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AI Automated Inbound - Playbook

1. Definition​

What it is: A technical implementation project that deploys AI-powered chat and real-time meeting booking on the website to engage, qualify, and route high-intent visitors to Sales instantly—transforming website traffic into a predictable pipeline channel through tools like Qualified.com.

What it is NOT: Not a general chatbot or support bot implementation (that's customer service). Not a Marketing Automation Platform setup (that's lead nurturing workflows). Not a CRM implementation project. Not a static lead form optimization.

2. ICP Value Proposition​

Pain it solves: Sales teams lose 70% of inbound leads due to slow response times. Static forms create friction and delay follow-up. High-intent buyers leave the website before connecting with Sales, and reps waste time chasing unqualified visitors while hot leads go cold.

Outcome delivered: Real-time AI-powered engagement that instantly identifies visitors, qualifies them through conversational flows, and books meetings with the right rep—reducing speed-to-lead from hours/days to minutes. MQL-to-SQL conversion increases 2-3X, and meetings booked from inbound traffic double.

Who owns it: VP of Marketing or VP of Demand Generation, with RevOps supporting integration and Sales providing input on qualification criteria and routing rules.

3. Implementation Procedure​

Part 1: Discovery & Current State Assessment​

Step 1: Conduct Stakeholder Discovery Session​

Step Overview: Meet with Marketing, Sales, and RevOps leadership to align on inbound engagement goals and define success metrics. End state: Documented goals for MQL-to-SQL conversion, speed-to-lead targets, and meeting booking rates.

  • Schedule 60-90 minute discovery session with Marketing, Sales Ops, and Sales leadership
  • Define primary inbound engagement goals (e.g., 2X meetings booked, <5 min speed-to-lead)
  • Document current MQL-to-SQL conversion rate as baseline
  • Identify key buyer segments and their typical on-site behaviors
  • Clarify budget and timeline constraints for tool selection
  • Capture stakeholder expectations for AI vs. human handoff scenarios

Step 2: Audit Current Inbound Workflows​

Step Overview: Assess existing inbound processes, website visitor data, and qualification criteria to identify gaps and opportunities. End state: Gap analysis showing current state vs. desired state with specific improvement areas.

  • Map current lead capture mechanisms (forms, demo requests, contact pages)
  • Pull speed-to-lead metrics from CRM (time from form submission to first response)
  • Document existing qualification criteria (BANT, MEDDIC, or custom framework)
  • Identify high-traffic pages where inbound intent signals are strongest
  • Review current routing rules and SLA compliance rates
  • Quantify missed opportunities (e.g., "40% of demo requests never get follow-up within 24 hours")

Step 3: Evaluate and Select AI Inbound Tool​

Step Overview: Compare AI inbound platform options against client's tech stack, requirements, and budget. End state: Tool selected (e.g., Qualified.com, Drift) with procurement approved.

  • Document client's current tech stack (CRM, MAP, website platform, existing chat tools)
  • Evaluate options: Qualified.com, Drift, Intercom, HubSpot Conversations
  • Compare on: CRM compatibility, AI capabilities, meeting booking, analytics depth, cost per seat
  • Assess integration complexity with existing systems
  • Present recommendation with ROI projections to stakeholders
  • Get budget approval and initiate procurement/contract process

Part 2: Buyer Journey & Playbook Design​

Step 1: Map Buyer Segments and On-Site Behaviors​

Step Overview: Define key buyer personas and the on-site behaviors that indicate buying intent for each segment. End state: Documented buyer segment map with intent signals and qualification criteria per segment.

  • Identify 3-5 primary buyer segments (e.g., enterprise vs. SMB, by industry, by role)
  • Map high-intent behaviors per segment (pricing page visits, demo page, security pages)
  • Define qualification criteria for each segment using existing framework
  • Document account signals (company size, industry, tech stack) that indicate fit
  • Create segment-specific routing rules (e.g., enterprise → AE, SMB → SDR)
  • Validate segment definitions with Sales leadership

Step 2: Design Conversational Playbooks​

Step Overview: Create AI chat playbooks with qualification questions, value prompts, and objection handling for each buyer segment. End state: Complete conversational flow documents ready for tool configuration.

  • Draft 3-5 conversational playbooks aligned to buyer segments and intent levels
  • Define opening messages based on page context (pricing vs. blog vs. product pages)
  • Create qualification question sequences (3-5 questions per flow)
  • Write value prompts and benefit statements for key objections
  • Define escalation triggers for human handoff (complex questions, high-value accounts)
  • Include meeting booking CTAs at optimal points in conversation flow
  • Document fallback responses for out-of-scope questions

Step 3: Define Routing Rules and Escalation Logic​

Step Overview: Establish routing rules for hot leads, existing customers, target accounts, and non-fit visitors. End state: Complete routing matrix with owner assignments and SLA targets.

