AI in HubSpot: Automatically answer tickets with chatbots

AI-powered chatbots have transformed customer service management, and HubSpot has made a significant leap forward with the launch of Breeze AI. Companies can no longer rely solely on human teams to handle the volume of support inquiries, especially when many of these requests are repetitive and could be resolved automatically.

Organizations face the ongoing challenge of providing continuous support while maintaining service quality and controlling operational costs. Customer service teams are frequently overwhelmed by inquiries that come in around the clock, many of which follow predictable patterns that intelligent systems can handle efficiently. According to recent data from HubSpot, companies that implement AI chatbots are able to automatically resolve more than 50% of their support tickets, representing a significant reduction in manual workload.

HubSpot Breeze AI represents the natural evolution of customer service automation, integrating advanced natural language processing capabilities, machine learning, and contextual access to enterprise knowledge bases. This technology not only automates responses but also continuously learns from interactions to improve service quality and identify opportunities for optimization within support processes.

>> How to automate business processes with HubSpot Workflows and Sequences <<

• What does it mean to automate ticket handling?
• The pillars of Breeze AI in conversational automation
• Real capabilities of HubSpot chatbots
• Success stories
• Practical implementation guide
• The ICX approach
• Conclusions
  
  

   

What does it mean to automate ticket handling?

Automating ticket handling goes far beyond simply responding with a predefined message. It’s about building a system capable of interpreting a customer’s request, understanding its context, finding an appropriate solution, and delivering it in a conversational, empathetic way. This transformation turns customer support into a seamless, always-on process even when agents are unavailable.

In practice, this kind of automation combines multiple layers of technology. First, a conversational interface allows users to initiate requests from any digital channel, such as a website, mobile app, or even WhatsApp. Second, there’s a logic engine that makes sense of the conversation, interpreting the user’s needs even when they are expressed with errors, ambiguities, or informal language. Finally, a response structure is connected to reliable data sources—a knowledge base, a history of previous tickets, or an automated workflow that enables actions to be executed without human intervention.

When a customer writes “my order never arrived,” the ideal system should not just reply with a generic, “Sorry, we’re looking into your case.” It should recognize this is a delivery claim, check if there are any pending orders on the customer’s account, verify shipping status in real time, and provide a specific answer: “I see your order #1298 was delivered yesterday at 3:45 pm to your office. Would you like us to follow up with the carrier?” That is the real promise of automated service.

This level of response is only possible if the system is able to interpret natural language, connect to distributed data, and execute logical decisions in other words, it needs artificial intelligence, not just simple automation of repetitive tasks. That’s the crucial difference between a chatbot that frustrates and a digital assistant that solves.

It’s also important to recognize that automation does not mean dehumanization. On the contrary, when designed correctly, automation frees human teams from mechanical, repetitive tasks, allowing them to focus on cases that truly require empathy, judgment, or specialized expertise. The goal isn’t for the chatbot to do everything, but to resolve what it can and know when to step aside.

The pillars of Breeze AI in conversational automation

Breeze AI isn't just a sum of AI-enabled tools; it's a complete architecture that powers the automation of real-time customer conversations. From the first message to the complete resolution of a ticket, every component of Breeze helps HubSpot's chatbot not only respond, but do so with intelligence, context and actionability.

1. Breeze Copilot: Assisted Intelligence for Human Agents

At the heart of HubSpot’s conversational inbox, Breeze Copilot acts as a co-pilot for human agents. While the chatbot handles common inquiries, Copilot supports agents in the background with features such as:

•  Real-time automated reply suggestions based on historical data and conversation context.
  
  
•  Automatic summaries of previous tickets or lengthy interactions.
  
  
•  Generation of internal notes or knowledge base articles from frequently addressed tickets.
  
  

This way, even when the conversation needs to transition from bot to human, the handover is fast, informed, and ensures no loss of context.

2. Breeze Agents: Specialist chatbots ready for action

Among the different AI agents available, the Customer Agent takes center stage in ticket automation. This intelligent chatbot doesn’t rely on rigid scripts; instead, it draws on your HubSpot content (knowledge base, documents, public URLs) to:

• Understand user intent, even when the language is informal or ambiguous.
  
