Get Value Fast. Build the Foundation.

Experience AI Working in HubSpot

Most AI implementations fail because they're bolted onto broken foundations. AIRops is different. We deploy working AI agents, clean your data, and configure real automation—in weeks, not months.

Let's Talk About Fit View the Framework

What You Get in 6-8 Weeks

  • Working AI agents from day one
  • Cleaner, more complete data
  • Automation handling work humans shouldn't do
  • Foundation that enables everything that comes next

And here's what happens next: once you see AI actually working, you'll know exactly what you want your HubSpot to become.

The Experience

Week 2
First agents running
Week 4
Data visibly cleaner
Week 6
Automation handling real work
Week 8
Full infrastructure deployed

The Fundamental Shift

This isn't about making RevOps more efficient. It's about rethinking the work entirely.

Traditional RevOps

Teams do work

AIRops

Teams design systems that do work

Manually builds workflows

Designs workflow engines that generate themselves

Assembles dashboards

Maintains intelligence layers that interpret data continuously

Responds to GTM requests

Builds AI agents and products that serve GTM autonomously

Cleans data manually

Deploys systems that maintain data quality automatically

Diagnoses errors case by case

Creates monitoring that predicts and prevents issues

The 90-Day Framework

A practical roadmap for making the transition within HubSpot.

01

Phase 01: Foundation

Days 1-30

Objective: Establish the infrastructure for AI-powered operations

Week 1-2: System Audit & Data Foundation

Data Quality Assessment
  • Deploy HubSpot's Data Quality Command Center to identify duplicates, incomplete associations, and sync errors
  • Activate Breeze Data Agent to continuously monitor and flag data issues
  • Document current data sources, sync patterns, and known quality gaps
  • Establish baseline metrics: record completeness %, duplicate rate, association coverage
Workflow Inventory
  • Map all existing workflows across Marketing, Sales, and Service Hubs
  • Categorize by type: lead routing, lifecycle stage, notification, data management
  • Identify redundant, conflicting, or outdated workflows
  • Calculate manual hours currently spent maintaining workflows

Week 3-4: Breeze Foundation Deployment

Breeze Assistant Activation
  • Enable Breeze Assistant across all user roles
  • Configure data access permissions and privacy controls
  • Train team on prompt-based interaction patterns
  • Document initial use cases: CRM summarization, quick analysis, content drafting
Breeze Intelligence Setup
  • Activate data enrichment for contacts and companies
  • Configure buyer intent tracking on key website pages
  • Enable form shortening for progressive profiling
  • Establish enrichment quality benchmarks

Phase 01 Success Metrics

Metric Target Measurement
Data completeness 85%+ on key fields Data Quality Command Center
Duplicate rate <5% Deduplication report
Breeze adoption 100% team enabled Usage analytics
Workflow inventory 100% documented Audit spreadsheet
02

Phase 02: Agent Deployment

Days 31-60

Objective: Deploy AI agents that autonomously handle GTM workflows

Week 5-6: Customer Agent & Knowledge Base Agent

Breeze Customer Agent Configuration
  • Train on website content, knowledge base articles, and up to 1,000 pages
  • Configure escalation rules and human handoff triggers
  • Enable across channels: chat, email, WhatsApp, Facebook Messenger
  • Set up lead qualification and meeting booking workflows
  • Define guardrails for sensitive topics and compliance requirements
Breeze Knowledge Base Agent Setup
  • Connect to support tickets and customer conversations for knowledge extraction
  • Configure article drafting workflow with human review gates
  • Establish gap identification process: unanswered questions → article queue
  • Link Customer Agent to Knowledge Base Agent for continuous improvement loop

Week 7-8: Prospecting Agent & Content Agent

Breeze Prospecting Agent Configuration
  • Define ideal customer profile criteria in Breeze Studio
  • Configure signal detection: buying signals, engagement patterns, timing triggers
  • Set up personalized email outreach templates aligned to brand voice
  • Establish approval workflows for high-value accounts
  • Create selling profiles for different products/buyer personas
Breeze Content Agent Activation
  • Configure brand voice, tone, and messaging guidelines
  • Set up content workflows: blog → social → email repurposing
  • Define audience segmentation for personalized content delivery
  • Establish quality review process and publishing approval gates

Phase 02 Success Metrics

Metric Target Measurement
Support ticket resolution 50%+ via Customer Agent Service Hub analytics
Knowledge base coverage 30% new articles generated KB Agent dashboard
Prospecting efficiency 4+ hours saved per rep/week Time tracking comparison
Content production 3x output with same resources Content calendar metrics
03

Phase 03: System Integration

Days 61-90

Objective: Create unified AI ecosystem with cross-team intelligence

Week 9-10: Custom Agents & Marketplace Expansion

Breeze Studio Custom Agent Development
  • Identify 3-5 high-impact custom agent opportunities specific to your business
  • Build ICP analysis assistant for sales team targeting
  • Create market research agent for competitive intelligence
  • Develop deal analysis assistant for pipeline review
  • Configure without code using Breeze Studio's intuitive interface
Marketplace Agent Deployment
  • Evaluate additional HubSpot-built agents from Breeze Marketplace
  • Deploy RFP Agent for proposal automation
  • Activate Company Research Agent for account intelligence
  • Implement Social Media Agent for channel management

