Our Vision for Building an Open Ecosystem for the Agent Era For years, HubSpot invested in making our platform the best place for marketing, sales, and service teams to do their work. With AI, we’ve been building it to do the work for them – through agents that qualify leads, resolve tickets, save deals, and drive outcomes across the business. That’s why we call HubSpot an agentic customer platform. But agents don’t click through dashboards or navigate interfaces; they call APIs, read structured outputs, and take action. Software built for humans has to evolve to be genuinely accessible to agents, too. Access alone isn’t enough, though. Agents also need substance. An agent reasoning over raw records has no way to know what’s normal for a specific business, or what’s worked for hundreds of thousands of companies like it. As we recently wrote, the real AI race isn’t about models or data; it’s about context. That conviction shapes everything we build. It’s why we were among the first to ship an MCP server, and why we’ve kept expanding what agents can read, write, and act on since. That was only the beginning. The vision we are working toward is bigger: Agents can run on HubSpot. And agents can run HubSpot. Running on HubSpot means any agent – ours or anyone else’s – can plug into HubSpot’s data, context, and capabilities as a building block. Running HubSpot means agents can operate the platform end-to-end through our APIs, MCP server, CLI, and whatever...

The Real AI Race Isn't About Models or Data. It's About Context. Every company I talk to right now is convinced they have an AI problem. Their AI writes emails nobody responds to. It researches accounts and surfaces leads the sales team already closed six months ago. Finger-numbing sessions copying and pasting between tools generate content that sounds exactly like what every competitor is publishing. Leaders invest in tool after tool, run training session after training session, and still find themselves staring at the same question: why isn’t AI actually moving the needle? Here’s what you’re not being told. The problem is not your model. The problem is not your data. The problem is context: the specific knowledge of your business, your customers and what they need right now, and how your team actually works. It is also the hardest problem to solve, and the one the industry has been slowest to address. Context is the Infrastructure, Not the Feature Here is the distinction that I think is getting lost. Data is what happened. Context provides meaning around real events, what they mean, why they matter, and what to do about it. Context is not a feature; it is necessary infrastructure. Your CRM has a record that a deal closed eighteen months ago. That is data. Context is knowing the deal closed because your champion switched companies, the pricing had to be adjusted three times before it landed, and that customer now refers several new deals a year and hates...

Knowing About AI Isn't Enough. Here's How to Actually Use It. Maybe you’ve opened ChatGPT a handful of times, gotten subpar results, and moved on. Maybe you’ve sat through an AI training or two and thought, “Cool, but how does this actually apply to my job?” Or maybe you’ve bookmarked a dozen AI tools you saw recommended on LinkedIn and haven’t tried a single one. You’re not alone. That gap between knowing AI and using AI is where many of us are right now. And it doesn’t help that everyone’s telling you to use it. I know because this is pretty much my job: I manage a writing team on the HubSpot Blog, and a big part of my work is enabling them with AI. Not in the abstract, inspirational keynote sense, but in the here’s how to get your actual work done better sense. What I’ve learned is that the problem is almost never motivation. People want to learn. It’s that information about AI is everywhere, but genuine enablement — what actually changes how you work — is surprisingly rare. That’s what this post is about. In this guide, I’ll share a practical framework for integrating AI into your work in a way that advances your skills, impact, and career. Table of Contents Why Being AI-Enabled Helps Your Career Why is AI so hard to adopt? What does AI enablement look like? How Teams Can Move From AI Experimentation to Execution Where...

