Introduction

For a long time, CRM systems have been the best way to monitor and manage operations across sectors. But most teams still spend hours repeating the same tasks, including entering data by hand, relying on reminders for follow-ups, and having useful information spread out.

The result? A CRM that stores information but rarely acts on it. This is where AI and, more specifically, LLMs (Large Language Models) change the equation. Personalization becomes scalable, routine actions disappear, and teams spend more time closing deals or supporting customers.

HubSpot has taken a clear step in this direction by opening its CRM workflows to LLM-powered actions. With AI-driven automation and enriched data intelligence, hubspot consulting services now allows businesses to move beyond basic triggers and build workflows that think, adapt, and respond in real time.

As AI becomes embedded in CRM operations, it’s no longer a future upgrade; it’s becoming the standard for teams that want speed, relevance, and measurable efficiency.

What Are LLMs & Why They Matter for CRM

Role of An LLM

A Large Language Model (LLM) is a type of software that can read, comprehend, and produce language by looking at patterns in actual data. It doesn’t only look for certain terms. Before generating any answer or insight, the system considers the situation, its existing knowledge, and its objectives.

Tools like GPT, Claude, and Gemini are common examples. While they’re often used for writing, their real strength is interpretation. They can summarize conversations, spot patterns in messy data, and respond in a way that feels relevant rather than scripted.

Why This Changes How CRM Systems Work?

Most CRM systems already store valuable information (emails, deal notes, call logs, tickets). The problem is that this data typically remains unreviewed unless someone manually reviews it.

HubSpot allows teams to plug LLMs directly into workflows using custom workflow actions. This means AI can run inside existing processes, not outside them.

For example, a workflow can summarize a sales thread, enrich a contact record, or suggest next steps based on real interaction data. Everything happens where teams already work.

This is exactly how AI transforms HubSpot CRM workflows—turning the platform into an intelligent system that supports decision-making, not just record-keeping.

How HubSpot Supports Custom LLM Workflow Actions

What Are Custom LLM Workflow Actions

Custom LLM workflow actions allow HubSpot workflows to call an external language model at specific points in a process. Instead of relying solely on fixed rules, the workflow sends context to an LLM and uses the LLM’s response to determine next steps.

These activities can include text processing, record sorting, and sense-making of notes, messages, and other unstructured inputs. You may write the output straight into the attributes of a contact, deal, ticket, or custom object.

HubSpot may work with several suppliers, including OpenAI, Anthropic, Cohere, and Google. This gives teams the freedom to choose the option that best fits their needs, whether it’s security, pricing, or use case.

Key Settings & Configuration

An API key is used to connect to an AI provider before each LLM activity. Next is the prompt template, which tells us what to do next:

• What the model needs to look at
• What information can it use as a reference
• How to format the answer

Making small modifications here may often have a significant impact on the quality of the result. Teams may also change how people respond by adjusting factors such as temperature and the complexity of their thinking. Finally, the result is sent back to HubSpot, which either updates CRM attributes or instructs the next step in the process.

When configured properly, these actions add intelligence to workflows without adding operational complexity.

Sales Automation with HubSpot + LLMs

1. Auto-Generated Follow-Up Messages

Most follow-ups say the same thing, just written slightly differently each time. An LLM can review the last call, email thread, or meeting note and draft a response that accurately reflects what actually happened.

The rep doesn’t lose control. They still review and send. Instead of starting from a blank screen, they begin with something useful that already fits the conversation.

2 Lead Qualification with AI

Not every lead that fills a form is ready to talk. And not every good lead looks good on paper.

LLMs can read messages, internal notes, and CRM activity together. Combined with AI-powered lead scoring in HubSpot, this allows sales teams to prioritize intent and urgency instead of relying only on surface-level metrics. They help sort leads by intent, urgency, and relevance, not just by clicks or page views. That keeps sales focused on the right conversations.

