AI agents for marketing: Top use cases and how to build them

Marketing teams are under pressure from every direction: tighter budgets, more channels that need content creation support, higher personalization expectations, and faster campaign cycles. At the same time, the marketing tech stack keeps expanding.

AI tools promised relief from the many manual processes of traditional marketing, but we’re at the dawn of yet another chapter in the evolution: AI agents for marketing.

Unlike standalone marketing automation features that generate copy on demand, or chatbots that react to prompts, AI agents are proactive, goal-driven systems. They can monitor data, trigger workflows, take action across tools, and continuously improve campaign performance or optimize conversion rates—all with minimal human oversight.

For marketing leaders, this raises a few big questions:

1. Where do AI agents actually create value?

2. How can you build them without adding more complexity to your stack?

3. How do agents use LLMs (large language models) to make appropriate decisions?

In this guide, we’ll explore practical use cases, implementation strategies, and how to build AI agents with care—no code necessary—using Airtable.

What are AI agents for marketing?

AI agents for marketing are autonomous or semi-autonomous systems that execute marketing tasks based on goals, rules, and real-time data. Unlike simple AI assistants that respond to prompts, AI-powered agents can:

Think of them as digital teammates embedded inside your workflows that support the human team. For example, instead of manually reviewing brand consistency across assets, an AI agent could:

The key distinction is autonomy. Agents don’t just respond, they are trained to take action. In a marketing context, that means automating parts of complex digital marketing workflows, such as:

When built inside a connected workspace like Airtable, agents operate directly within your source of truth, reducing tool sprawl and manual coordination.

Why AI agents in marketing are important

Complexity has grown exponentially in modern marketing orgs.

Yet most marketing teams are still operating with manual handoffs and fragmented tools. AI agents address three core challenges facing those teams: speed, scale, and insight.

1. Speed

With campaigns moving quickly, artificial intelligence can save valuable hours and days. AI agents reduce time spent on repetitive tasks—drafting briefs, reviewing copy, summarizing insights—so teams can focus on creativity and strategy.

2. Scale

Localization, content variations, and personalization used to require large teams. Agents can generate and adapt high-quality content at scale when trained properly.

3. Insight

Customer feedback, survey data, and support tickets contain powerful signals—but extracting insights manually is time-consuming. AI agents surface patterns instantly. Instead of hiring more coordinators or adding more point solutions, marketers can embed intelligence directly into their workflows.

How AI agents are used in marketing

Let’s explore five real-world, high-impact use cases for AI agents in marketing from Airtable’s own AI Plays.

1. Localizing images and copy

Global marketing teams constantly localize campaigns for different regions, languages, and audience segments. Traditionally, this involves:

An AI agent can automate much of this process. For example, it can be trained to:

When integrated into a structured system like Airtable, the agent can also:

The result: faster, more globally inclusive launches and fewer bottlenecks.

Try the AI Play.

2. Performing brand reviews

Maintaining brand consistency across hundreds of assets is a major challenge. AI agents can:

For instance, if your brand voice prohibits certain words or requires specific product naming conventions, an agent can scan all outgoing materials before publication.

Instead of manual QA cycles, marketers get real-time feedback embedded directly in their workflow. This amplifies the power of brand teams rather than replacing them.

Try the AI Play.

3. Generating campaign ideas (and content)

Creative ideation is one of the most exciting uses of AI agents, supporting the creative process from brainstorming through campaign optimization. With proper training, an agent can:

Unlike static AI tools, an agent can also:

Inside Airtable, agents can connect campaign performance data, creative briefs, and asset libraries, turning raw data that’s already in the company ecosystem into actionable creative concepts.

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4. Discovering marketing insights in customer feedback

Customer feedback often lives in multiple places across the customer journey:

It’s inefficient and time-consuming to manually read and categorize this data to analyze the quality of the customer experience or the strength of the company’s customer engagement approach. Here, an AI agent can:

For marketing teams, this means:

Instead of quarterly analysis projects, insights become continuous.

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5. Creating campaign status briefings

Marketing leaders need to stay current on dozens of campaigns, launches, and initiatives — but pulling together a coherent executive update takes hours of digging through spreadsheets, Slack threads, and project trackers.

With AI marketing agents, that work happens automatically.

AI agents can:

Marketing teams spend less time formatting updates and more time acting on them. And when leadership has the right information at the right time, campaigns move faster.

Try the AI Play.

How to choose the best AI agent for marketers

While it’s possible for an in-house IT or developer team to create custom AI agents for your marketing org, this will likely take up time and resources they don’t have to spare. Luckily, many companies, including Airtable, have out-of-the-box agentic AI tools.

But not all AI tools are built for agentic workflows. When evaluating AI agents for marketing, here are some functions to look for that go beyond pricing:

1. Workflow integration

Does the agent operate inside your existing systems? Or does it require exporting data into another tool? The best AI agents live where your data lives.

2. Structured data access

Agents need access to clean, organized data. Platforms that combine databases and automation provide better results.

3. Automation triggers

Look for agents that can trigger based on events, such as status changes, new entries, performance thresholds.

4. Customizability

Look for a tool that enables you to define goals, constraints, and guardrails for the agents. Marketing workflows require flexibility that must be built into the agents' continuous learning.

5. Governance and permissions

Agents should know what they're allowed to touch — and what they're not. Prioritize platforms that enforce role-based permissions and make their security model easy to inspect and trust.

Airtable’s combination of databases, automation, and AI makes it particularly well-suited for building controlled, workflow-aware marketing agents.

How to build AI agents for marketing

Building AI agents no longer requires a machine-learning team. Here’s a simplified framework.

Step 1: Define the goal

Agents work best when they’re given specific objectives. Start with a clear outcome, for example:

Step 2: Centralize your data

Agents need structured inputs. AI agent builders like Airtable act as a connected data layer across marketing operations, consolidating:

Step 3: Create automation triggers

Define when the agent should act or flag for human review. For example:

Step 4: Embed AI logic

Use AI models to:

Step 5: Add human oversight

Marketing is strategic, and human teams are best at that work. Agents should assist, not replace their decision-making. Include human oversight in AI-driven processes, such as at key points in approval workflows, campaign orchestration, or performance tracking.

The future of agents for marketing

AI agents are evolving beyond automation into intelligent collaborators. In the near future, marketing agents will:

Instead of isolated AI features, teams will operate within intelligent systems where:

While better campaigns and more time in the day for strategy and creativity are nice pluses, the real impact is operational transformation, moving marketing from reactive to proactive.

Build AI agents without code with Airtable

You don’t need engineering resources to build AI agents. With Airtable, marketing teams can build AI agents in minutes.

Instead of adding another AI point solution, you embed intelligence directly into your marketing system of record. Whether you’re building a localization agent, campaign-deation agent, feedback analysis agent, or a brief-generation agent, you can do it without writing code.

Start building custom AI agents and intelligent workflows today.