AI agents in marketing

In fact, 50% of companies that currently use generative AI will initiate agentic AI pilot programs in 2025.¹

Unlike earlier tools, AI agents can perform complex tasks with less human interaction. They support a wide range of marketing functions including customer engagement, content creation, campaign management and performance analysis.

To understand how marketers use AI agents, it helps to first understand AI agents themselves. This starts with knowing the difference between two key types of AI: generative AI and agentic AI. Generative AI creates original content based on a user’s prompt. Agentic AI can decide and act on its own, pursuing complex goals with little supervision.

AI assistants exist along a continuum. On one end are rule-based chatbots that follow predefined scripts. Followed by more advanced virtual assistants, and then assistants powered by generative AI and large language models (LLMs), which can handle single-step tasks. At the top of this progression are AI-powered agents, which operate autonomously. These agents make decisions, design workflows and use function calling to connect with external tools to fill gaps in their knowledge.

AI agents go far beyond simple marketing automation. While AI tools like basic chatbots might deliver scripted responses, AI agents can interpret input, reason through options and make context-aware decisions across multiple platforms. They adapt their behavior over time and can break large goals into smaller steps, supporting complex marketing strategies with minimal oversight.

For example, IBM’s own transformation team collaborated with IBM HR to deliver a simplified, personalized and data-driven employee self-service experience. Now 94% of IBM’s company-wide, lower-level HR queries are answered by our AskHR digital agent, freeing up HR professionals to focus on more complex issues.²

To accomplish such results, AI agents rely on a combination of technologies. Machine learning helps them recognize patterns and make predictions. Natural language processing (NLP) enables them to understand and generate human language, and generative AI gives them the ability to create original content.

These agents are typically powered by AI models trained on large datasets to support reasoning and personalization. Just as important is their connection to external systems—like customer relationship management (CRM) and application programming interface (API)—which allows them to pull relevant data, personalize interactions and act in real-world environments.

Rather than just answering a product question, for example, an AI agent might recognize a customer’s intent to purchase based on the line of questions and collected customer data. The agent can then summarize key features, offer a discount and follow up with personalized product recommendations. The agent takes these steps without receiving explicit prompts.

A single AI agent might handle a relatively simple repetitive task like updating CRM records or replying to a customer query. In more advanced applications, multiagent systems act like intelligent teams. These agents can delegate subtasks, share information and coordinate across tools to complete complex workflows, including planning campaigns, generating content variations, distributing materials and analyzing performance.

This ability to collaborate sets AI agents apart from traditional assistants. By sharing insights and dividing responsibilities, agents can handle interdependent tasks and carry context from one process to another—making marketing operations more intelligent, adaptive and efficient.

Agentic AI systems are impressive. But they pose complex governance challenges due to their autonomous, opaque decision-making and vulnerability to bias, cybersecurity threats and regulatory gaps. Their reliance on machine learning and external APIs makes it difficult to ensure fairness, accountability and privacy.

Organizations must expand beyond traditional governance practices to manage these risks. Safeguards like AI sandboxing, stress testing, agent-to-agent monitoring, emergency shutdown mechanisms, human oversight and governance systems like the IBM® watsonx.governance® toolkit should be adopted. As AI systems scale, responsible governance is critical to ensuring their safe, ethical and effective use.³

According to IBM-commissioned research, customer care and sales support are two use cases where AI agents are expected to have the biggest initial impact to performance gains and ROI.⁴ A Gartner report predicts that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.⁵

AI agents give marketers a powerful new tool. Marketers can now delegate real decision-making and execution to systems that learn, reason and perform at scale. An AI marketing agent can analyze customer data. It can write and send personalized messages. It can manage ad campaigns and adjust strategies. The agent does all this work without needing constant human guidance.

These use cases span key marketing functions. They include email marketing, social media, SEO and pricing strategy. These capabilities make AI a major force in driving modern marketing performance.²

For example, a simple chatbot might respond with a scripted answer when someone types “What’s your return policy?” An AI agent can do much more. It might recognize that a customer is likely to make a purchase and proactively offer help. It can also summarize product details and personalize a follow-up, tailoring its behavior based on what it learns over time.

