Proving GenAI value in policy and marketing workflows
Strategic Marketing Communications | AI Integration
A leading strategic marketing communications firm wanted to reduce the time spent manually processing 10–25 daily political newsletters to create client briefings. This process was time-consuming, inconsistent, and prone to missed insights.
I led the strategic design of an AI-enabled workflow that used LLM summarization, clustering, and conflict detection to extract key narratives and deliver daily briefings via Slack and email. I worked closely with ML and engineering partners to shape the architecture, while designing the prompt structure, tagging logic, and human-in-the-loop review process—laying the groundwork for broader AI adoption across the organization.
CLIENT
Confidential
01. Situation
Each day, teams manually reviewed dozens of political newsletters to identify client-relevant narratives. The process relied on individual interpretation, lacked consistency, and consumed significant time. Without a standardized format or shared output logic, insights were difficult to align across teams or reuse efficiently.
02. Task
Define and guide the implementation of a scalable GenAI-assisted workflow that:
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Structures summarization of key political narratives across sources
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Flags contradictions and urgency levels through model logic and review
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Tailors outputs for different internal audiences using modular prompts
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Embeds delivery into team workflows (Slack, email)
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Builds AI confidence through transparent, human-in-the-loop processes
03. Action
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Led strategic design of an end-to-end system using Claude 4 Sonnet (LLM), OpenAI embeddings, LangChain orchestration, and structured prompts
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Designed a three-phase rollout: MVP (single source), pilot (multi-source + conflict detection), and full deployment
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Built feedback loops via thumbs-up/down ratings, missed-story reporting, and weekly optimization
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Developed trust safeguards including human-in-the-loop review, source validation, contradiction transparency, and audit trails
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Embedded change management tactics: AI champions, pilot huddles, and editorial calibration against gold-standard summaries
04. Result
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80% time savings in newsletter summarization
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<5% editorial review time per day by week 8
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<2% factual error rate in production
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Enabled summaries referenced in 30%+ of client briefings
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System architecture reused in 2+ other AI initiatives
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Created a model for AI governance, evaluation, and human-AI collaboration
Illustrating the process
Artifacts from this implementation showcase the layered intelligence, tooling rationale, and strategic frameworks used to move from idea to system-level change.
Prompting for strategic clarity
Designing modular prompts aligned to client-facing use cases
This prompt architecture was something I designed in collaboration with our ML partner. It served as a shared map for aligning model behavior with editorial needs—ensuring outputs were readable, client-relevant, and trusted.
Newsletter-to-insight pipeline
A structured, multi-step system built for scale
This pipeline visualized the human–machine collaboration: what the system does, what the human reviews, and how we layered in transparency and fallback logic. I used this to align technical teams and editorial reviewers.
Onboarding and training sessions
Building AI confidence through shared rituals and review
To support adoption, we ran daily pilot huddles, team training, and live editorial reviews. These sessions created space for feedback, surfaced edge cases, and helped build comfort with AI-generated output. This human layer was key to framing the system as a co-pilot—not a threat—and enabled long-term trust and engagement.
Evaluation and feedback loop
Balancing automation with editorial judgment
This tuning process combined blind editorial review, thumbs-up/down feedback, and weekly optimization sessions. I designed the framework and cadence, while our engineers handled integration. It was key for team confidence and trust.
Key takeaway
This project bridged AI experimentation and operational need—creating a daily system that saves time, improves insight quality, and builds cross-functional confidence in AI. It also became a strategic asset: reusable, trusted, and ready to scale.
Navigator methods & frameworks used
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Prompt architecture design – for high-clarity summaries and conflict analysis
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Modular AI pipeline planning – for flexible, model-agnostic implementation
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LangChain orchestration – for chaining prompts and managing fallback logic
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AI evaluation framework – combining human judgment and performance metrics
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Change management principles – for adoption, literacy, and trust calibration
This became a model for how we can responsibly adopt AI across teams. It wasn’t just a tool; it gave us a way to start feeling more comfortable embracing AI.
– Internal stakeholder, Strategic Marketing Communications Agency