partners. By the time campaigns launched, market conditions had often shifted, and competitors with faster pipelines captured first-mover advantage.
Technical Pain Points
Fragmented toolchain: Creative teams juggled 8+ disconnected tools (Photoshop, Figma, DAM systems, project management, proofing tools) with no unified workflow.
Manual asset multiplication: Each master creative required manual resizing, reformatting, and localization for 40+ format/market combinations.
Version control chaos: No single source of truth for approved assets led to outdated creatives reaching production.
Approval bottlenecks: Sequential review process meant one stakeholder's vacation could delay entire campaigns.
Constraints
Strict brand governance requirements — legal sign-off required for any AI-generated imagery.
Existing agency contracts that couldn't be immediately terminated.
IT security requirements mandating on-premise or private cloud deployment.
Creative team skepticism about AI replacing their expertise.
Baseline Metric | Baseline Value |
|---|---|
Average campaign cycle time | 28 days (brief to deployment) |
Creative variants per campaign | 8-12 per SKU |
Cost per campaign | $2.4M average |
First-pass approval rate | 34% |
Strategic Objectives & KPIs
High-Level Goals
Reduce time-to-market for seasonal campaigns by at least 50%.
Cut creative production costs by 40% within 12 months.
Enable rapid response to market trends (72-hour campaign deployment capability).
Creative Goals
Maintain or improve brand consistency scores across all generated assets.
Increase creative experimentation — more concepts tested per campaign.
Empower creative directors to focus on strategy over production mechanics.
Technical Goals
Build a modular, extensible pipeline that can incorporate new AI models as they emerge.
Achieve 95%+ uptime for the production system.
Integrate seamlessly with existing DAM and project management tools.
KPIs
KPI | Target | Measurement Method |
|---|---|---|
Campaign cycle time | ≤14 days | Project management system timestamps |
Cost per campaign | ≤$1.4M | Finance tracking |
Asset variants per SKU | ≥50 | DAM asset count |
First-pass approval rate | ≥70% | Approval workflow logs |
Research & Insight
Market & Competitor Audit
(We conducted a comprehensive audit of how competitors and industry leaders were approaching AI in creative production)
Coca-Cola's 'Create Real Magic' campaign: Demonstrated consumer appetite for AI-generated brand content, but relied on one-off activations rather than systematized production.
Unilever's internal experiments: Reported 50% time savings on asset adaptation but struggled with brand consistency at scale.
Agency landscape: Major agencies were building AI capabilities but primarily as add-on services, not integrated workflows.
Audience Research
(Interviewed 18 stakeholders across the creative production chain)
Creative Directors: Frustrated by time spent on production minutiae vs. strategic thinking. Average 60% of time on non-creative tasks.
Brand Managers: Primary concern was brand safety and consistency. Skeptical of AI but open if guardrails existed.
Regional Marketing: Needed faster localization. Often waited 2-3 weeks for adapted assets.
Legal/Compliance: Required audit trail for any AI-generated content. No exceptions.
Data Analysis
(Analyzed 3 years of campaign production data)
Asset resizing/reformatting accounted for 40% of production hours.
Copy variations (headlines, CTAs) represented another 25%.
66% of revision requests were for minor adjustments (color, layout tweaks), not conceptual changes.
Highest-performing campaigns shared common visual patterns that could be codified.
Insight Statements
The production bottleneck isn't creative ideation, it's creative multiplication. The core concepts are solid; turning them into 200+ format variations is the drag.
Human creative judgment is irreplaceable at key inflection points, but delegatable for systematic variations.
Trust in AI-generated content requires visible human checkpoints, not invisible automation.
Ideation & Concept Development
Creative Brief
Design an AI-augmented creative production system that treats generative AI as a 'force multiplier' for human creativity by automating the mechanical while amplifying the meaningful. The system must be invisible when working correctly and transparent when intervention is needed.
Concept Exploration
(We explored three architectural approaches through rapid prototyping)
Full Automation Model: Brief → AI generates complete campaigns → Human review at end. Rejected: Too risky for brand safety, removed creative ownership.
AI-Assisted Tool Model: Integrate AI features into existing tools (Photoshop plugins, etc.). Rejected: Fragmented experience, no workflow unification.
Orchestrated Pipeline Model (Selected): Define human decision points, automate everything between them. AI generates options; humans curate and approve.
Ideation Methods
Design Sprints: Two 5-day sprints with cross-functional teams to prototype pipeline concepts.
Workflow Mapping: Detailed process maps of current state vs. proposed state with time estimates.