Week 11-12: Unified Intelligence Layer

Cross-Team AI Coordination
  • Configure agents to share context via Smart CRM foundation
  • Establish unified metrics dashboard across Marketing, Sales, Service
  • Create joint AI use case reviews between teams
  • Define shared KPIs that span the entire customer journey
Data Hub Integration
  • Connect external data sources through Data Hub for complete AI context
  • Sync data warehouse, spreadsheets, and external apps
  • Enable 360-degree AI ecosystem: Breeze + Smart CRM + Data Hub + Commerce Hub
  • Configure automated data quality monitoring across all sources

Phase 03 Success Metrics

Metric Target Measurement
Custom agents deployed 3+ business-specific agents Breeze Studio inventory
Cross-team data visibility 100% unified view Dashboard coverage audit
Manual operations reduction 60%+ decrease Time tracking comparison
AI-driven decisions 5+ daily per team Usage analytics

Team Role Evolution

AIRops fundamentally changes what RevOps teams do—not eliminates what they do.

RevOps Manager

Traditional Focus

  • Building workflows
  • Creating reports
  • Managing data

AIRops Focus

  • Designing agent strategies
  • Defining AI guardrails
  • Orchestrating human-AI teams

HubSpot Admin

Traditional Focus

  • System configuration
  • User management
  • Technical troubleshooting

AIRops Focus

  • AI model training
  • Agent customization
  • Integration architecture

Data Analyst

Traditional Focus

  • Report building
  • Data cleaning
  • Manual analysis

AIRops Focus

  • AI output validation
  • Pattern recognition
  • Strategic interpretation

Marketing Ops

Traditional Focus

  • Campaign execution
  • List management
  • Email building

AIRops Focus

  • Content strategy
  • Agent orchestration
  • Audience intelligence

Sales Ops

Traditional Focus

  • Pipeline management
  • Territory planning
  • Comp administration

AIRops Focus

  • Signal intelligence
  • Deal coaching
  • Revenue prediction

Common Pitfalls to Avoid

The traps that derail AIRops transformations—and how to avoid them.

Treating AIRops as "RevOps with Automation"

The trap: Layering AI tools on top of broken processes. AIRops isn't about making bad workflows faster—it's about rethinking what work needs to exist at all.

The fix: Before automating any process, ask: "Does this process need to exist, or is AI making the underlying activity obsolete?"

Deploying Agents Without Guardrails

The trap: Letting AI agents operate without clear boundaries. "Full automation" is rarely the goal—augmented intelligence is.

The fix: Every agent needs defined escalation triggers, human approval workflows for high-stakes decisions, compliance guardrails, and clear success metrics.

Ignoring Data Quality

The trap: Deploying AI on dirty data. AI amplifies data quality issues—garbage in, garbage out at scale.

The fix: Phase 1 data foundation isn't optional. You cannot skip to agent deployment without clean, connected, complete data.

Underestimating Change Management

The trap: Focusing only on technology. AIRops changes job descriptions, daily workflows, and team structures.

The fix: Invest in training, communication, and celebrating early wins. Without proper change management, adoption stalls.

Measuring AIRops Success

True success isn't measured by AI tool adoption—it's measured by business outcomes enabled by AI.

Efficiency Metrics

  • Hours saved per team member per week
  • Manual workflow reduction percentage
  • Time-to-response for customer inquiries
  • Content production velocity

Quality Metrics

  • Data accuracy and completeness rates
  • Customer satisfaction scores (post-AI interaction)
  • Agent accuracy rate (correct resolutions)
  • Human escalation rate (lower is often better)

Business Outcomes Metrics

  • Pipeline velocity improvement
  • Lead-to-customer conversion rate
  • Customer retention rate
  • Revenue per employee

Transformation Metrics

  • Percentage of decisions informed by AI
  • Team capacity reallocation (from manual to strategic)
  • New capabilities enabled (things you couldn't do before)
  • Time spent on strategic work vs. operational work

What Comes After AIRops

AIRops builds the foundation. Here's what becomes possible next.

Continuous Agent Optimization

Agents learn from every interaction. Establish weekly review cadences to analyze agent performance, identify training gaps, and expand capabilities based on real-world usage.

Full Value-First Transformation

Once you've experienced AI working, you'll see what's possible. Many organizations choose to pursue full Customer Value Platform architecture—the natural next step.

Strategic Evolution

Move beyond operational efficiency to strategic intelligence. Use AI-generated insights to inform product development, market positioning, and customer strategy.

New Agent Development

As HubSpot releases new agents in Breeze Marketplace, evaluate and deploy those that address emerging needs. Build custom agents for increasingly specific use cases.

Is AIRops Right For You?

AIRops is for you if...

  • You want AI value fast but don't want to commit to full transformation yet
  • You're frustrated with failed AI initiatives or vaporware promises
  • You need to show value to leadership quickly
  • You want working systems, not slide decks about "digital transformation"

Consider Feasibility Assessment first if...

  • You're not yet on HubSpot or considering migration
  • Your business model is highly complex (dealer/distributor, subscription hybrid)
  • You've had multiple failed implementations in the past

Investment

$15,000 - $40,000

6-8 week engagement

Range reflects complexity: organizational size, number of integrations, current data foundation state, and custom agent requirements.

What's included: Breeze agent configuration, data cleanup, workflow automation, team enablement, and the foundation for everything that comes next.

No surprises: We scope together, agree on deliverables, and you know the investment before we start.

Let's Talk About Fit

We'll tell you honestly whether AIRops is right for your situation—and what the path to working AI infrastructure would look like.

Start a Conversation See All Services