AI agents for marketing — I talked to experts about the benefits

I love the Back to the Future series, especially Part II where we see “the future.” Of course, the most famous part of our promised 2015 was the Mattel Hoverboard. A decade later, and I’m still waiting to glide down the sidewalk on my hoverboard.

The pattern of excitement, overpromising, and then reality isn’t relegated to the movies. Because I’m a marketer, AI tools have flooded my working world with the promise of revolutionizing my department and company.

The latest push centers on agentic AI. I’ve found AI agents helpful in some capacities — saving me time, automating repetitive tasks, and assisting with research. But have they reached their full potential? Not yet.

Agentic AI offers impressive advances in technology. But many companies haven’t realized AI’s potential yet — and they’re still not fully ready to implement agentic AI for maximum benefit.

Let’s talk about where AI agents fit into marketing today, the real benefits they can deliver to your marketing team, and what the future could hold.

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Essentially, you give an agentic AI a goal and allow it to figure out what to do and then do it. An AI agent goes beyond basic automation by adapting and responding to tasks without human prompting. It’s an agent you put into the world to do things.

This is an early space, but demand is growing: The AI agent market is expected to grow to $47 billion by 2030. Expect to see more AI agents populating the marketing space soon.

Why are AI agents useful in marketing?

Marketing today demands two things above everything else: speed and personalization.

Audiences always expect better marketing — they want their marketing messages to feel timely, relevant, and real to their experience. But marketing teams are stretched thin, being asked to deliver that individualized experience at scale across more customer segments, channels, and product lines. And, of course, to do so with tighter marketing budgets and timelines.

Agentic AI fills that gap.

Most people hear “AI” and think of a generative AI tool like ChatGPT or DALL-E. And these AI tools have already influenced digital marketing by helping teams brainstorm ideas, draft content, and automate simple tasks. Agentic AI builds on that foundation — managing the workflows within the team and executing actions with much less human involvement.

Right now, AI agents for marketing show up in four key areas:

The common thread across these use cases is workflow automation. Agentic AI offers marketing teams freedom from draining, repetitive work tasks like drafting social copy, pulling reports, or triggering customer messages.

Unlike their chat-based or bot counterparts, AI agents can run with greater autonomy within their pre-designed boxes. Once set up, they can listen for triggers, take action, and adjust outputs based on real-time data — all (mostly) on their own.

Mind you: AI agents don’t replace strategy or creativity. Instead, they give marketers more time and energy to focus on those things.

That said, part of agentic AI’s challenge today is separating its specific use cases from “AI” as a general concept. Many companies are still discovering what AI is, let alone how to plug it into their operations and grant it more decision-making authority.

The data reflects this challenge: McKinsey found that, while 55% of organizations use generative AI in some capacity, over 80% haven’t seen measurable impacts on enterprise-level earnings. If AI broadly hasn’t driven bottom-line benefits yet, it’s understandable that leaders might hesitate to invest more — even if agentic AI offers something more advanced.

Gartner projects that 33% of enterprise software will include true agentic AI by 2028 — up from less than 1% today. The potential is clear, as is the utility. But for most marketing teams, agentic AI’s actual power lies just over the horizon.

Benefits of Marketing AI Agents

Even though AI agents haven’t reached their full potential, they offer interesting (if mostly incremental) benefits for any marketing team. The common ones you’ll find are faster content creation, personalized customer experiences, and increased team efficiency.

That said, I asked a few marketing experts about benefits beyond the basics. Here’s what they shared.

Parallelizing Variant Work

Who’s ready to redesign their landing page again? Every marketer who’s undergone that process knows the linear steps you take to pick a target audience, build a campaign, test, and repeat.

Ross Simmonds, founder of Foundation Marketing and Distribution AI, sees agentic AI’s power in adding another dimension to the grind of this build-and-test process.

“One surprising way AI agents are reshaping marketing workflows is by parallelizing variant work,” said Simmonds. “Historically, marketers tackled tasks like writing landing pages or emails in a linear process: one industry, one page, one campaign at a time.”

“But with AI agents, you can now create 5-10 variations of the same asset — tailored by industry, persona, or geography — simultaneously. What once took days or weeks can now be completed in hours.”

Part of that benefit comes from what Simmonds calls “autonomous quality assurance” — an important trust-building piece of AI as a teammate.

“Trained AI agents can review documents for brand voice, grammar, tone, and formatting errors at scale,” he said. “Instead of manual checks, these agents can flag inconsistencies across hundreds of assets in minutes, freeing up marketers for more strategic tasks.”

Adaptive Decision-Making

You’ll find plenty of chatter about using agentic AI to handle repetitive marketing tasks. But Sergey Ermakovich, CMO at HasData, pushes marketers to widen their thinking on using AI’s data-crunching capabilities for decision-making.

“An aspect [marketers] don't think about is its adaptive decision-making,” said Ermakovich. “AI scans through first-party data at scale. Then, change customer segmentation depending on behavioral triggers. It can shift a customer to a high-intent audience segment after they abandon their cart. The adjustment happens in real-time and at a frequency and precision that a human team cannot match.”