  • Create routing matrix: segment × intent level × owner/queue
  • Define rules for existing customers (route to CSM or dedicated queue)
  • Set up ABM-specific routing for target accounts (priority queue, specific AE)
  • Establish non-target visitor handling (resource offer, nurture path, graceful exit)
  • Define SLA targets for each queue (e.g., hot leads <2 min, standard <5 min)
  • Document after-hours routing logic (meeting booking only vs. queue for morning)

Part 3: Data Integration & Enrichment Setup​

Step 1: Connect AI Platform to CRM​

Step Overview: Establish bidirectional connection between Qualified.com (or selected tool) and CRM with proper API permissions. End state: AI platform connected to CRM with leads/contacts syncing correctly.

  • Configure OAuth connection to Salesforce or HubSpot
  • Grant required API permissions (read/write leads, contacts, accounts, activities)
  • Map AI platform fields to CRM standard and custom fields
  • Set up lead/contact creation rules (create new vs. update existing)
  • Configure account matching logic for known visitors
  • Test bidirectional sync with sample records
  • Document integration settings for client handoff

Step 2: Set Up Data Enrichment Integration​

Step Overview: Connect data enrichment tools to enable real-time visitor identification and account scoring. End state: Enrichment flowing into AI platform for personalized, data-driven conversations.

  • Connect enrichment tools (Clearbit, ZoomInfo, 6sense, or native enrichment)
  • Configure reverse IP lookup for company identification
  • Set up contact enrichment for known visitors (title, seniority, department)
  • Define enrichment triggers (on page load vs. on engagement)
  • Map enriched data to AI platform variables for personalization
  • Test enrichment accuracy with sample visitors

Step 3: Integrate Marketing Automation Platform​

Step Overview: Connect AI platform to MAP for lead scoring sync and nurture workflow triggers. End state: Leads scored correctly with automated nurture enrollment based on AI interactions.

  • Connect to HubSpot, Marketo, or Pardot via native integration or API
  • Configure lead score sync from MAP to AI platform (for prioritization)
  • Set up activity logging from AI platform to MAP (conversation events)
  • Define nurture enrollment triggers based on AI qualification outcomes
  • Configure lead source/campaign tracking for attribution
  • Test end-to-end flow: visitor → AI chat → CRM lead → MAP nurture

Part 4: Build, Configure & Test AI Experiences​

Step 1: Configure AI Chatbot Foundation​

Step Overview: Set up core AI chatbot configuration including appearance, behavior settings, and base AI training. End state: AI chatbot deployed on website with foundational settings in place.

  • Configure chatbot appearance (colors, logo, positioning) to match brand
  • Set up bot personality and tone of voice aligned with company brand guidelines
  • Configure AI training with company-specific context (product info, pricing tiers, competitors)
  • Set up knowledge base connections for accurate responses
  • Define bot behavior by page type (aggressive on pricing, passive on blog)
  • Configure mobile vs. desktop experience differences
  • Set initial AI confidence thresholds for escalation

Step 2: Implement Conversational Playbooks​

Step Overview: Build out the designed conversational flows in the AI platform with all qualification paths and routing logic. End state: All playbooks configured and ready for testing.

  • Build each conversational playbook in the platform
  • Configure conditional branching based on visitor responses
  • Set up qualification data capture (company size, use case, timeline, budget)
  • Implement routing logic per playbook (trigger assignments, queue selections)
  • Configure meeting booking integration (calendar connections, availability rules)
  • Set up real-time alerts for high-priority visitors (Slack, email, SMS)
  • Add personalization tokens using enrichment data

Step 3: Configure Meeting Booking Experience​

Step Overview: Set up real-time meeting booking with calendar integrations and availability rules. End state: Visitors can book meetings directly from AI conversations with correct rep calendars.

  • Connect rep calendars (Google Calendar or Outlook) to AI platform
  • Configure availability windows and buffer times per rep
  • Set up round-robin or weighted distribution logic
  • Define meeting types (15 min intro, 30 min demo, 60 min deep dive)
  • Configure confirmation emails and calendar invites
  • Set up rescheduling and cancellation workflows
  • Test booking flow end-to-end with multiple rep calendars

Step 4: QA and Test All AI Experiences​

Step Overview: Conduct comprehensive testing of all conversational flows, routing, and integrations before launch. End state: All flows tested, issues resolved, and system ready for pilot launch.

  • Test each playbook flow manually (happy path and edge cases)
  • Verify routing delivers to correct owner/queue for each scenario
  • Test CRM record creation and field mapping accuracy
  • Verify enrichment data appearing correctly in conversations
  • Test meeting booking across all rep calendars and time zones
  • Validate mobile experience functionality
  • Test after-hours behavior and fallback scenarios
  • Document any issues and resolve before pilot

Part 5: Rollout, Training & Enablement​

Step 1: Launch Pilot with Select Rep Group​

Step Overview: Deploy AI inbound to a subset of traffic/reps to validate performance before full rollout. End state: Pilot running with initial data on engagement, meeting booking, and rep feedback.