Answer engine optimization trends in 2026: How AEO is transforming the landscape Emerging trends in answer engine optimization are reshaping how brands earn visibility, trust, and demand in AI-powered search. Answer engines like ChatGPT, Google AI Overviews, Perplexity, and Gemini now deliver fully synthesized answers directly to users, compressing the traditional customer journey. According to HubSpot's Consumer Trends Report, 72% of consumers plan to use AI-powered search for shopping more frequently. If your content isn’t structured for or easily parsed by answer engines, your brand won’t appear. Competitors will. Or worse, inaccurate narratives about your company, pulled from sources you don’t associate with, may surface prominently in AI-driven results. That’s a visibility risk no business can afford. In this post, I break down the emerging trends in answer engine optimization, why they matter for revenue, and how to integrate AEO with traditional SEO strategies to drive full-funnel growth. Table of Contents Why Emerging Trends in Answer Engine Optimization Matter Now 6 Emerging Trends in Answer Engine Optimization You Should Act On How to Integrate AEO Strategies With SEO for Full-Funnel Growth How to Measure AEO Beyond Rankings and Clicks Frequently Asked Questions About Emerging Trends in Answer Engine Optimization Why Emerging Trends in Answer Engine Optimization Matter Now Answer engine optimization matters because search behavior is fundamentally changing: AI Overviews reduce organic clicks but increase the value of citations, and conversational assistants are becoming preferred search options for consumers. HubSpot's Consumer...

The best AI visibility tools that actually improve lead quality Search has changed faster than most teams have adapted. For years, visibility meant ranking — climbing search pages through backlinks, keywords, and authority signals. Now, customers open ChatGPT or Gemini, type a question, and receive a synthesized answer drawn from multiple sources. McKinsey’s recent finding that only 16% of brands systematically track AI search performance underscores the gap between how people search and how companies measure visibility. Most teams simply don’t know whether AI systems recognize their brand or include it in generated responses. AI visibility tracking tools fill that blind spot. These tools track vital brand health outcomes like brand mentions, sentiment, and share of voice across AI search engines and connect those insights to CRM and pipeline data. This visibility shows which content earns citations, which competitors surface, and which topics require reinforcement. With that data in place, marketers can finally measure whether citations in generative answers correlate with qualified leads, faster sales cycles, or higher conversion rates. Table of Contents What are AI visibility tools, and how do they work? How to Compare AI Search Optimization Tools for Your Needs The 5 Best AI Visibility Tools Right Now AI visibility can turn mentions into higher-quality leads AEO Content Patterns That Increase Citations in AI Answers Measure impact beyond vanity metrics in GA4 and your CRM Frequently Asked Questions About AI Visibility Tools What are AI...

AI search strategy: A guide for modern marketing teams Search no longer rewards keywords alone — it rewards clarity. Large language models now read, reason, and restate information, deciding which brands to quote when they answer. An AI search strategy adapts content for that shift, focusing on being understood and cited, not just ranked and clicked. Structured data defines entities and relationships; concise statements make them extractable; CRM connections turn unseen visibility into measurable influence. Clicks may decline, but authority doesn’t. In AI search, every sentence becomes a new point of discovery. This article explores what an AI search strategy is and how content marketers and SEOs can implement an effective one. Readers will also learn how to measure success and the tools that can help. Check your AI visibility with HubSpot’s AEO Grader to see how AI systems currently represent your brand. Table of Contents What is an AI search strategy? Where Inbound Marketing Fits AI Search Strategy for Content Marketers and SEOs How Loop Marketing Integrates With Your AI Search Strategy How to Measure AI Search Strategy Success How HubSpot’s AEO Grader Can Help Frequently Asked Questions About AI Search Strategy What is an AI search strategy? An AI search strategy is a plan to optimize content for AI-powered search engines and answer engines. An AI search strategy aligns content with how large language models (LLMs) and answer engines interpret, summarize, and attribute information. Traditional SEO...

What we learned building SalesBot — HubSpot’s AI-powered chatbot selling assistant When I first joined HubSpot’s Conversational Marketing team, most of our website chat volume was handled by humans. We had a global team of more than a hundred live sales agents — Inbound Success Coaches (ISCs) qualifying leads, booking meetings, and routing conversations to sales reps. It worked, but it didn’t scale. Every day, those ISCs fielded thousands of chat messages from visitors who needed product info, had support questions, or were just exploring. While we loved those interactions, they often pulled focus from high-intent prospects ready to engage with sales. We knew AI could help us work smarter, but we didn’t want another scripted chatbot. We wanted something that could think like a sales rep: qualify, guide, and sell in real-time. That’s how SalesBot was born — an AI-powered chat assistant that now handles the majority of HubSpot’s inbound chat volume, answering thousands of chatter questions, qualifying leads, booking meetings, and even directly selling our Starter-tier products. Here’s what we’ve learned along the way. How We Built SalesBot and What We Learned 1. Start with deflection. Then, build for demand. When we first launched SalesBot, our primary goal was to deflect easy-to-answer, low sales intent questions (example: “What’s a CRM” or “How do I add a user to my account”). We wanted to reduce the noise and free up humans to focus on more complex conversations. We trained the bot on HubSpot’s knowledge base, product catalog,...