This process removes many of the barriers that those repetitive tasks create.

“It creates a personalized customer journey that optimizes conversion from each moment and interaction,” said Ermakovich. “The optimization isn't dependent on scheduled campaigns or A/B tests.”

Real-Time Micro-Segmentation

Customer segmentation has long been a focus of marketing research and tools — how do you more effectively reach the right people? Anastasia Parokha, head of marketing at Creative Fabrica, sees an opportunity to get incredibly tactical by using AI for real-time micro-segmentation. And she thinks it’s a gap in many teams’ marketing strategies.

“Modern AI models are trained to analyze user behavior in real time and even adjust your content. Now, you can create specific micro-groups of audiences that help you personalize content,” she said.

She also notes many marketers still doubt this approach because they worry about uniqueness or authenticity.

My advice is to take a hybrid approach, such as using AI for lower-risk tasks,” said Parokha. “This could be A/B testing or copywriting for emails. After that, you can expand the role of AI in marketing because you will be the one to train it. The key is to collaborate and continuously improve the artificial intelligence models you use in your work.”

Best AI Agents for Marketing

In my list of the best AI agents for marketing, you’ll notice a theme: workflow automation and assistance. That’s really where the wall is now — we’re waiting to cross the autonomy threshold. But, in the meantime, these are solid tools to help your marketing team save time, enhance personalization, and optimize campaigns with far less manual work.

Breeze AI by HubSpot

I’ve found the more specialized the agent’s purpose, the better the results. Such an idea seems straightforward in theory, but it’s much trickier to implement in practice.

That’s why I like HubSpot’s Breeze AI agents. You can deploy agents focused entirely on content generation, customer inquiries, prospecting, social media, or knowledge base development.

For instance, I’ve been on a landing page split testing kick lately, and retargeting landing page content is a perfect use case for an AI agent.

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Plus, if you use HubSpot’s platform, your internal data can inform more tailored answers for customers and better results for your team. No confusing integration points or additional tools required.

Pricing: Some parts of Breeze AI, like Copilot, are available for free with a HubSpot plan. These advanced agents need a Professional plan (starting at $800/month) or Enterprise plan (starting at $3,600/month).

ZBrain AI Agents

ZBrain AI agents are great options for AI power users and enterprise-level buyers. Integrating AI agents is one of the largest hurdles facing enterprises, and ZBrain can help solve that problem.

I really like ZBrain’s “Agent Store,” with a gigantic selection of pre-built and curated agents. Technical proficiency needs can slow down many enthusiastic AI adopters within the enterprise setting, so having it laid out so “plug-n-play” style is fantastic.

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ZBrain a “low-code” option, but even with the Agent Store I’d recommend at least intermediate levels of AI know-how before investing. It’s a powerful work suite and comes with a heftier price tag to boot. But, when you’re ready to scale agentic work, lean on ZBrain.

Pricing: ZBrain starts at $999/month, with custom enterprise quoting available.

Chatsonic

For a tactical marketing AI assistant, Chatsonic by Writesonic does some fine work. It’s built for content creation but extends across the entire process, from generating ideas your audiences like to analyzing performance automatically.

I like Chatsonic’s multimodal approach — it combines multiple models like ChatGPT, Claude, and Gemini in the content creation process. I’ve found each model to be more adept at certain kinds of writing and other creation tasks, so it’s nice to have it all under one digital umbrella.

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Pricing: Start for free with Chatsonic or upgrade starting at $16/month/user.

Agentforce

Salesforce has recently thrown a lot of its weight behind agentic AI integrated into its suite. Agentforce provides agentic assistance for automating customer service, sales, and marketing operations.

If you keep your Salesforce databases updated, you have tons of data at your disposal for conversational AI tools and predictive analytics to anticipate your customers’ needs.

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Like any company-specific offering, I’d advise you to think carefully about integration requirements.

Pricing: Agentforce’s pricing rolls into your Salesforce contract. You can get a dose of Agentforce for free with Salesforce Foundations — after that, expect a consumption-based pricing model of $2/conversation.

Relevance AI Agents

Relevance AI isn’t totally no-code, but its platform makes creating and launching AI agents much easier than coding them on your own.

For marketing, the company highlights its “AI Lifecycle Marketing Agent,” focused on customer research and outreach management. That’s a useful need, especially for smaller teams.

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Pricing: Relevance AI will give you 100 credits per day on its free plan. The Team Plan will run you $199/month with 100,000 credits for some real agentic horsepower.

SmythOS

If coding isn’t your jam, SmythOS offers a solid no-code platform to help your team build and deploy AI agents. You assemble your agent using a drag-and-drop interface, making it a more visually appealing process (and less complex). I like SmythOS’s pre-built modules and templates for common tasks, so you don’t get caught in a building loop of your own.

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It’s a good place to handle workflows and repetitive tasks — where agentic AI is most useful now.

