{"id":208,"date":"2025-04-29T11:00:00","date_gmt":"2025-04-29T11:00:00","guid":{"rendered":"https:\/\/web-stil.info\/?p=208"},"modified":"2025-05-02T21:54:11","modified_gmt":"2025-05-02T21:54:11","slug":"ai-agents-for-marketing-i-talked-to-experts-about-the-benefits","status":"publish","type":"post","link":"https:\/\/web-stil.info\/index.php\/2025\/04\/29\/ai-agents-for-marketing-i-talked-to-experts-about-the-benefits\/","title":{"rendered":"AI agents for marketing \u2014 I talked to experts about the benefits"},"content":{"rendered":"
I love the Back to the Future<\/em> series, especially Part II where we see \u201cthe future.\u201d Of course, the most famous part of our promised 2015 was the Mattel Hoverboard<\/a>. A decade later, and I\u2019m still waiting to glide down the sidewalk on my hoverboard.<\/p>\n The pattern of excitement, overpromising, and then reality isn\u2019t relegated to the movies. Because I\u2019m a marketer, AI tools have flooded my working world with the promise of revolutionizing my department and company.<\/p>\n<\/p>\n The latest push centers on agentic AI. I\u2019ve found AI agents helpful in some capacities \u2014 saving me time, automating repetitive tasks, and assisting with research. But have they reached their full potential? Not yet.<\/p>\n Agentic AI offers impressive advances in technology. But many companies haven\u2019t realized AI\u2019s potential yet \u2014 and they\u2019re still not fully ready to implement agentic AI for maximum benefit.<\/p>\n Let\u2019s 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.<\/p>\n Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n \n Essentially, you give an agentic AI<\/a> 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\u2019s an agent you put into the world to do things.<\/p>\n This is an early space, but demand is growing: The AI agent market is expected to grow to $47 billion by 2030<\/a>. Expect to see more AI agents populating the marketing space soon.<\/p>\n <\/a> <\/p>\n Marketing today demands two things above everything else: speed and personalization.<\/p>\n Audiences always expect better marketing \u2014 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<\/a> and timelines.<\/p>\n Agentic AI fills that gap.<\/p>\n Most people hear \u201cAI\u201d and think of a generative AI tool like ChatGPT or DALL-E. And these AI tools have already influenced digital marketing<\/a> by helping teams brainstorm ideas, draft content, and automate simple tasks. Agentic AI builds on that foundation \u2014 managing the workflows within the team and executing actions with much less human involvement.<\/p>\n Right now, AI agents for marketing show up in four key areas:<\/p>\n 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.<\/p>\n Unlike their chat-based or bot counterparts<\/a>, 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 \u2014 all (mostly) on their own.<\/p>\n Mind you: AI agents don\u2019t replace strategy or creativity. Instead, they give marketers more time and energy to focus on those things.<\/p>\n That said, part of agentic AI\u2019s challenge today is separating its specific use cases from \u201cAI\u201d as a general concept. Many companies are still discovering what AI is<\/em>, let alone how to plug it into their operations and grant it more decision-making authority.<\/p>\n The data reflects this challenge: McKinsey found that, while 55% of organization<\/a>s use generative AI in some capacity, over 80% haven\u2019t seen measurable impacts on enterprise-level earnings. If AI broadly hasn\u2019t driven bottom-line benefits yet, it\u2019s understandable that leaders might hesitate to invest more \u2014 even if agentic AI offers something more advanced.<\/p>\n Gartner projects that 33% of enterprise software will include true agentic AI<\/a> by 2028 \u2014 up from less than 1% today. The potential is clear, as is the utility. But for most marketing teams, agentic AI\u2019s actual power lies just over the horizon.<\/p>\n <\/a> <\/p>\n Even though AI agents haven\u2019t reached their full potential, they offer interesting (if mostly incremental) benefits for any marketing team. The common ones you\u2019ll find are faster content creation, personalized customer experiences, and increased team efficiency.<\/p>\n That said, I asked a few marketing experts about benefits beyond the basics. Here\u2019s what they shared.<\/p>\n Who\u2019s ready to redesign their landing page again? Every marketer who\u2019s undergone that process knows the linear steps you take to pick a target audience, build a campaign, test, and repeat.<\/p>\n Ross Simmonds<\/a>, founder of Foundation Marketing and Distribution AI, sees agentic AI\u2019s power in adding another dimension to the grind of this build-and-test process.<\/p>\n \u201cOne surprising way AI agents are reshaping marketing workflows is by parallelizing variant work,\u201d said Simmonds. \u201cHistorically, marketers tackled tasks like writing landing pages or emails in a linear process: one industry, one page, one campaign at a time.