Lead to Cash Discovery Guide

This guide provides a structured framework for conducting discovery for AI Employees that address friction points across document-heavy and sales workflows -- from data extraction and rule validation through document generation and outbound sales.

Product Suite Overview

The Lead-to-Cash suite includes document intelligence agents and AI-powered sales tools:

AI Employee	Purpose
Data Extraction	Parses structured information from documents of any format (PDFs, scans, Word files) and prepares it for downstream consumption.
Rule Validation	Evaluates extracted data against customer-defined business rules or regulatory constraints to flag errors, omissions, or compliance risks.
RFP Writer	Generates tailored responses to RFPs by leveraging prior submissions, product knowledge, and document structure.
Document Generator	Automates creation of enterprise-ready documents such as contracts, memos, performance summaries, and customer-facing letters.
AI-SDR	Automates outbound sales prospecting: sources leads, generates personalized messaging, and executes outreach via CRM integration.

These AI Employees can be deployed independently or as a cohesive solution depending on input data complexity and pipeline maturity.

Discovery Preparation

Required Knowledge

Document formats, intake processes, and downstream output usage.
Validation rules and audit/compliance dependencies.
Sales outreach stack: enrichment, sequencing, CRM integration.
Document automation workflows (e.g., RFP lifecycle, doc generation templates).

Stakeholders to Involve

Admins managing AI configuration and user access.
Business users (SDRs, proposal managers, legal analysts).
Sales leaders and revenue operations.
Procurement, legal, and compliance stakeholders.
IT/Security responsible for integration and data governance.

Recommended Pre-Work

Collect sample input documents (contracts, RFPs, forms) and desired outputs.
Document validation rules and doc generation templates.
Audit lead sourcing tools and current sales messaging flows.
Confirm target KPIs and measurable outcomes.

End-to-End Workflow Mapping

Document and Sales Process Discovery

What to capture:

Document lifecycle stages: Intake, data extraction, validation, drafting, review, and delivery.
Sales engagement flow: How leads are sourced, segmented, contacted, and handed off to AEs.
Template and rules management: How frequently used templates and validation rules are stored and maintained.
Tool and system transitions: How data or content moves between CRM, shared drives, CMS, or via manual handoffs.
Approval and review checkpoints: Which stakeholders review or approve documents and at what stage.
Manual effort and bottlenecks: Areas where teams spend time on formatting, data entry, or chasing approvals.

Data Extraction

What to capture:

Source document formats and intake channels.
Target fields or tables routinely parsed, especially sections with the most errors today.
Volume and variety of documents processed per day or week.
Error and delay points where staff correct low-confidence outputs.
Process handoffs: who receives the output, in what format.

Rule Validation

What to capture:

Validation logic: key rules, ranges, or flags for quality or compliance.
Rule sources: where rules are defined, stored, and who owns them.
Failure handling: what happens when a record fails (escalation, rejection, manual fix).
Review checkpoints: where humans intervene to approve or override.
Recurring gaps: rules that often fail or require frequent human intervention.

RFP Writer

What to capture:

Intake methods: how RFPs arrive and are triaged or assigned.
Drafting workflows: who writes each section, whether content is reused or started fresh.
Content reuse: availability of answer banks, past responses, or shared templates.
Review cycles: how drafts are reviewed and by whom.
Collaboration process: which teams contribute and how responses are assembled.

Document Generator

What to capture:

Common document types produced across departments.
Template logic: structure, dynamic fields, conditional content.
Content sources: manual input, historical examples, system data.
Approval flows by document type.
Brand and tone controls: formatting, tone, or legal checks.

AI-SDR

What to capture:

Team structure: SDR responsibilities, quotas, relationship to AEs.
Prospecting process: how leads are selected, segmented, and approached.
Tooling stack: CRM, enrichment, sequencing, and personalization tools.
Conversion metrics: meetings booked, conversion rates, cost per lead.
Efficiency blockers: time spent on research, copy-pasting, or personalizing content.

Success Metrics

AI Employee	Metric	Description
Data Extraction	Throughput	Number of documents parsed daily.
Data Extraction	Accuracy	Percentage of correct field extractions.
Rule Validation	Precision	Percentage of rules correctly flagged.
Rule Validation	Compliance	Percentage of outputs compliant to policy.
RFP Writer	Time Saved	Average hours saved per RFP.
RFP Writer	Draft Quality	SME rating of first draft usability.
Document Generator	Creation Efficiency	Average time saved producing documents.
Document Generator	Review Success Rate	Percentage of generated content approved without edits.
AI-SDR	Meetings Generated	Meetings per SDR per month.
AI-SDR	Cost Efficiency	Cost per qualified lead.

Pre-Launch Evaluation Checklist

Golden input/output pairs validated.
Error thresholds defined for extraction and validation.
Template formats and field mappings aligned.
Integration feasibility assessed (CRM, storage, etc.).
Pilot timeline and success metrics confirmed.

Stakeholder Roles

Role	Responsibility
Executive Sponsor	Strategic alignment, budget ownership.
Proposal Owner	Oversees RFP and structured document workflows.
Content Owner / Department Lead	Manages document categories across HR, Legal, Marketing.
End Users	SDRs, proposal writers, document reviewers.
Sales / Revenue Ops	Sales metric tracking, CRM workflows.