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AI agents for digital marketing: How to scale campaign execution without scaling headcount

Artificial Intelligence
October 6, 2026/by
AI agents for digital marketing: How to scale campaign execution without scaling headcount Digital marketing is one of the most process-intensive functions in marketing, and AI agents for digital marketing are changing how teams handle that load. Every campaign involves the same chain of tasks. Building audiences, setting up nurture sequences, writing follow-ups, and monitoring performance all have to happen every time, across every channel. The manual version of campaign execution does not scale. As campaigns multiply and channel complexity increases, the gap between what the team can execute and what the strategy calls for grows. The answer is rarely more headcount. It is smarter automation. AI agents handle repetitive execution tasks such as audience building, sequence setup, and performance monitoring. That gives marketing teams more time for the work that requires human judgment. Table of Contents What AI Agents Actually Do for Marketing Teams Shifting Execution Load So Strategy Can Scale What AI Agents Actually Do for Marketing Teams AI agents are software programs that plan, decide, and act across multiple steps to complete a goal without waiting for direction at each stage. Traditional automation executes a fixed sequence. An AI agent interprets context, adapts to new information, and coordinates across tools independently. For marketing teams, that distinction matters. Three capabilities define how AI agents actually change marketing execution. Automate campaign workflows from trigger to follow-up without manual upkeep. A well-designed campaign workflow has a lot of moving parts. Enrollment triggers, email sequences, wait...
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Inside the creator economy’s AI reckoning

Artificial Intelligence, Creator Economy
September 17, 2026/by
Inside the creator economy’s AI reckoning   Few phenomena within the creator economy have moved as fast as AI’s embrace. What was once treated with anxious suspicion is now more widely viewed as a necessary strategy. Last year, according to Kameron Buckner, founder of Social Docket, which helps creators navigate the legal implications of their work, the conversation around AI was defined by dread. This year, she says, the tone has shifted from fear to leverage — less “this thing is going to take over” and more how do we actually use it. I spent the last several weeks talking to Buckner and others closest to this shift: top creators, consultants, founders, and social media strategists. Unsurprisingly, those who returned from the Cannes Lions International Festival of Creativity this year said AI was a centerpiece of the discourse. But across the board, I sensed no real fear. Everyone I spoke to is using AI. Some more than others, some with discernment, others with keen enthusiasm. What’s certain is that AI has brought new questions to an industry built on areas like increasing productivity, monetizing creativity, and protecting IP. The bottom line: using it wisely is the difference between staying ahead of the curve and getting drowned out by it. Table of Contents The Accelerator vs. The Author Legal is trying to catch up. Does AI level the playing field, or widen the gap? The consensus: Humans remain irreplaceable. The Accelerator vs. The...
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The AI Perception-Reality Gap

Artificial Intelligence, HubSpot POV
June 1, 2026/by
The AI Perception-Reality Gap There’s a widening gap between what the market says about AI and what we actually hear from customers. The media, the VCs, the AI labs, and influencers have all talked about AI replacing humans, ripping out trusted software, and token-maxxing as ends worth pursuing. But the leaders running real businesses are increasingly asking the right questions. How do I make my people better with AI? Which systems can I trust? How can I measure the ROI of this spend? We hear these questions every day. After three and a half years of building, shipping, and watching many of our growing customers put AI to work, the AI perspectives we are most certain of at HubSpot are the things almost no one else is saying out loud. Here are six of them. AI activity is not AI outcomes. The industry has confused motion for progress. Drafting emails, generating summaries, doing research. These are activities that AI has made much easier. They are useful capabilities, and we ship them at HubSpot. But activity is the input, not the result. Activity without outcomes is theater. The companies winning with AI are the ones working backwards from a business problem, not forward from a model demo. For example, customers using Customer Agent are responding to tickets 25% faster, while those using Prospecting Agent are generating 76% more leads. This is why we moved Customer Agent and Prospecting Agent to outcome-based pricing in April. AI outcomes are what matter....
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AI search behavior: What it means for your marketing strategy in 2026

Artificial Intelligence
June 1, 2026/by
AI search behavior: What it means for your marketing strategy in 2026 AI search behavior may be causing a dip in your traffic, but it’s also sending higher-quality leads your way. For marketers, that second part is a massive win. AI search was the number one predictor of purchase intent for CRM software buyers, according to HubSpot’s State of AEO 2026 report. And there are more findings from the report that every go-to-market team needs to know. In this article, I’ll share the latest findings on AI search behavior, its impact on brand discovery, an answer engine optimization (AEO) strategy you can implement today, and much more. Table of Contents What is AI search behavior, and why should marketers care? How AI Search Behavior Creates New High-Intent Discovery Paths The Impact of AI Search on Brand Discovery How to Plan Content Around AI Search Behaviors Why Track AI-Driven Search Engines and How to Start How AI Model Updates Impact Search Optimization What AI Search Behavior Means for Sales and Service An AEO Playbook You Can Run Today Frequently Asked Questions About AI Search Behavior What is AI search behavior, and why should marketers care? AI search behavior refers to the actions people take when they’re seeking answers using artificial intelligence, whether that’s asking ChatGPT or consulting Google AI Overviews. In the past, traditional search consisted of a user entering keywords into a search engine like Google, getting...
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Introducing the HubSpot Agent CLI