3. Intelligent Deal Summaries

Deals get messy over time. Notes pile up. Context gets lost. An LLM can pull everything together into a short summary: what’s happened, what’s pending, and what could block the deal. This helps during reviews, handoffs, or when a rep returns to an existing opportunity.

What does this change for Sales?

• Less time writing
• Fewer cold handoffs
• More time spent selling

The goal isn’t automation for its own sake. It’s clarity, speed, and better use of sales effort.

Marketing Automation with LLMs in HubSpot

Email Content Generation

Email teams spend more time rewriting than thinking. LLMs can draft subject lines, email bodies, and simple variations using existing CRM data. 

Factors such as lifecycle stage, past opens, or deal status provide enough context to start with something usable. Marketers still review and edit. The difference is speed. You don’t begin from scratch every time.

These AI-driven workflows elevate traditional campaigns far beyond standard HubSpot marketing automation strategies.

Campaign Personalization

Personalization usually breaks down at scale. Instead of cloning the same campaign five times, LLMs can adjust the language based on CRM attributes such as role, industry, or account type. 

The offer stays the same. The message adjusts slightly to match the reader. This keeps campaigns relevant without adding weeks of prep work.

Social & Blog Content Assistance

Content planning often starts with a blank page. LLMs can help draft post ideas or rough outlines based on what customers already interact with inside HubSpot. It’s not a strategy. It’s momentum. The team decides direction. AI helps fill the gaps faster.

Intelligent Segmentation Suggestions

Most lists are built the same way every time. LLMs can analyze behavior and attributes and suggest new segment ideas that may not be immediately apparent. These aren’t final answers. They’re prompts for better testing.

Customer Support Transformation with LLMs

Automated Case Summaries

Most tickets don’t start clean. They grow. By the time an agent opens one, there are already emails, replies, and updates to skim through.

LLMs can keep a running summary of the case. What the issue is. What’s been tried. What’s still pending? Agents don’t waste the first few minutes reading. They respond with context already in hand.

Response Suggestions for Agents

Even experienced agents pause before typing. LLMs can draft replies using similar past tickets and internal notes. These aren’t final answers. They’re starting points. Agents tweak, shorten, or rewrite sections. But they don’t start from zero every time.

Ticket Classification and Priority

Manual tagging breaks under pressure. Important tickets slip through. LLMs can sort incoming cases by topic or urgency based on what the customer actually says. That helps queues stay cleaner, and serious issues surface faster.

Draft Replies for Repeating Questions

Some questions show up daily. Order status. Access issues. Basic setup steps. LLMs can prepare draft replies for these cases, so agents only need to review and send.

Unlocking Value: Using HubSpot Data to Power AI Workflows

Feeding Real CRM Data into LLM Prompts

HubSpot stores years of interaction history, including emails, meetings, deal movement, and form submissions. When this data is pulled into an LLM prompt, automation stops guessing.

Instead of a generic instruction, the model receives real signals. Who the contact is. How they’ve behaved. What stage the deal is in. That context changes the quality of every output, whether it’s a follow-up message, a summary, or a classification. This is what separates basic AI usage from useful automation.

Moving HubSpot Data Beyond the Platform

Some teams need more control than in-platform actions allow. This approach is especially useful for organizations running HubSpot for enterprise-scale operations, where data volume and complexity demand more advanced AI logic. Airbyte is a popular tool for extracting HubSpot data and sending it to bespoke AI services or internal environments. This enables more extensive processing, custom logic, or model tuning without disrupting core CRM activities. The CRM stays clean, and the intelligence layer runs separately.

LLMs don’t replace CRM logic; they depend on it. When HubSpot data is used properly, AI workflows stop feeling artificial and start behaving like informed decisions.

HubSpot Academy AI – Learning as You Automate

HubSpot Academy now uses AI to answer questions directly inside the learning experience. Instead of browsing through long courses or documentation, users can ask a question and receive an answer sourced from HubSpot’s own materials.