  
• Respond accurately using up-to-date, contextual information.
  
  
• Execute actions such as opening or closing tickets, updating CRM properties, or triggering automated workflows.
  
  

The Customer Agent can operate 24/7, handle thousands of conversations simultaneously, and escalate to human agents when it detects sensitive, complex, or out-of-scope inquiries.

3. Breeze Intelligence: Contextual data at the service of each response

An automated conversation is only effective if it’s backed by relevant information. Breeze Intelligence enriches every chatbot interaction in real time with data from a database of more than 200 million profiles, along with CRM records. Thanks to this, the bot can:

• Personalize responses based on customer history, subscription type, or previous tickets.
  
  
• Detect buying intent and trigger timely offers.
  
  
• Verify key data before responding, such as whether there are open orders, associated products, or active SLAs.
  
  

Contextual intelligence turns a basic answer into an experience that truly resolves and anticipates customer needs.

4. Integrated AI capabilities: Automation beyond chat

More than 80 AI-powered capabilities are integrated into various modules of the CRM, and many of them have a direct impact on the quality of automated service. For example:

• Automatic ticket classification based on urgency, sentiment, or detected topic.
  
  
• Activation of personalized workflows according to the conditions of the conversation.
  
  
• Automated follow-up after case resolution.
  
  
• Creation of new knowledge base articles when the chatbot identifies recurring unanswered questions.
  
  

Together, these functions enable HubSpot chatbots to not only have conversations but also learn, evolve, and actively contribute to continuous service improvement.

Real capabilities of chatbots at HubSpot

HubSpot makes available a series of conversational tools through its Service Hub module, integrated with the Conversation Inbox, ticketing system and knowledge base. This combination turns the CRM into a case resolution center, not just a registration center.


Chatflows in HubSpot allow you to design conversations with conditional logic, embedded forms, personalized responses and automatic assignments. Through built-in generative AI, the system is able to interpret user intent, understand topics even if they are expressed in informal language, and generate responses without relying on a preset decision tree.

Advanced contextual automation

With Breeze AI, the level of automated responses reaches new heights. One example is when a customer types "how do I cancel my subscription?" The system begins by understanding that they are referring to an unsubscription process, then accesses the customer's history and verifies their specific subscription type. At this point, the artificial intelligence evaluates whether there are promotions or alternatives that can retain the customer, suggests specific cancellation conditions according to their current plan, and offers alternatives such as downgrade or temporary pause.


Throughout this process, the bot guides the customer step by step to complete the management, while automatically recording the ticket with full context in the contact's file. All this happens without human intervention and with a level of personalization that was previously only possible with specialized agents.

Enhanced intelligent routing

Another valuable capability is AI-powered intelligent routing. Instead of assigning tickets randomly or in turn, HubSpot can automatically route them to the team. The system analyzes the type of query through natural language processing, determines priority based on sentiment analysis and context of the conversation, and considers the category of the customer based on their previous value and history. The system also automatically detects the language of the conversation, evaluates the availability and expertise of available agents, and queries historical patterns of successful resolution for similar cases.

Use of contextual properties

The use of contextual properties allows enriching each conversation with data from the CRM. The bot accesses critical information such as whether the customer has open orders and their current status, whether they have previously written about the same issue and how it was resolved, and whether they have special terms of service.

This information translates into more relevant and personalized responses, eliminating the need for the user to repeat their story or provide additional context.

Automated workflows

Finally, thanks to integration with AI-powered workflows, the chatbot transcends simple response to act intelligently and executively. The system creates records with automatic categorization based on conversation analysis, updates relevant contact properties based on the detected context, and sends personalized automated emails that reflect the specific nature of each interaction.


When the situation warrants, the bot triggers notifications to teams, automatically escalates complex cases to human agents, and has the ability to close tickets when it detects that the resolution has been successful. Additionally, it schedules automated follow-ups according to the nature and criticality of the case, ensuring that no query goes unattended.