AI Transformation, Artificial Intelligence
May 27, 2026/by
Introducing the HubSpot Agent CLI A few weeks ago, I wrote about our vision for the agent era: agents should be able to run on HubSpot, and to run HubSpot. I want to go a level deeper on what “run HubSpot” actually means, and our latest step in bringing this vision to life. Businesses aren’t just sending employees into HubSpot to do work. They’re sending agents. And those agents need to be able to act as effectively as possible on your behalf, wherever they’re operating. That last part is important. An agent isn’t always running in one place, on one infrastructure. With AI Connectors, HubSpot context and actions are already available in Claude, ChatGPT, and other environments where teams work. Now, we’re adding another agent infrastructure: Command Line Interface (CLI). Introducing the HubSpot Agent CLI The HubSpot Agent CLI brings HubSpot’s data and intelligence into the environments where GTM and ops teams are composing their own workflows – Codex, Claude Cowork, and Claude Code – and allows agents to automate repetitive, bulk, and scheduled work. The simplest way to think about it: take the questions you’ve been asking or the tasks you’ve been completing repeatedly in chat, and automate them. Build automations in Codex or schedule them in Cowork, and the work happens on its own before you even get to your desk. It’s built on the same foundation as our public APIs and MCP server that already power our AI Connectors — and it’s designed to complement them,...
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Our Vision for Building an Open Ecosystem for the Agent Era

ai-hidden, Artificial Intelligence
May 5, 2026/by
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...
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The Real AI Race Isn’t About Models or Data. It’s About Context.

ai-hidden, Artificial Intelligence
April 9, 2026/by
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...
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Knowing About AI Isn’t Enough. Here’s How to Actually Use It.

ai-hidden, Artificial Intelligence
April 8, 2026/by
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...
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Where to Start with AI: A Practical Guide for GTM Teams

Artificial Intelligence
March 4, 2026/by
Where to Start with AI: A Practical Guide for GTM Teams Over the past year, I‘ve had hundreds of conversations with business leaders about AI. The pattern is always the same. They’re not short on tools or ambition. They're struggling with where to get started and how to get value. The pressure to adopt AI is real. But pressure without direction leads to experiments that don‘t stick, tools that don’t get used, and teams that grow more skeptical. Why? Because AI output didn't lead to actual outcomes. Here‘s what I’ve learned from watching teams that succeed with AI: they don't start with AI. They start with a problem. A specific, painful, time-consuming part of their work that they want to fix. Then, they find the right AI use case to achieve that goal. As they see results, their confidence grows, and they explore other AI capabilities – again, tied to a clear goal. That‘s the approach I want to share. Not an exhaustive list of everything AI can do, but a practical guide to where marketing, sales, and service teams can get started and see real value with AI. For transparency, we’ve organized use cases by how ready the technology is today. At HubSpot, we are building and improving these capabilities every day. Let’s start with simple definitions: Established: These are use cases where AI works reliably. Implementation is straightforward. Results are repeatable. If you're wondering where to start, it’s here! Emerging: These use cases are...
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Claude vs. ChatGPT: A marketer’s guide to choosing AI

Artificial Intelligence
March 2, 2026/by
Claude vs. ChatGPT: A marketer’s guide to choosing AI “What’s better: Claude or ChatGPT?” is the mind-boggling question every marketer is asking right now. As AI tools become essential to content workflows, understanding the differences between Claude and ChatGPT for marketing can mean the difference between a streamlined operation and a frustrating bottleneck. In my opinion, both tools have legitimate strengths. ChatGPT – which you can train on your specific needs – excels at rapid ideation, email copy, and social content. However, Claude shines at long-form editing, brand voice consistency, and handling large context windows. The question isn't really “is Claude better than ChatGPT?” It’s about which LLM you should use for each specific task. In this guide, I’ll break down everything you need to know, including: Claude AI versus ChatGPT for writing ChatGPT versus Claude for email Claude versus ChatGPT pricing Claude versus ChatGPT integrations with your existing stack Plus, my (very smart) colleagues have tested writing blog posts with ChatGPT, explored ChatGPT for SEO, evaluated ChatGPT alternatives, including Claude, and even used both for AI-powered spreadsheet tasks. Now I’m putting in my two cents, sharing what I’ve learned so you can make confident decisions about ChatGPT versus Claude for coding, content creation, and everything in between. Let’s get into the good stuff. Table of contents: Claude vs. ChatGPT: Which is better? Which is better for common marketing workflows, Claude or ChatGPT? Claude vs. ChatGPT for SEO briefs...
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