Executive Summary
From manual feed-scanning to a governed revenue operating system
The client's outbound motion depended entirely on SDRs manually scanning public channels for buying signals — a process that was slow, inconsistent, and impossible to attribute. I led the architecture, build, and governance program for an AI revenue system that codified that manual judgment into a repeatable pipeline: one that surfaces genuine intent, enriches it, and hands reps a CRM-ready, human-approved opportunity instead of a cold guess.
The Business Problem
The client's growth plan called for a 40% increase in outbound-sourced pipeline, but the existing process could not scale. SDRs spent an estimated 60% of their working day manually scanning social and community channels for buying signals, then researching and drafting outreach one lead at a time. Capture was inconsistent across reps, attribution back to specific signals was effectively impossible, and leadership had no reliable way to measure which channels or messages were actually driving revenue.
A baseline audit at engagement kickoff quantified the gap: average prospecting time of 4.2 hours per qualified opportunity, an outreach approval bottleneck that stalled high-intent leads for 24–48 hours, and a reported pipeline-to-close conversion rate roughly 35% below the team's target. The revenue leadership team needed a system that could scale lead capture without surrendering control of who gets contacted, when, and how — a governance requirement, not just a productivity one.
The Solution
I designed and delivered a closed-loop, governance-first revenue operating system that monitors targeted public channels for buying-intent signals, classifies genuine intent using a tuned AI model, enriches qualifying accounts and contacts, and writes clean, deduplicated records directly into the CRM. Every high-intent match generates a context-rich, editable outreach draft that is routed to the assigned rep in Slack for explicit human approval — nothing is sent without a person in the loop.
This governance posture — human approval before every send, full attribution back to the originating signal, and a standardized qualification threshold applied consistently across the team — was the deciding factor in getting sales leadership and compliance to sign off on an AI-driven outbound motion.
How It Works
Ingestion
Scoped, keyword-driven monitoring of target channels with batching and cost controls, replacing hours of manual scanning per rep per day.
Qualification
AI-based intent classification with tuned thresholds and defined failure modes, applying one consistent bar across every rep and channel.
Activation
Enrichment, CRM writeback, and a Slack-delivered outreach draft — routed for human approval before a single message goes out.
Architecture & Governance
The diagram below traces how a public signal becomes a CRM-ready opportunity, with a mandatory rep approval checkpoint before any outreach is sent.
System Architecture — Signal-to-Opportunity Flow
Five stages · one mandatory human checkpoint
Scoped, keyword-driven monitoring of target channels with batching and cost controls
Intent classification with tuned thresholds, applying one consistent bar across every rep and channel
Third-party enrichment with duplicate avoidance, writing one clean record per account into the CRM
Context-rich, editable outreach draft requires explicit rep sign-off before any send
Approved message sent with full attribution back to the originating signal
Attribution & audit log runs beneath every stage — every signal, qualification decision, and approval is recorded and traceable to closed revenue.
- Ingestion pipeline with scoped queries and batching, engineered for signal quality and cost discipline at production volume.
- AI classification layer that scores genuine service intent and assigns a lead score, tuned against real conversion outcomes rather than static keyword rules.
- Enrichment via a third-party data API with duplicate avoidance, ensuring reps only ever see one clean, current record per account.
- CRM integration via centralized webhook automation, handling schema mapping and authentication so field data stays consistent across the funnel.
- Slack-based approval workflow that pairs every draft with full context and a mandatory human sign-off before send.
- Governance controls: standardized qualification thresholds, complete attribution logging, and no autonomous outreach under any circumstance.
Business Impact & ROI
- Grew qualified pipeline volume 3.4x within two quarters by converting a manual, capacity-limited process into a continuously running intent-capture system.
- Cut average prospecting time per qualified opportunity from 4.2 hours to under 90 minutes, primarily by eliminating manual feed-scanning and first-draft research.
- Reduced the outreach approval bottleneck from 24–48 hours to same-day, materially improving response rates on genuinely time-sensitive intent signals.
- Delivered a fully costed ROI model to the revenue leadership team: total program investment recovered in approximately 7 weeks of production use, driven by SDR time reclaimed and higher-quality pipeline, with a 5.1x return in year one.
- Established end-to-end attribution from public signal to closed revenue for the first time, giving leadership a defensible view of which channels actually drive pipeline.
- Standardized qualification criteria across the entire SDR team, closing a performance gap that had previously depended on individual rep judgment and experience.
My Role
I owned this engagement end-to-end as the client's AI/ML leader, operating at CTO scope: I built the business case and ROI model presented to revenue leadership, defined the operating model and success metrics (pipeline volume, prospecting time, conversion, attribution), and set the governance requirements — mandatory human approval, full attribution logging, and standardized thresholds — that made an AI-driven outbound motion defensible to sales, compliance, and the executive team alike.
I also owned the data and integration architecture end-to-end, from the ingestion pipeline through CRM writeback, and led the phased channel rollout that got a skeptical SDR team to trust and adopt the system rather than work around it. The outcome was not a point automation, but a governed operating model the client's leadership could scale, measure, and stand behind.