Rather than just speeding up old workflows, AI agents are redefining what marketing teams can achieve. They represent a new generation of intelligent digital marketing tools that support creative and customer engagement strategies and can take on the complexity of data analysis and execution as proactive collaborative partners.  

AI agents are important in marketing efforts because they fundamentally reshape how decisions are made and actions are taken. Traditional marketing systems, even when automated, depend heavily on predefined rules and human-driven campaign planning. These approaches struggle to keep up with the speed, volume and variability of modern consumer behavior.

AI agents are designed to reason, learn and act autonomously. This gives marketers the ability to respond to individual customer needs in real time and at scale.

Their importance lies not just in automation, but in their potential to act as adaptive intermediaries between brands and consumers. As the marketing process becomes more fragmented—with users navigating across platforms, channels and devices—AI agents serve as intelligent entities capable of interpreting context and engaging and guiding users through the customer journey.

They can continuously update their strategies based on new information, improving performance across key metrics like rankings and conversion rates while maintaining consistency with the brand voice.

AI agents also address a growing strategic challenge: the limits of human attention and organizational capacity. As the number of channels, tools and data sources grows, it’s not practical for marketing teams to synthesize all this information in real time. AI agents operate continuously and autonomously, which lets marketing systems become more agile.

They reduce the need for human teams to micromanage every element of execution, allowing them to focus on higher-level planning, brand strategy and creative direction.

AI agents shift the marketing operational model—from one based on periodic, static campaigns to a dynamic, continuous and intelligent system. This change allows organizations to become more adaptive, responsive and efficient in serving customers and achieving business goals. This kind of end-to-end transformation is especially valuable for businesses with complex, multitouch customer journeys, like SaaS providers, e-commerce platforms and financial services companies.

Marketers use AI agents as intelligent copilots to manage tasks that would otherwise require manual effort, ongoing monitoring or large-scale coordination. Here are some examples of how they’re applied in AI-driven marketing initiatives:

AI agents improve how brands interact with customers through intelligent chat interfaces. Unlike traditional rule-based chatbots, these agents understand natural language, track the context of ongoing conversations and respond in a more human-like way.

They can guide users through complex tasks like product selection or troubleshoot service issues, often enhanced by integration with LLM tools like ChatGPT, which further expands their conversational capabilities.

A specialized example is a knowledge base agent, which dynamically updates self-service knowledge resources to empower customers to solve issues independently.⁶

AI agents can analyze user behavior, preferences and history to generate tailored messages for everyone, instead of, for example, manually designing dozens of ad variations for email campaigns to different audience segments. These agents use natural language generation to craft unique copy and machine learning to determine which content will most likely lead to a click, a conversion or deeper engagement.

Taking a step further, a personalized video spokesperson agent generates customized videos that greet users by name and reference their specific interests, adding a human feel to dynamic content.⁶

AI agents can run and refine marketing campaigns autonomously and take on tasks traditionally handled by large teams. They can automatically manage media buying across platforms like search engines, display networks and social networks like LinkedIn. They monitor real-time performance data—such as impressions, click-through rates and campaign performance—and then adjust bids, targeting criteria and budget allocation to meet the campaign’s goals.

Agentic AI operates continuously and without fatigue. Overnight it might, for example, adjust a large company’s advertising campaign. It might decide about the advertising spending and create and post content. A strategy agent would play a vital role here, optimizing campaign structures based on predictive analytics and past performance data.⁶

AI agents can process large volumes of market data, customer feedback and campaign results to generate insights or make recommendations. Some agents can even coordinate with each other—one managing content creation, another handling distribution and a third evaluating performance. This orchestration creates a smarter, more connected marketing system that can learn and adjust over time, contributing to strategic frameworks and case studies that demonstrate results.

For example, a trends and insights agent can mine behavioral data and sentiment analysis to surface emerging market trends. At the same time, a social listening agent actively monitors online conversations and engages with audiences in real time to detect and respond to shifts in brand perception.⁶

Internally, AI agents streamline marketing operations by automating internal tasks like content generation, reporting and performance tracking. They can draft copy variations, analyze engagement patterns and recommend next actions while coordinating across tools and platforms.