Artificial intelligence within the HubSpot ecosystem

The incorporation of AI in HubSpot goes far beyond a trend. Since 2023, the platform began integrating language model-powered capabilities like GPT, under its solution called ChatSpot. Originally designed for internal search and analysis, this functionality is now being leveraged for customer service.

In this context, generative AI enables chatbots to craft responses with a level of naturalness previously unimaginable. These aren’t generic answers, but responses that factor in customer intent, the internal knowledge base, the history of previous interactions, and company-defined rules.

This capability also allows the chatbot to learn from every interaction. By registering resolved tickets, it can suggest new knowledge base entries, identify gaps in automated responses, and generate statistics on the most frequent contact reasons.

Unlike a rigid system, AI in HubSpot evolves. It doesn’t just reply—it generates insights. Why do so many customers ask about the same issue? Which answers perform best? Which flows lead to higher abandonment? All of this can be translated into practical improvements: adjusting processes, improving content, and redesigning service policies.

Success stories

The adoption of Breeze AI in different companies is evidence of the transformative impact of AI-driven automation on ticketing and customer experience. These cases not only demonstrate the effectiveness of the technology across different industries, but also highlight how ICX's strategic, customer-centric approach empowers tangible results.

Kaplan: Transformation in response times

Kaplan uses Breeze to optimize customer service communications, achieving a 30% reduction in response times. The implementation allowed them to automate frequent inquiries about courses, exam dates and certifications, freeing up their team to handle more complex and strategic inquiries. This case demonstrates how intelligent automation not only improves efficiency, but also allows human resources to focus on higher value interactions.

Xeople: 73% satisfaction rate in automated interactions

Xeople achieved 73% customer satisfaction in 307 surveyed interactions using Breeze Customer Agent. By delivering consistent and immediate support, they demonstrate how intelligent automation can generate measurable gains in customer experience and satisfaction.

B12: Instant resolution of 58% of queries

B12 transformed its customer service by implementing Breeze Customer Agent, which now resolves 58% of chat queries instantly. Jay Douglas, customer success manager at B12, explains, "Satisfaction surveys have improved tremendously since we activated the customer service agent. Now, our users can get answers when they need them, even when our team is not available after hours. Being able to assist our customers when there is no one available to do so has made a big difference.

Nutribees: 77% reduction in manually managed tickets

Pietro Ripanti, CMO of Nutribees, reports extraordinary results: "In the course of a year and a half, we have reduced the number of tickets handled by our team by 77%, and we have improved the conversion rate because we can now offer 24-hour support. The Breeze customer service agent is a key tool to generate revenue and increase satisfaction.

American Frame: Cobertura 24/7 sin expandir personal

Nate Buswell, customer experience specialist at American Frame, highlights the operational impact: "We were closed on weekends and after hours, so we couldn't handle inquiries around the clock. Now the customer service agent has our back 24 hours a day.

RADD Companies: Better routing and customization

Sid Wambach, manager of customer relationship management at RADD Companies, emphasizes the improvement in efficiency: "The Breeze customer service agent has had a very positive impact on our business, allowing us to improve interaction and satisfaction. It effectively directs customers to the resources they need immediately, so our team can provide more personalized support where it matters most."

CMMG: Identification of obsolete resources

Jacob Blackerby of CMMG found unexpected benefits: "The Breeze customer service agent has significantly improved our efficiency by automatically handling simple queries, reducing our ticket volume and providing immediate responses to customers. An unexpected benefit was that it helped us identify outdated internal resources that needed to be updated, which has made a big difference to the overall quality of service we provide.

Practical Implementation Guide

Initial phase: Assessment and strategic planning

The successful implementation of Breeze AI begins with a comprehensive audit of existing tickets from the past six months. This assessment should identify the most frequent types of inquiries, following the 80/20 rule, the average resolution time by category, the most effective agents and their methodologies, as well as the escalation and resolution patterns that have demonstrated the greatest success.

Simultaneously, it is crucial to carry out a detailed mapping of organizational knowledge. This includes documenting the existing knowledge base, cataloging support policies and procedures, identifying the necessary integrations with external systems, and mapping current workflows that require automation.