Creative Director Shadowing: Observed 3 campaign cycles to identify unstated needs and friction points.
Rapid Prototyping: Built throwaway demos to test AI model outputs against brand guidelines.
Merging Brand Narrative with Technical Feasibility
The key breakthrough was reframing the project narrative. Instead of 'AI replaces creative production,' we positioned it as 'AI handles the mechanical so creatives can focus on meaning.' This framing secured buy-in from skeptical creative directors and addressed legal concerns by emphasizing human oversight at every brand-critical decision point. We documented this as the 'Creative Multiplication Principle' — AI multiplies approved creative directions, never generates unsupervised creative strategy.
Technical Architecture & Prototyping
High-Level Architecture
Stage | AI Function | Human Gate |
|---|---|---|
Brief Intake | Parse brief, extract requirements, generate creative direction options | Creative Director selects direction |
Concept Generation | Generate 20-50 visual concepts per direction | Art Director curates top 5-10 |
Copy Generation | Generate headlines, body copy, CTAs per concept | Copywriter edits and approves |
Asset Multiplication | Generate all format/market variations from approved masters | QA spot-check (10% sample) |
Final Review | Package for deployment, generate audit report | Brand Manager + Legal sign-off |
Technology Stack
Component | Technology |
|---|---|
Orchestration | Temporal (workflow engine) + n8n (visual automation) |
Text Generation | Claude 3.5 Sonnet (prompt interpretation, copy generation) |
Image Generation | Midjourney (hero imagery) + DALL-E 3 (product shots) + Stable Diffusion XL (batch variations) |
Video Generation | Runway Gen-2 (motion from stills) |
Frontend | React + Tailwind (approval dashboard) |
Storage/DAM | AWS S3 + Bynder integration |
Infrastructure | AWS (private cloud deployment per client security requirements) |
Prototyping Approach
(Built three proof-of-concept demos over 4 weeks)
Brief-to-concept pipeline using Claude + Midjourney, demonstrating 20 visual concepts from a single brief in under 2 hours.
Copy variation engine generating 50 headline alternatives with brand voice consistency scoring.
Format multiplication prototype converting a single master creative into 12 platform formats automatically.
Each POC was presented to stakeholders for feedback before proceeding to full development.
Production & Execution
Development Workflow
(We followed an agile methodology with 2-week sprints)
Sprints 1-2: Core orchestration infrastructure (Temporal workflows, API integrations).
Sprints 3-4: Prompt engineering and model fine-tuning for brand voice.
Sprints 5-6: Approval dashboard and human-in-the-loop interfaces.
Sprints 7-8: DAM integration, audit logging, and production hardening.
Key Deliverables
Campaign Factory Platform: Web-based dashboard for campaign creation, review, and approval.
Prompt Library: 120+ tested prompts for various campaign types, stored with version control.
Brand Voice Model: Fine-tuned embeddings capturing the client's tone, vocabulary, and messaging hierarchy.
Audit System: Complete traceability from brief to final asset with timestamps and approval signatures.
Training Program: 8-hour curriculum for creative team onboarding.
Collaboration Highlights
Creative Director Partnership: Weekly working sessions with senior art directors to refine prompt strategies and visual quality thresholds.
Legal Integration: Co-designed the audit trail system with compliance team to meet regulatory requirements.
Regional Marketing: Incorporated localization feedback loops from 4 regional teams during pilot.
IT Security: Collaborated on private cloud architecture to meet data sovereignty requirements.
Key Challenges & Decisions
Challenge 1: Brand Consistency at Scale
Problem: Early image generations showed style drift — outputs were technically competent but didn't 'feel' like the brand.
Resolution: Developed a 'Brand DNA' prompt prefix system — a structured set of visual parameters (color palette references, composition rules, lighting style) prepended to every image generation prompt. Combined with negative prompts to exclude competitor visual tropes. Also implemented a secondary AI classifier trained on 500 approved brand images to score outputs before human review.
Challenge 2: Creative Team Adoption
Problem: Senior art directors initially viewed the system as a threat to their expertise and creative judgment.
Resolution: Repositioned art directors as 'AI curators' with elevated decision-making authority. They spent less time on production and more time making creative calls. Introduced 'Creative Direction Score' metric showing how their curation improved campaign performance. After 4 weeks, the most skeptical AD became the system's biggest advocate.
Challenge 3: Approval Workflow Complexity
Problem: Initial design had too many approval gates, creating new bottlenecks that offset speed gains.