  • Select 3-5 reps for pilot group (mix of SDR and AE if applicable)
  • Configure traffic routing to limit AI exposure (e.g., 25% of traffic)
  • Brief pilot reps on what to expect and how to handle AI-sourced leads
  • Monitor first 48-72 hours closely for issues
  • Collect rep feedback on lead quality and handoff experience
  • Track pilot metrics: engagement rate, qualification rate, meetings booked
  • Identify and resolve any issues before scaling

Step 2: Train Sales and Marketing Teams​

Step Overview: Conduct training sessions for Sales and Marketing on using AI platform insights, notifications, and chat handoff. End state: Teams trained and confident using the new system.

  • Schedule 45-minute training session with full Sales team
  • Cover: how AI qualifies leads, real-time notification handling, chat takeover process
  • Train on using AI platform dashboard for visitor insights
  • Demonstrate how to access conversation transcripts in CRM
  • Create quick-reference guide for common scenarios
  • Train Marketing on monitoring AI engagement analytics
  • Record training session for onboarding new reps

Step 3: Launch Full Rollout​

Step Overview: Expand AI inbound to all traffic with full routing to all reps. End state: AI chatbot and meeting booking live across website with all reps receiving leads.

  • Expand traffic routing to 100%
  • Enable all reps in routing pool
  • Configure monitoring dashboards for real-time performance tracking
  • Set up daily/weekly automated reports to stakeholders
  • Establish Slack channel for issue escalation and feedback
  • Communicate launch internally with expectations and SLAs

Part 6: Optimization & Handoff​

Step 1: Establish Performance Monitoring Dashboards​

Step Overview: Build dashboards tracking key AI inbound metrics for ongoing optimization. End state: Dashboards live showing engagement, conversion, and speed-to-lead metrics.

  • Create AI platform native dashboard with key metrics
  • Build CRM reports for AI-sourced lead conversion tracking
  • Set up speed-to-lead measurement (form submit to first response)
  • Configure meeting booking funnel metrics (booked → held → converted)
  • Create comparison view: AI-sourced vs. traditional form leads
  • Set up automated weekly report delivery to stakeholders

Step 2: Conduct 30/60/90 Day Reviews​

Step Overview: Review AI inbound performance at regular intervals and refine playbooks based on data. End state: Documented optimizations implemented with measurable improvement.

  • Schedule 30-day review meeting with stakeholders
  • Analyze: which playbooks perform best, where visitors drop off, routing efficiency
  • Identify underperforming segments or flows for optimization
  • Review AI response accuracy and escalation patterns
  • Adjust qualification questions based on Sales feedback
  • Refine routing rules based on conversion data
  • Document changes and expected impact
  • Repeat at 60 and 90 days with deeper analysis

Step 3: Hand Off to Client​

Step Overview: Transfer ownership, documentation, and ongoing optimization playbook to client team. End state: Client self-sufficient with admin access, runbooks, and clear optimization process.

  • Transfer admin credentials to client RevOps/Marketing Ops
  • Deliver documentation package (configuration settings, playbook logic, integration details)
  • Create optimization runbook for ongoing refinement
  • Document troubleshooting procedures for common issues
  • Train client admin on making configuration changes
  • Schedule 90-day check-in for questions and advanced optimization
  • Close out project with final performance summary

4. Dependencies & Inputs​

What must exist before starting:

  • Website with sufficient traffic to justify AI investment (typically 10K+ monthly visitors)
  • CRM in place (Salesforce or HubSpot) with leads/contacts configured
  • Defined ICP and qualification criteria (even if informal)
  • Sales team with capacity to handle increased inbound leads
  • Budget approval for AI platform subscription

What client must provide:

  • Admin access to website/CMS for script installation
  • Admin access to CRM for integration setup
  • Access to Marketing Automation Platform if applicable
  • Rep calendar access or IT coordination for calendar connections
  • Sales leadership time for playbook design and routing decisions
  • 3-5 reps for pilot program participation

5. Common Pitfalls​

  • Poor integration causing data gaps: 40% of automation failures stem from integration issues. → Mitigation: Test bidirectional sync thoroughly before launch with sample records, and audit data flow weekly during first month.

  • Over-automating human touchpoints: Trying to make AI handle complex or sensitive conversations frustrates buyers. → Mitigation: Define clear escalation triggers and ensure human handoff is seamless; don't retrofit AI into interactions that need human nuance.

  • Vanity metrics fixation: Focusing on engagement rates without connecting to revenue outcomes. → Mitigation: Always track through to pipeline and closed-won; report on AI-sourced meetings held and converted, not just booked.

  • Set-and-forget AI training: AI chatbot accuracy degrades without continuous feedback and training. → Mitigation: Establish weekly review of AI responses and regular refinement cadence; OpenAI improved accuracy from 60% to 98% through continuous training loops.

6. Success Metrics​

  • Leading Indicator: Speed-to-lead under 5 minutes for AI-engaged visitors; meeting booking rate from AI conversations >15%
  • Lagging Indicator: 2X increase in MQL-to-SQL conversion rate; measurable pipeline sourced from AI inbound within 90 days