As they evolve, they increasingly serve as bridges between tools and systems and help marketers manage complex, cross-functional processes more efficiently. This includes integrating FAQs, customer workflows and outreach campaigns.

For example, a content generation agent can produce personalized, contextually relevant material across text, images and videos. A multimodal creative brief agent can convert campaign goals into comprehensive creative briefs with sample messaging, design direction and audience insight by using reusable templates to support consistency and speed.⁶

Some of the key ways AI agents are improving and transforming how marketing teams work are:

Continuous improvement: AI agents monitor and analyze performance data in real time, allowing them to autonomously test, learn and adjust campaigns without waiting for human input. This enables constant fine-tuning of messaging, creative and budget and results in better output.

Improved customer engagement: Rather than providing scripted responses like traditional chatbots, AI agents can understand context, reason through customer needs and carry conversations across sessions and channels, creating more helpful and relevant customer interactions.

Reduced complexity across systems: Marketing systems tend to be disjointed, making coordination more difficult. AI agents act as intelligent middle layers, stitching together tools, data and workflows to automate processes that would otherwise require multiple teams or platforms to coordinate.

Scalable execution with lower overhead: AI agents can manage thousands of microdecisions and tasks simultaneously, such as message testing or targeting adjustments, without increasing headcount. They can also integrate with platforms like HubSpot, Salesforce and other CRM solutions to support end-to-end campaign management, allowing smaller teams to efficiently run sophisticated marketing strategies.

Enhanced decision support: In addition to running tasks, some AI agents provide strategic recommendations, flag issues and offer suggestions, augmenting human judgment rather than replacing it.

Always-on marketing: Brands that use AI agents can more easily maintain active campaigns and all-day customer interaction. AI agents don’t take breaks, so customer experiences, updates and analytics keep running even when human teams are offline.

AI and AI agents cannot fully replace human marketers and likely won’t for the foreseeable future. Instead, they are transforming what marketing teams focus on and how they work. Here are some of the advancements in AI that we should see within ten years that affect marketing and other industries:⁷

Multimodal status quo: Multimodal AI, which processes and integrates multiple forms of input like text, voice, images and video, will become far more refined. This advancement will enable AI to better mimic human communication and power intelligent virtual assistants capable of understanding complex, context-rich queries and responding with personalized, multimodal outputs like diagrams, voice instructions or video demonstrations.

Democratization of AI and easier model creation: AI development will become increasingly accessible through no-code and low-code platforms, automated machine learning tools and plug-and-play APIs. Entrepreneurs, hobbyists and businesses can benefit from faster innovation cycles, AI-assisted workflows and cloud-based, customizable models. These advancements can empower nonexperts to create and deploy custom AI solutions, fostering creativity.

Hallucination insurance: As AI becomes integral to critical sectors, organizations might adopt “AI hallucination insurance” to protect against financial, legal or reputational damage caused by erroneous outputs. These policies would cover risks from generative AI mistakes, such as misinformation, faulty recommendations or biased decisions and help institutionalize safeguards around model reliability.

AI in the C-suite: AI will serve as a strategic advisor in executive settings, providing real-time, data-driven insights and scenario simulations to support business decisions. Advanced systems will streamline cross-departmental coordination, offer predictive planning and elevate small businesses with tools traditionally available only to large enterprises. AI will essentially become a trusted partner to leadership.

AI ethics and regulation: Global AI regulations will evolve to match the technology’s rapid pace. These regulations will emphasize transparency, robustness and human oversight, especially in high-risk applications. Ethical concerns like bias, privacy and accountability will drive policies that classify AI by risk level, enforce safety standards and prohibit harmful use cases such as mass surveillance or social scoring.

Data usage and governance: With real-world data becoming harder to obtain ethically and at scale, synthetic data will become standard for training AI. Organizations will prioritize proprietary datasets and rigorous quality assurance to create customized models. Stricter governance will be implemented to control “shadow AI” and ensure approved systems handle sensitive information responsibly.