How to adopt AI for content marketing: 2025 guide

Brands are radically changing their approach to content marketing now that artificial intelligence (AI) and generative AI (GenAI) are widely available and embedded within so many technologies. Notably, AI allows content marketing teams to scale content production and create bespoke  content for target audiences at an incredible pace. 

As we’ll explore in this guide (which, yes, was influenced by AI), there are many ways that content marketers can effectively adopt AI tools and strategies to enhance processes, boost audience engagement, and drive measurable results in 2025.

What is AI in content marketing?

AI in content marketing uses intelligent technologies to create, refine, distribute, and analyze marketing content. These tools act as sophisticated assistants (or, some say, digital buddies or interns) that can automate repetitive tasks, fuel search engine optimization (SEO), spot patterns in data, and personalize content—saving you time and helping to ensure that your content strategy is efficient and effective.

The role of AI in content marketing: Common use cases

Many marketers are already using AI. According to a 2024 survey from Statista, roughly 42% of marketers use AI tools a few times a week for writing or content generation, and 40% reported using AI for social media content creation. 

Similarly, SurveyMonkey found that 51% of marketers were using AI to optimize content (51%), create content (50%), brainstorm content ideas (45%), automate repetitive tasks and processes (43%), and analyze data (41%).

Now, many content marketers love to write and create—that’s generally what we’re here for, so these stats may seem discouraging. But consider that AI can help raise the bar across the board, remove tedious legwork, and prompt you to think about how you can go deeper and more strategically represent your brand throughout the content creation process. 

Here are five ways to collaborate with AI, or even to use it to build an entire enterprise content marketing strategy:

Content creation and ideation

AI content marketing tools can brainstorm data-driven content ideas by analyzing what’s trending, evaluating competitor content, and accounting for customer pain points. GenAI can produce initial drafts of blog posts, copy for social media campaigns, email newsletters, podcasts, or provide a list of headlines to play with. It can also help with generating image assets. Typically, these outputs are not ready to publish as-is, but they’re a good starting point and allow you to further refine.

Content optimization

AI-powered content optimization tools analyze existing content and suggest improvements for readability, search rankings, and audience engagement. They can identify keyword opportunities, recommend structural adjustments for clarity and cohesion, and highlight areas that could be expanded or streamlined in real time.

Personalization and audience segmentation

Content marketers can use AI to summarize voice of the customer data to identify preferences and behaviors, or common pain points from call transcripts, which leads to more personalized content experiences. Some systems can automatically group audiences based on user actions, interests, and engagement history, and then deliver tailored content to each segment. This level of customization was previously impossible to achieve at scale without an enormous amount of manual effort.

Content distribution and promotion

AI can help determine the best channels, formats, and timing for sharing each piece of content by analyzing historical performance. This is helpful as you build integrated marketing campaigns or social media marketing plans, for example. It can also help with related aspects, such as localization. Systems designed for distribution can predict which distribution strategies will generate the highest engagement for specific content types and audience segments. Automated testing continually refines these predictions, improving effectiveness over time.

Performance analysis and insights

AI-driven analytics can track conversions across complex customer journeys, identify which content elements or assets drive engagement, and predict the potential impact of content adjustments, providing a deeper understanding of content performance than traditional tools. Natural language processing capabilities can help content marketers understand the more qualitative aspects of audience response through automated sentiment analysis.

3 benefits of using AI for content marketing

Some companies may use general-purpose tools like ChatGPT or Claude, but there are also purpose-built content marketing platforms designed specifically for content teams—some of which integrate AI in the context of your own data and workflows. It’s important to assess the tools available and identify the features that best support your content strategy.

1. Enhanced productivity and efficiency

AI can significantly reduce the time spent on content production and research, whether you’re creating briefs, templates, landing pages, social media posts, or complete drafts of long-form reports or articles. It’s ultimately about choosing where to spend your time: on prompting and refinement, or fact checking and editing, or just to quickly gain a fresh perspective or a few headline options. It also helps with operational tasks, like triaging requests from stakeholders as you build out your content calendar or social media calendar.

2. Improved content personalization and relevance

By analyzing individual behaviors and preferences, AI enables truly personalized content experiences that better resonate with specific audience segments, which then impacts engagement and conversion rates. Advanced systems can even adjust content elements in real-time based on contextual signals like time of day or recent interactions.

3. Consistency and brand voice

AI content tools can learn from brand guidelines and high-performing content to maintain consistency across channels and content creators. This ensures that content adheres to quality standards and brand voice regardless of who created it. AI quality control can flag potential issues before publication, reducing the risk of content errors that might damage your brand reputation.

Challenges of using AI in content marketing

Many content marketing leaders might agree: it’s both okay (and advisable) to take what AI gives you and make it your own. AI content creation is not meant to replace human creativity. When AI and human expertise work together, you get the best of both worlds: speed and scale without sacrificing the human touch. That balance helps you avoid the challenges outlined below.

Resistance from content teams

AI can feel threatening—especially to writers who fear being replaced or overwhelmed by new tools. Some may also struggle to see how AI fits into their existing workflows.

How to address it: Frame AI as an opportunity to upskill, not a replacement. Involve your content team early in the process to understand where AI can support their work—not override it. Co-create your approach and offer hands-on training to build confidence and fluency.