Pricing: You can use SmythOS on a limited free plan or jump into a paid plan starting at $39/month. It also scales from startup to enterprise sizes, depending on your needs.

Challenges of Using AI Agents in Marketing

I asked several marketing experts to share their experiences and challenges with AI today. Here’s what they told me.

Agentic AI Integration

Even with powerful tools and low/no-code options available, operational integration remains a massive hurdle to clear. As companies grow their staff count and tech stack, the number of integration points expands faster than some people expect.

When it comes time to integrate a new resource like agentic AI, marketing leaders can hit some difficult walls. Jose Fuente, marketing lead at SYMVOLT, shares more.

“AI tools often struggle to mesh seamlessly with legacy systems, creating data silos that hinder performance,” Fuente said. “Add to this the technical expertise required for implementation, and it's clear why adoption rates can lag behind expectations.”

However, integration challenges shouldn’t halt progress forever. Fuente shares her solution for pushing past these barriers.

“We [marketers] can overcome this by focusing on solutions with dynamic API integrations and partnering with AI specialists for smoother implementation,” she said.

“Pilot programs are also invaluable as they allow teams to test and refine processes before scaling up. The broader trend here is about shifting mindsets. AI isn‘t just a shiny new tool; it’s a co-worker that thrives on collaboration.”

Data Hygiene and Management

It’s 10PM — do you know where your data is? Proper data management was hard enough before AI tools clamored for access. Without clear structures and guidelines for data collection, management, and use, agentic AI can stall out before it hits velocity.

Sean Clancy, managing director at SEO Gold Coast, shares why specificity of data shared with AI matters.

“The hard part is training it on what's actually important. Marketers throw everything at these tools without showing what a ‘bad’ campaign looks like in context,” he said.

“I've seen better results when teams feed in a few messy past campaigns first. Let the agent learn from those before giving it new material. This makes the checks more relevant and the alerts more useful.”

Clancy continues by noting that’s when marketing teams actually accomplish things with agentic AI.

“You stop wasting time on things that don‘t move the needle, and your team doesn’t need to babysit live campaigns hour by hour,” he said. “It's a quiet shift, but it changes how teams catch problems before they become expensive.”

Staff Resistance

You might build it, but they might not come. I believe employee distrust of AI is your biggest barrier to adoption. If people don’t understand, care, or want to use these tools, they’ll flop.

It’s a challenge that Vrutika Patel, CMO of Cambay Tiger, met head-on when using AI to run hyper-local campaigns.

“Our team worried about job security and learning curves. We overcame this by starting small — training staff on one AI tool at a time and celebrating early wins,” she said.

“Begin with a clear problem to solve. For us, it was proving our freshness claims to specific neighborhoods. We matched delivery speed data with customer locations to create tailored messages that resonated with local buyers. This story-driven approach works because customers connect with authentic, relevant messaging.”

Understanding AI as a Partner

I’ve seen marketers be encouraged to “just try AI for a bit” and become incredibly frustrated when AI doesn’t behave as expected. But if the marketer doesn’t understand what they’re asking in the first place? AI can’t magically fill the gap; it’s a partner, not a replacement.

And agentic AI does even more processing away from the human operator, which can give it a black-box feel if you’re not careful with implementation.

Tim Hanson, CMO at Penfriend, calls this the “understanding gap.”

“I‘ve witnessed countless marketing teams throw vague prompts at AI and then complain that ‘AI doesn’t work’ when the output isn't what they imagined,” said Hanson.

“The AI never did it wrong; they just didn‘t know the process well enough themselves to explain it properly. The uncomfortable truth is that AI exposes our knowledge gaps. If you can’t clearly articulate every step of how you‘d create something manually, you can’t effectively delegate it to AI.”

Hanson continues with an answer to this marketing conundrum.

“The solution is counterintuitive — to use AI effectively, you need to first get better at doing things manually,” he said.

“I had this exact experience when I started with AI. I was getting mediocre results until I realized I needed to map out processes I knew intimately first. Once I started with processes I could explain step by step (like competitive content analysis), suddenly I was getting exceptional results.”

“Start with a process you know cold, map out every decision point, and use that as your foundation for AI integration. Only then expand to more complex workflows.”

Prepared marketing teams will benefit the most from AI agents.

While I wait for my hoverboard, I hang onto the excitement and enthusiasm for new ideas that have driven my marketing career — especially with AI.

Like any new marketing idea or technology, agentic AI follows the same pattern. Tools improve and promises grow. But the real marketing work stays the same: Build good systems, craft strong strategies, and solve for your customer.

AI agents aren’t science fiction anymore, but we’re not quite at the hoverboard either. In this in-between state, agentic AI can help marketing teams, and they’re getting smarter. The potential to change how we work is real.

But what I learned most from researching AI agents is that the future of marketing won’t belong to teams chasing shiny new tools. The teams that’ll win with agentic AI will build readiness: organized data, clear processes, well-mapped workflows, and a culture that embraces testing and learning.