Automated email segmentation: Setting up for better targeting Automated email segmentation uses dynamic rules and real-time data to group contacts automatically, eliminating manual list updates while boosting campaign relevance. By connecting unified customer data, you can build segments that update based on behavior, lifecycle stage, or engagement, and then trigger personalized workflows and content for each group. Start by cleaning your data, creating dynamic lists, linking them to automated journeys, and using AI to scale targeting and copy. In this blog post, we'll guide you through setting up better targeting, step by step. Table of Contents What is automated email segmentation? What data do you need before you automate segmentation? How to Automate Email Segmentation Starter Templates for Automated Segmentation Frequently Asked Questions about Automated Email Segmentation Unlike traditional static lists that require constant manual updates, automated segmentation continuously adjusts audience membership based on changing customer behaviors, preferences, and lifecycle stages. Dynamic lists update segment membership automatically in response to data changes, whereas static lists remain fixed until manually modified. For example, a dynamic segment for “recent purchasers” will automatically include new customers who have completed a purchase and exclude those who haven't made a purchase in the past 90 days. This automation eliminates the need for manual exports and improves message relevance by ensuring your segments always reflect current customers. The key advantage is that segment membership triggers automated workflows and personalized content delivery. When someone moves from...

Top 7 use cases for AI personalization in marketing As a marketer and consumer, few can explain the impact of AI personalization quite like yours truly. I’ve created (and received) hundreds of personalized marketing assets in my day, and it’s crystal clear when something was created in a half-hearted effort, versus when it’s tailored to one's specific interests and behaviors. The latter makes both of my alter egos smile, and a lot of it is thanks to artificial intelligence. If you’re interested in using AI personalization marketing to reach your customers, I put together this guide to help. Table of Contents Executive Summary What is AI personalization? Why use AI for marketing personalization? Benefits of AI Personalization Marketing Challenges of AI Personalization Top 5 Use Cases for AI Personalization Marketing Real-World AI Personalization Examples Across Industries AI Personalization Best Practices What are the future trends in AI personalization? Frequently Asked Questions About AI Personalization in Marketing Executive Summary AI personalization uses artificial intelligence to deliver tailored experiences, content, or offers to each customer based on their behavior, preferences, and real-time data. Unlike traditional personalization, AI adapts automatically and at scale. Key benefits include higher engagement, increased revenue, and improved customer satisfaction. Common real-world examples can be seen in Amazon‘s product recommendations and Netflix’s viewing suggestions. To get started with AI personalization, select the right tools for your goals and experience, establish a robust data foundation, and...

AI-powered email content suggestions that actually convert leads AI-powered email content suggestions are changing how marketing teams write, test, and scale email campaigns. Instead of guessing what message will resonate, marketers can now use AI to analyze data, predict engagement, and generate personalized content that converts. But there’s a huge difference between emails that sound good and emails that perform. In this guide, we’ll cover how to use AI email content suggestions to deliver stronger open rates, higher click-throughs, and measurable revenue impact. Table of Contents What AI-Powered Email Content Suggestions Are and Why They Matter Best AI Tools for Email Content Suggestions How to Set Up AI Email Suggestions Inside Your Existing Workflow AI Email Content Prompts for Every Lifecycle Stage AI Email Content Generation Guardrails How to Measure AI Email Content Performance and Iterate Fast Common AI Email Content Pitfalls and How to Avoid Them What AI-Powered Email Content Suggestions Are and Why They Matter AI-powered email content suggestions use machine learning to tailor your subject lines, body copy, and CTAs to each audience segment and lifecycle stage. The system analyzes engagement data, like open rates and replies, to recommend what message, tone, or offer is most likely to drive action. In practical terms, that means: Generating email subject lines designed to boost open rates Optimizing email copy that earns more clicks and replies Recommending CTAs that move contacts from MQL...