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Implementation Guide

UBC Playbook

From Data Requests to Embedded Intelligence

A step-by-step guide to deploying Breeze AI agents, configuring intelligence workflows, and embedding context where decisions happen.

4
Parts
12
Sections
4-6
Hours
Requires: Unified Customer View + Unified Revenue View

Press or swipe to begin

PART 1

Before You Build

UBC requires unified data. AI agents can't provide intelligence if underlying data is fragmented.

1.1

Data Foundation Check

Verify UCV and URV are complete. AI needs unified customer and revenue data to surface meaningful patterns.

  • • Customer data unified
  • • Revenue data connected
  • • Associations working
~15 min
1.2

Intelligence Use Cases

Identify key decision points where embedded intelligence would change outcomes.

  • • Customer calls
  • • Support triage
  • • Leadership decisions
~20 min
1.3

AI Readiness Assessment

Evaluate organizational readiness to trust and act on AI-powered intelligence.

  • • Trust in AI recommendations
  • • Change capacity
  • • Decision empowerment
~15 min

Critical Dependency

Don't start UBC until UCV + URV Foundations are validated. AI agents need complete data to provide meaningful intelligence.

PART 2

Breeze AI Configuration

Deploy the AI agents that will provide embedded intelligence across your operation.

2.1

Breeze Copilot Setup

Natural language query interface

30 min

2.2

Customer Agent

AI-powered support intelligence

25 min

2.3

Content Agent

Context-aware content creation

20 min

2.4

Prospecting Agent

Pattern-based lead intelligence

20 min

2.5

Social Agent

Social monitoring and sentiment intelligence

15 min

Agent Philosophy

AI agents amplify human capability, not replace human judgment. They surface intelligence; people make decisions.

PART 3

Intelligence Workflows

Create automated workflows that surface intelligence at decision points.

3.1

Pattern Alerts

Workflows that proactively surface concerning or promising patterns before they become obvious.

  • • Churn risk detection
  • • Expansion signals
  • • Engagement changes
~30 min
3.2

Context Enrichment

Automated context injection into records so intelligence is visible where work happens.

  • • AI summaries on records
  • • Similar situation links
  • • Health score displays
~25 min
3.3

Knowledge Capture

Systematically capture successful approaches so organizational learning compounds.

  • • Resolution tagging
  • • Playbook suggestions
  • • Pattern documentation
~20 min

Intelligence Flow

Pattern Detection → Context Enrichment → Decision Support → Knowledge Capture → Loop

PART 4

Validation & Milestones

How do you know if it's working? Each milestone has specific validation criteria.

4.1

Foundation: "Intelligence Accessible"

AI agents deployed and functional. Teams can ask questions in natural language and get intelligent answers.

Test: "What customers show similar patterns to ones that churned last quarter?"

Answer should come from Copilot in <30 seconds

4.2

Capability: "Intelligence Trusted"

Organization trusts and acts on AI insights. Proactive interventions become the norm.

Test: "How many customer issues were addressed proactively this month?"

Should be measurable and trending upward

4.3

Multiplication: "Intelligence Compounding"

Intelligence infrastructure enables decisions competitors cannot match. Learning accelerates.

Indicators: Teams cite AI intelligence in decision discussions; new hires productive faster

USE CASES

Start With High-Impact Decisions

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Customer Conversations

Sales and success calls with real-time context

Intelligence Needs:

Usage patterns Health signals Similar situations
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Support Triage

Faster resolution with proven approaches

Intelligence Needs:

Similar tickets Resolution history Customer context
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Leadership Decisions

Strategic questions answered instantly

Intelligence Needs:

Pattern analysis Trend synthesis Risk visibility

Prioritization Principle

Start with decisions that happen frequently and have measurable impact.

TIMELINE

Realistic Timeline

W1

Week 1: Assessment & Planning

Complete Part 1. Verify data foundation, identify use cases, assess AI readiness.

W2-3

Weeks 2-3: Breeze Configuration

Complete Part 2. Deploy AI agents for prioritized use cases.

W4-5

Weeks 4-5: Intelligence Workflows

Complete Part 3. Build pattern alerts, context enrichment, knowledge capture.

W6+

Week 6+: Validation & Trust Building

Complete Part 4. Run validation tests, build organizational trust in AI intelligence.

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Ready to Build?

The interactive playbook guides you through every step with checklists, configuration cards, and validation tests.

Track your progress. Build trust incrementally. Transform decision-making.

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