This AI is grounded in HubSpot content, not generic sources. That matters. The guidance reflects how the platform actually works, not how it should work in theory.

There’s a practical side to this…. teams using AI to learn HubSpot adopt AI workflows faster. They become accustomed to reviewing outputs, correcting responses, and treating AI as a support tool rather than a black box.

Over time, this changes behavior. Learning and execution start happening together. Once they’re introduced to LLM-powered workflows, the shift feels natural and effortless. 

Best Practices for Integrating LLMs with HubSpot

Standardize your CRM data before using AI

If contact records, deal stages, or notes are inconsistent, the AI output will reflect that confusion. LLMs don’t “fix” messy data. They amplify whatever they’re given.

First, cleaning fields and aligning naming conventions make every AI action more reliable.

Set clear boundaries to avoid incorrect outputs

LLMs strive to be helpful, even when they lack sufficient knowledge. That’s where mistakes occur. Prompts should specify what the AI may use, what it must disregard, and which response types are acceptable.

Review outputs regularly, not just at launch

AI behavior may shift over time as data changes. What works well in the first week may decline over time. Sampling outputs and maintaining records allow teams to identify problems early, before they affect consumers or sales decisions.

Always plan for AI failure scenarios

APIs time out. Responses can be incomplete. Sometimes the AI just doesn’t return anything useful. Every workflow needs a fallback path, whether that’s routing to a human or skipping the AI step entirely.

Start with low-risk use cases and expand slowly

Internal drafts, summaries, or classifications are appropriate places to start. They assist, but they aren’t necessary. Teams can proceed to more visible automation with confidence once they trust the results.

For teams unsure how to scale responsibly, understanding when to work with a HubSpot consultant helps avoid missteps while accelerating ROI.

Performance & Measurement – KPIs to Track

Sales cycle reduction

One of the first signals is speed. When follow-ups, summaries, and qualifications occur more quickly, deals move forward with fewer gaps. Track how long opportunities sit in each stage before and after automation.

Email open and response lift

Personalized, context-aware messages usually perform better. Compare open and reply rates for AI-assisted outreach versus standard templates to understand the real impact.

Support resolution time and CSAT impact

Summaries, suggested replies, and better routing reduce back-and-forth. Measure first-response time, resolution time, and customer satisfaction together. The relationship between them matters more than any single metric.

Marketing engagement lift

Campaigns supported by AI-generated variants often see higher clicks and interactions. Track engagement by segment, not just overall performance.

Workflow efficiency gains

Finally, look at the effort saved. Fewer manual steps, fewer handoffs, and fewer rework loops. That time recovered is often the biggest ROI, even if it doesn’t show up directly on a revenue chart.

Challenges & How to Mitigate Them

Prompt quality isn’t stable

What works today won’t always work the same way later. Inputs change. CRM data becomes complicated again. Prompts need versions, minor changes, or rollbacks. The same discipline used for workflows should apply here.

Data privacy questions show up fast

CRM data includes information that shouldn’t be shared everywhere. It includes key aspects such as pricing, internal notes, and customer conversations, while passing only what’s needed and masking the rest. Decide this before workflows go live, not after someone asks.

Costs don’t spike, they creep

API usage grows as more teams add automation. One workflow becomes five. Five becomes twenty. Usage tracking and alerts keep this visible. Without them, costs become a surprise line item.

Too much automation is still a risk

Not every decision benefits from AI. Some need context, judgment, or a pause. Human checkpoints in critical workflows protect quality and trust.

How Solvios Technology Revamps Your AI Workflow 

AI processes add value only when they are carefully planned, constructed, and managed. That’s where Solvios Technology comes in. We begin with AI strategy consulting for HubSpot, collaborating with teams to determine where LLMs add value and where they do not.

We also develop the data pipelines and connections that power these procedures. Whether the data is in HubSpot or other systems, we guarantee that the correct context reaches the model without disclosing unneeded information.