\u201d<\/p>\n \u201cBut with AI agents, you can now create 5-10 variations of the same asset \u2014 tailored by industry, persona, or geography \u2014 simultaneously. What once took days or weeks can now be completed in hours.\u201d<\/p>\n Part of that benefit comes from what Simmonds calls \u201cautonomous quality assurance\u201d \u2014 an important trust-building piece of AI as a teammate.<\/p>\n \u201cTrained AI agents can review documents for brand voice, grammar, tone, and formatting errors at scale,\u201d he said. \u201cInstead of manual checks, these agents can flag inconsistencies across hundreds of assets in minutes, freeing up marketers for more strategic tasks.\u201d<\/p>\n You\u2019ll find plenty of chatter about using agentic AI to handle repetitive marketing tasks. But Sergey Ermakovich<\/a>, CMO at HasData, pushes marketers to widen their thinking on using AI\u2019s data-crunching capabilities for decision-making.<\/p>\n \u201cAn aspect [marketers] don’t think about is its adaptive decision-making,\u201d said Ermakovich. \u201cAI 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.\u201d<\/p>\n This process removes many of the barriers that those repetitive tasks create.<\/p>\n \u201cIt creates a personalized customer journey that optimizes conversion from each moment and interaction,\u201d said Ermakovich. \u201cThe optimization isn’t dependent on scheduled campaigns or A\/B tests.\u201d<\/p>\n Customer segmentation<\/a> has long been a focus of marketing research and tools \u2014 how do you more effectively reach the right people? Anastasia Parokha<\/a>, 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\u2019s a gap in many teams\u2019 marketing strategies.<\/p>\n \u201cModern 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,\u201d she said.<\/p>\n She also notes many marketers still doubt this approach because they worry about uniqueness or authenticity.<\/p>\n My advice is to take a hybrid approach, such as using AI for lower-risk tasks,\u201d said Parokha. \u201cThis 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.\u201d<\/p>\n <\/a> <\/p>\n In my list of the best AI agents for marketing, you\u2019ll notice a theme: workflow automation and assistance. That\u2019s really where the wall is now \u2014 we\u2019re 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.<\/p>\n I\u2019ve found the more specialized the agent\u2019s purpose, the better the results. Such an idea seems straightforward in theory, but it\u2019s much trickier to implement in practice.<\/p>\n That\u2019s why I like HubSpot\u2019s Breeze AI agents<\/a>. You can deploy agents focused entirely on content generation, customer inquiries, prospecting, social media, or knowledge base development.<\/p>\n For instance, I\u2019ve been on a landing page split testing<\/a> kick lately, and retargeting landing page content is a perfect use case for an AI agent.<\/p>\n Source<\/em><\/a><\/p>\n Plus, if you use HubSpot\u2019s 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.<\/p>\n Pricing:<\/strong> 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).<\/p>\n 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.<\/p>\n I really like ZBrain\u2019s \u201cAgent Store,\u201d 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 \u201cplug-n-play\u201d style is fantastic.<\/p>\n Source<\/em><\/a><\/p>\n ZBrain a \u201clow-code\u201d option, but even with the Agent Store I\u2019d recommend at least intermediate levels of AI know-how before investing. It\u2019s a powerful work suite and comes with a heftier price tag to boot. But, when you\u2019re ready to scale agentic work, lean on ZBrain.<\/p>\n Pricing:<\/strong> ZBrain starts at $999\/month, with custom enterprise quoting available.<\/p>\n For a tactical marketing AI assistant, Chatsonic<\/a> by Writesonic does some fine work. It\u2019s built for content creation but extends across the entire process, from generating ideas your audiences like to analyzing performance automatically.<\/p>\n I like Chatsonic\u2019s multimodal approach \u2014 it combines multiple models like ChatGPT, Claude, and Gemini in the content creation process. I\u2019ve found each model to be more adept at certain kinds of writing and other creation tasks, so it\u2019s nice to have it all under one digital umbrella.<\/p>\n Source<\/em><\/a><\/p>\n Pricing:<\/strong> Start for free with Chatsonic or upgrade starting at $16\/month\/user.<\/p>\n 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.<\/p>\n 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\u2019 needs.<\/p>\n Source<\/em><\/a><\/p>\n Like any company-specific offering, I\u2019d advise you to think carefully about integration requirements.