AI Workflow for Marketing Request Intake and Brief Triage

This workflow directly attacks the costly operational bottleneck where marketing teams waste days manually chasing stakeholders for campaign details, checking capacity, and formatting briefs. By implementing an autonomous intake layer, you convert vague requests into fully-formed, actionable briefs, reducing campaign setup time from weeks to hours. The business value is clear: faster time-to-market for revenue-generating activities and a 70-80% reduction in administrative labor for planners and traffic managers, allowing them to focus on strategic work instead of data entry and email triage.

Implementation integrates with existing request portals, CRM (Salesforce), and marketing resource management (MRM) systems like Workfront. The orchestrator, built on LangGraph, coordinates specialized agents for qualification, data gathering via email/calendar APIs, and capacity checks against resource planning tools. Critical controls include human-in-the-loop approval gates for high-budget or non-standard requests, along with full audit trails and exception routing to a dedicated operations queue. This creates a scalable, governed architecture that plugs directly into your current marketing operations stack.

The workflow automates the administrative drag of marketing intake, where requests arrive via forms, email, or Slack with inconsistent detail. A specialized agent qualifies the request against business rules, identifies missing information, and engages the requester to gather it. This eliminates repetitive back-and-forth, accelerates campaign initiation, and ensures planning teams receive fully-formed briefs, directly improving marketing throughput and reducing time-to-launch by days.

Implementation integrates with ServiceNow or Jira for ticketing, Salesforce for account context, and a data warehouse for historical capacity analysis. The orchestration layer, built with LangGraph, manages state and exception routing. Critical controls include human-in-the-loop gates for high-value or non-standard requests, automated audit logging for governance, and observability dashboards to monitor queue health and agent performance, ensuring the system scales without losing operational oversight.

Phase 1 delivers immediate ROI by automating the initial request capture and triage. An orchestration layer, built with LangGraph or a similar framework, integrates with your existing ticketing system (e.g., Jira, ServiceNow) or intake form. An initial classification agent qualifies the request against predefined campaign types and business rules, automatically rejecting or flagging non-marketing items. This first layer alone can eliminate 30-40% of manual sorting effort, freeing strategic staff from administrative noise and providing a clear baseline for capacity assessment.

Phase 2 introduces the information-gathering and capacity-checking agents, connecting to CRM, MRM, and financial systems. The system proactively requests missing details from stakeholders via email or chat, structures the data, and assesses it against live team capacity and budget guardrails. Approved, fully-formed briefs are routed directly to the appropriate planning team in your marketing resource management (MRM) platform, while capacity conflicts trigger automated stakeholder notifications. This phase cuts the average request-to-brief cycle time by over 60%, directly accelerating time-to-market for revenue-critical campaigns.

A custom marketing request intake workflow automates the qualification, enrichment, and routing of campaign briefs, but its value is only realized if deployed safely. Governance starts with immutable audit logs for all agent decisions and data accesses, integrated with your existing IAM and SIEM. Approval gates are embedded at critical junctures: before a brief is auto-assigned to a high-cost channel, before a request is rejected for insufficient information, and before any system-generated communication is sent to a stakeholder. This ensures human oversight where financial or reputational risk exists, while automating 80% of routine triage.

Phased rollout is non-negotiable. Phase 1 deploys the intake agent in shadow mode, logging its proposed actions without execution, to tune logic and build confidence. Phase 2 enables automation for a single, low-risk request type (e.g., social media boost requests) with a mandatory post-action human review checkpoint. Phase 3 expands to all standard digital campaign requests, removing the review checkpoint but retaining the high-budget approval gate and real-time performance dashboards in tools like Datadog or New Relic. This measured approach de-risks adoption, aligns change management, and proves ROI before full-scale deployment.

This workflow automates the high-volume, repetitive process of qualifying inbound marketing requests from sales, product, or regional teams. It eliminates the manual back-and-forth to gather missing information, assesses requests against team capacity and strategic goals, and ensures only fully-formed, prioritized briefs reach human planners. The operational upside comes from slashing request-to-brief cycle times by 60-80%, freeing strategic staff from administrative triage, and creating a structured, auditable pipeline that improves planning throughput and resource allocation.

Implementation integrates with existing MRM, project management (Asana, Jira), and communication platforms (Slack, Teams). The LLM-based triage agent uses RAG over brand guidelines and historical briefs to assess completeness. Key controls include human-in-the-loop approval gates for capacity conflicts, exception routing for ambiguous requests, and full audit trails in the observability layer. Data flows are designed to handle poor-quality inputs gracefully, with clarification loops and fallback to a managed exception queue, ensuring no request is lost and governance is maintained.