Quality control

AI must be well implemented and integrated to be effective. It requires careful training and thoughtful prompting for all kinds of copywriting. Human oversight is generally required at all stages to ensure the highest quality output, from ensuring alignment with brand voice, values, and messaging to fact-checking and scanning for potential biases.

How to address it: Keep humans in the loop. Assign editors or strategists to review and refine AI-generated content—ensuring quality, consistency, and alignment with brand standards. Upskilling content marketers in AI prompting and editing can turn this into a long-term advantage.

Ethics and privacy

This is a major challenge that every organization faces. Content marketers must be transparent about how customer data is used and ensure AI tools comply with their company’s data governance, legal requirements, and ethical guidelines. Whether you use AI to optimize and streamline internal processes or to create public-facing content, it should be in service of building customer trust.

How to address it: Choose AI platforms with enterprise-grade security, clear data usage policies, and ethical guardrails. Make transparency part of your content process—especially when using AI to generate or personalize public-facing content.

How to integrate AI and content marketing

Think strategically about what makes sense for your specific team when you implement AI into your broader content marketing strategy. Here’s how you can go about it:

1. Audit your processes and workflows

Review existing content processes and map current workflows, from ideation through distribution and measurement, to identify bottlenecks and the most time-consuming tasks. These may be opportunities where AI can add the most value. Consider which tasks might benefit from automation, AI-assisted project management, or whether an AI Assistant can serve as a resource for your team. Aim for high-impact, straightforward use cases that will help build momentum and demonstrate value to stakeholders.

2. Select the right tools for your goals

Evaluate your tools’ existing capabilities and any potential new software based on your team’s requirements. Consider starting with applications that address specific pain points rather than trying to implement comprehensive solutions. Be sure to choose tools that are transparent about how they function and that allow for human oversight.

3. Develop and document your AI strategy and governance

Create clear guidelines for how AI fits into your content operations, including approval processes, quality control measures, and ethical boundaries. Document which tasks will be AI-assisted versus human-driven, and establish protocols for reviewing and improving AI-generated outputs. This is generally a cross-functional effort and requires team training.

4. Implement, measure, and iterate

It’s always a good idea to begin with a pilot project. Before you roll out AI, set clear success metrics tied to broader marketing and business objectives, and gather feedback from your team and stakeholders to build a cycle of continuous improvement.

Examples of AI in content marketing

There are many ways to creatively apply AI in service of your marketing content. Some of these examples represent the most aspirational end of the spectrum while others can be implemented right away.

Campaign, calendar, and content creation

Code and Theory, a creative technology agency, helps its clients leverage AI for everything from summarization and document analysis to drafting full-scale campaign plans and content to creating reporting dashboards. The agency encourages a “campaign down” approach to quickly build out calendaring and resourcing and early drafts of channel assets. The end goal is to give teams a quick starting point that they can build on.

Personalized content recommendations

Netflix uses AI to analyze viewing patterns and deliver personalized content recommendations within the streaming platform itself, and also for marketing, by creating personalized trailers, emails, or even thumbnail images based on individual preferences and behaviors. Similarly, the meditation app Calm uses AI and machine learning to tailor content and suggest popular new Sleep Stories that a user hasn’t listened to before.

Innovative advertising campaigns

While advertising doesn’t typically fall to content marketing teams, content teams may sit under wider brand teams or support brand campaigns. Always a frontrunner, Nike used AI to create a tennis match featuring Serena Williams’ present and past selves for their “Never done evolving” campaign. The creative genius is still human, but the end outcome is reliant on AI. Coca-Cola also took an innovative approach with its “Create Real Magic” campaign, which invited users to experiment with AI (in a custom sandbox using ChatGPT and DALL-E) to co-create new brand assets using historic branded imagery.

Proprietary localized content

The Washington Post uses AI to create hundreds of short, straightforward and localized news stories and social posts about high school sports, election results, and financial reports, allowing journalists to focus on more complex, nuanced, and long-form investigative stories. Their AI use also includes a measure of comment moderation on the AI-generated stories.

Content optimization

Luxury fashion marketplace brand Farfetch used AI to optimize email campaigns by testing different writing styles and subject lines, leading to increased open and click rates.

Future of AI for content marketing 

If I’ve done my job well, it should be difficult to tell which parts of this article were influenced or written by AI. For now, the output is only as good as what we put into it. We’re each responsible for striking the right balance and ensuring that content maintains a human touch, is consistent across channels, and offers a unique, valuable, and on-brand perspective.

Going forward, teams across digital marketing orgs—from social media to email marketing to content—will find that it’s possible to use AI to create high-quality content across all formats and to make strategic shifts and find efficiencies within their teams. AI presents an opportunity to get creative about how you put it to service, and many marketers are on board—according to SurveyMonkey, 69% of marketers feel excited about AI technology and its impact on their jobs. So if you’re not already working with AI, the best way to catch up and keep pace is to begin experimenting now and learn from the results.

AI content marketing with Airtable 

Airtable AI allows content marketers to implement and manage AI-enhanced workflows and centralize content operations—from brainstorming to production to distribution and analysis. Even better, content teams who struggle with image and design resources can also bring assets to market faster with OpenAI image generation in Airtable.