<\/p>\n Pricing:<\/strong> Agentforce\u2019s pricing rolls into your Salesforce contract. You can get a dose of Agentforce for free with Salesforce Foundations \u2014 after that, expect a consumption-based pricing model of $2\/conversation.<\/p>\n Relevance AI isn\u2019t totally no-code, but its platform makes creating and launching AI agents much easier than coding them on your own.<\/p>\n For marketing, the company highlights its \u201cAI Lifecycle Marketing Agent<\/a>,\u201d focused on customer research and outreach management. That\u2019s a useful need, especially for smaller teams.<\/p>\n Source<\/em><\/a><\/p>\n Pricing:<\/strong> 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.<\/p>\n If coding isn\u2019t 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\u2019s pre-built modules and templates for common tasks, so you don\u2019t get caught in a building loop of your own.<\/p>\n Source<\/em><\/a><\/p>\n It\u2019s a good place to handle workflows and repetitive tasks \u2014 where agentic AI is most useful now.<\/p>\n Pricing:<\/strong> 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.<\/p>\n <\/a> <\/p>\n I asked several marketing experts to share their experiences and challenges with AI today. Here\u2019s what they told me.<\/p>\n Even with powerful tools and low\/no-code options available, operational integration<\/a> 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.<\/p>\n 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<\/a>, shares more.<\/p>\n \u201cAI tools often struggle to mesh seamlessly with legacy systems, creating data silos that hinder performance,\u201d Fuente said. \u201cAdd to this the technical expertise required for implementation, and it’s clear why adoption rates can lag behind expectations.\u201d<\/p>\n However, integration challenges shouldn\u2019t halt progress forever. Fuente shares her solution for pushing past these barriers.<\/p>\n \u201cWe [marketers] can overcome this by focusing on solutions with dynamic API integrations and partnering with AI specialists for smoother implementation,\u201d she said.<\/p>\n \u201cPilot 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\u2018t just a shiny new tool; it\u2019s a co-worker that thrives on collaboration.\u201d<\/p>\n It\u2019s 10PM \u2014 do you know where your data is? Proper data management<\/a> 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.<\/p>\n Sean Clancy<\/a>, managing director at SEO Gold Coast, shares why specificity of data shared with AI matters.<\/p>\n \u201cThe hard part is training it on what’s actually important. Marketers throw everything at these tools without showing what a \u2018bad\u2019 campaign looks like in context,\u201d he said.<\/p>\n \u201cI’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.\u201d<\/p>\n Clancy continues by noting that\u2019s when marketing teams actually accomplish things with agentic AI.<\/p>\n \u201cYou stop wasting time on things that don\u2018t move the needle, and your team doesn\u2019t need to babysit live campaigns hour by hour,\u201d he said. \u201cIt’s a quiet shift, but it changes how teams catch problems before they become expensive.\u201d<\/p>\n You might build it, but they might not come. I believe employee distrust<\/a> of AI is your biggest barrier to adoption. If people don\u2019t understand, care, or want to use these tools, they\u2019ll flop.<\/p>\n It\u2019s a challenge that Vrutika Patel<\/a>, CMO of Cambay Tiger, met head-on when using AI to run hyper-local campaigns.<\/p>\n \u201cOur team worried about job security and learning curves. We overcame this by starting small \u2014 training staff on one AI tool at a time and celebrating early wins,\u201d she said.<\/p>\n \u201cBegin 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.\u201d<\/p>\n I\u2019ve seen marketers be encouraged to \u201cjust try AI for a bit\u201d and become incredibly frustrated when AI doesn\u2019t behave as expected. But if the marketer doesn\u2019t understand what they\u2019re asking in the first place? AI can\u2019t magically fill the gap; it\u2019s a partner, not a replacement.<\/p>\n And agentic AI does even more processing away from the human operator, which can give it a black-box feel if you\u2019re not careful with implementation.<\/p>\n<\/a><\/p>\n
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Why are AI agents useful in marketing?<\/strong><\/h2>\n
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<\/p>\n
Benefits of Marketing AI Agents<\/h2>\n
Parallelizing Variant Work<\/h3>\n
Adaptive Decision-Making<\/h3>\n
Real-Time Micro-Segmentation<\/h3>\n
Best AI Agents for Marketing<\/h2>\n
Breeze AI by HubSpot<\/a><\/h3>\n
<\/p>\n
ZBrain AI Agents<\/a><\/h3>\n
<\/p>\n
Chatsonic<\/a><\/h3>\n
<\/p>\n
Agentforce<\/a><\/h3>\n
<\/p>\n
Relevance AI Agents<\/a><\/h3>\n
<\/p>\n
SmythOS<\/a><\/h3>\n
<\/p>\n
Challenges of Using AI Agents in Marketing<\/h2>\n
Agentic AI Integration<\/h3>\n
Data Hygiene and Management<\/h3>\n
Staff Resistance<\/h3>\n
Understanding AI as a Partner<\/h3>\n