Objective setting should establish specific and measurable KPIs. The percentage of tickets to be automated should have a conservative initial target of 30–40%, with clear goals for reducing first response time, specific customer satisfaction targets, and realistic expectations for operational efficiency.

Configuration and training phase

The initial setup of Breeze Copilot requires carefully connecting all relevant internal data sources, configuring secure access to the knowledge base, defining appropriate permissions and roles for each team member, and establishing robust security and privacy parameters.

Training the Customer Agent is one of the system’s most innovative features, as the process is completed within minutes. The system uses the existing content in your knowledge base, relevant pages from your website, support-related blog articles, comprehensive product and service documentation, and all customer service policies to create an intelligent, context-aware agent.

Developing conversational flows should address the most frequent inquiries identified during the initial audit, establish clear processes for automatic escalation, implement efficient contextual data-gathering mechanisms, and ensure perfect integration with existing ticketing systems.

Validation and refinement phase

Internal testing should include thorough trials using real scenarios drawn from historical tickets, rigorous validation of responses with support teams, fine-tuning of tone and brand personality, and continuous optimization of conversational flows based on observed results.

Launching a controlled pilot allows for system evaluation with a limited percentage of total traffic (typically 10–20%), establishes real-time monitoring of all interactions, gathers systematic feedback from both customers and agents, and enables rapid iterations based on preliminary outcomes.

Validation metrics should assess the automatic resolution rate, measure customer satisfaction specifically in automated interactions, compare resolution times with traditional methods, and analyze necessary escalations along with their root causes.

Deployment and scaling-up phase

Phased escalation includes a gradual and controlled increase in the percentage of automated traffic, progressive activation of advanced functionality, full integration with all relevant workflows, and enabling the system's continuous learning capabilities.


Training of the team should encompass specialized training in AI supervision, development of new processes to handle escalated cases, education in the effective use of Breeze-generated insights, and continuous response optimization techniques based on feedback and data.

Continuous optimization phase

Continuous monitoring and improvement requires weekly analysis of performance metrics, regular updating of the knowledge base, constant refinement of escalation algorithms, and gradual expansion to new query types as the system demonstrates its effectiveness.


Strategic evolution includes deep pattern analysis to identify product enhancement opportunities, comprehensive end-to-end customer experience optimization, and strategic integration with broader digital transformation initiatives.

ICX Consulting's approach: more than technology, strategic design

At ICX we understand that technology is not enough. Automating with AI at HubSpot requires a structured process that is sensitive to the business, the customer and the internal teams. That's why we apply a phased approach that combines maturity diagnosis, flow design, technical implementation and subsequent support.


In the first stage, we evaluate how prepared the company is: is there a knowledge base, is there segmentation in the CRM, are tickets used? Then, we design the ideal flow, taking into account business objectives, brand tone and current friction points.


Once the design is defined, we implement the chatflows, connect the knowledge base, configure the AI and define the escalation criteria. During the first weeks of operation, we accompanied the team in the review of logs, suggestions for improvement and optimization of flows.


More than installing a bot, we want companies to install a new way of thinking about service.

Conclusion

Automating tickets with AI within HubSpot is not a technical decision, it's a strategic one. It's about transforming a fragmented and slow experience into a fluid, continuous and scalable relationship without losing personalization. Experience is everything, solving well and fast is no longer an advantage, it is the minimum expected.


The technology exists, but it needs to be configured with judgment, intelligence and sensitivity. It is not only about responding faster, but also about building trust, reducing friction and improving brand perception. When a customer feels that they were understood and attended to without having to repeat their story, value is generated that goes beyond the resolved ticket.


At ICX, we have seen how this transformation has an impact on indicators, on the operational load, but above all, on customer satisfaction and team peace of mind. Because when AI is well implemented, everyone wins: the customer, the business and the people who make it all work.

Next steps

If you feel that your support team is saturated, if your customers demand faster responses or if you simply want to scale your operation without skyrocketing costs, this is the signal you were waiting for.


From ICX Consulting we offer you a free diagnostic session to evaluate the maturity level of your service and present you with a clear roadmap to implement AI with HubSpot.