Resolution: Implemented tiered approval based on asset risk level. Hero imagery required full review chain. Format variations from approved masters only needed spot-check sampling. Reduced approval touchpoints from 7 to 4 for standard campaigns, with 'express lane' for time-critical reactive campaigns.
Challenge 4: Model Provider Reliability
Problem: Third-party AI APIs (especially Midjourney) had inconsistent availability and rate limits.
Resolution: Built a provider abstraction layer with automatic failover. Primary generation through Midjourney, fallback to DALL-E 3, emergency fallback to self-hosted Stable Diffusion. Queue management system to stay within rate limits while maximizing throughput.
Final Solution
Product Overview
The AI Campaign Factory is an end-to-end creative production platform that transforms campaign briefs into deployment-ready multi-channel asset packages. Users input a strategic brief; the system generates visual concepts, copy variations, and format adaptations through an orchestrated AI pipeline with human approval gates at each critical decision point.
Core Capabilities
Brief Intelligence: Natural language processing extracts objectives, constraints, and success criteria from marketing briefs.
Concept Generation: Produces 20-50 on-brand visual concepts per creative direction in under 2 hours.
Copy Engine: Generates headlines, body copy, and CTAs calibrated to brand voice and character limits per platform.
Format Multiplication: Automatically adapts approved master creatives to 40+ format specifications across digital channels.
Localization Pipeline: Adapts messaging and imagery for regional markets with cultural sensitivity checks.
Audit & Compliance: Complete chain-of-custody documentation for every generated asset.
User Experience
The platform presents as a clean, Kanban-style dashboard where campaigns move through stages from Brief → Concepts → Copy → Formats → Review → Deploy. Each stage surfaces AI-generated options for human curation. Approvers see side-by-side comparisons, can request regenerations with modified parameters, and approve with one click. A progress bar shows real-time status across all active campaigns.
Tech Specs
Specification | Value |
|---|---|
Average concept generation time | 47 minutes for 30 concepts |
Format multiplication throughput | 200+ variants per hour |
System uptime (production) | 99.4% |
Supported output formats | 47 (social, display, video, print) |
Results & Impact
Before/After Metrics
Metric | Before | After | Change |
|---|---|---|---|
Campaign cycle time | 28 days | 8 days | −71% |
Cost per campaign | $2.4M | $960K | −60% |
Asset variants per SKU | 10 | 120+ | +12x |
First-pass approval rate | 34% | 78% | +129% |
Creative concepts tested/campaign | 3-4 | 15-20 | +5x |
Qualitative Results
Creative team satisfaction: Post-implementation survey showed 82% of creative staff felt they spent more time on 'meaningful creative work.'
Market responsiveness: Successfully deployed 3 reactive campaigns within 72 hours during pilot period.
Brand consistency: External brand audit scores improved from 7.2 to 8.4 (out of 10) post-implementation.
Agency relationships: Retained strategic agency partnerships while transitioning production work in-house.
ROI Summary
First-year savings of approximately $8.6M across 6 major campaign cycles. Platform development cost recovered within 4 months of production deployment. Projected 3-year NPV of $24M based on continued operation and expansion to additional brand portfolios.
Learnings & Next Steps
What I Would Iterate
Earlier creative team involvement: Should have embedded art directors in the technical team from sprint 1, not sprint 3.
Simpler approval tiers from start: Initial over-engineering of approval workflows cost 2 weeks of rework.
More aggressive provider abstraction: Built tighter Midjourney integration initially; should have assumed provider changes from day one.
Skills & Tools Mastered
Advanced prompt engineering: Developed expertise in chained prompts, negative prompting, and brand voice calibration.
Workflow orchestration: Deep proficiency in Temporal for complex, long-running AI workflows.
Change management for AI adoption: Learned how to navigate organizational resistance to AI-augmented workflows.
Multi-model orchestration: Practical experience combining text and image models in production pipelines.
Influence on Future Work
This project established the architectural patterns and prompt engineering methodologies I've since applied to video production pipelines, personalization engines, and creative operations dashboards. The 'human gate' framework became a template for all subsequent AI-augmented creative systems. Most importantly, it taught me that successful AI implementation is 30% technology and 70% organizational design.
Facing a creative production challenge?
I help organizations design and deliver AI-augmented creative systems that amplify human creativity and produce measurable business results. Whether you’re evaluating generative AI for the first time or scaling experiments into production, I partner with product, design, and engineering teams to build workflows, tooling, and governance that actually move the needle — let’s talk.