How to Use AI for Content Marketing: 5 Key Business Benefits

With 48% of marketing leaders citing AI as making the most significant difference in how customers interact with their brands, it’s no wonder that more content marketing teams are exploring how to use it to increase user engagement, support content generation, and automate workflows. From ideation and content creation to publishing dynamically in multi-channel approaches and supporting measurement and analysis, AI can transform your content marketing team performance..

However, with so many different tools available and the technology evolving at a rapid pace, it can be hard to know where to start. In this article, identify exactly how AI can help your team, discover the benefits of using AI for content marketing, and learn how to overcome AI challenges faced by marketers.

AI supports marketing teams with every part of the content marketing lifecycle.

Content writers and content teams typically begin their content creation workflow with research that seeks to uncover the topics that will be interesting and relevant to their audiences. While traditionally the research process might have involved reading white papers, books, articles, and other blog posts, AI can now support a marketing team’s content research efforts with the automation of many research-related processes.

Studies show that bloggers who use AI content creation tools spend an average of 30% less time writing a blog post. In many cases this is thanks to AI tools taking over a significant portion of the information gathering, background research, and analysis required to write an informed article. Powerful generative AI tools, such as GenAI Learning Content, empower content marketers to minimize time spent searching for and wading through different sources. Instead, they can present the exact information and data that a content marketer needs with a single prompt instruction.

AI tools can also now summarize articles (TLDRthis), find research gaps (Powerdrill), fact-check and scrutinize (MirrorThink), interpret and visualize data sets (Julius), and capture citations.

One example of how AI supports content research is when natural language processing (NLP) algorithms analyze inputted data to provide subjects or areas that are likely to be relevant to a particular industry or audience. This AI technology works by scanning and analyzing news sources, conversations, social media platforms, and blogs, before swiftly identifying trends, hot topics, audience interests, and relevant keywords.

Once a team has decided on an idea and performed some initial research into the topic to ascertain that it will be a hit with readers, an article outline is the next logical step in the workflow. This involves creating a structure for the article, consisting of headers and subheads to give the writer a guideline to follow.

Content marketers are increasingly using AI to ensure that all the key points of the topic are addressed, a target audience’s interest is piqued, and all SEO keywords are strategically included. Studies show that after content ideation (71%), most marketing teams use AI for content development (68%) and drafting content (47%), with article outline generation an essential part of this process. This is even truer for teams whose core strategy is centered around blog articles and social media posts.

There are a few different ways that AI helps to create article outlines; natural language processing (NLP) algorithms can review existing content to extract key ideas and structural elements, while machine learning techniques can swiftly analyze successful articles in the same niche to identify common (and successful) structural patterns. When provided with the right inputs and parameters, AI-powered tools can also generate article outlines that will meet specific objectives such as target audience and tone of voice.

The content marketing team at Belkins, the B2B lead generation agency uses generative AI to produce article structures, which the team then adjusts where necessary, speeding up the content production process, streamlining workflows, and enabling the team to more effectively meet its content targets.

Consistently generating new and engaging ideas that address and resolve the issues faced by a brand’s target users is a challenge for any content expert. When a content marketing team is struggling to come up with fresh, relevant ideas, AI technology comes to the fore. By analyzing industry trends, competitor content, search engine queries, and audience behavior and preferences, a wide range of AI-powered tools are now able to produce lists of compelling ideas that a team can use for their newsletters, blog posts, social media captions, videos, and much more.

In addition to producing ideas relevant to a theme or industry, machine learning algorithms can suggest topics and ideas that are more likely to be a success with audiences by analyzing previous posts, the posts of competitors, and user trends.

The content team at dream job recruitment company, Remotive, uses generative AI for LinkedIn poll topics and daily threads ideas thanks to its impressive brainstorming power.

AI is now so good at crafting captivating copy that it is virtually indistinguishable from human-written copy.,Among marketers who are already using generative AI, 76% have said they use it for basic content creation. From blog posts, social media copy, and newsletters, to ebooks, white papers, and magazine articles, AI tools are now supporting content teams with entire content pieces, titles, and translations.

With 58% of marketers citing increased performance as a top benefit of using generative AI for content creation, we are likely to see more and more teams utilize AI-powered tools.

Smartcat’s AI content generator is the ideal tool for those looking for personalized yet on-brand translations, with the tool producing copy in over 280 languages using the desired tone of voice and terminology of the customer. This smart technology can do this thanks to the tool’s ability to learn from the existing content you have stored on your Smartcat Drive, meaning that you can generate highly contextual, on-brand content for any content requirement.

Content marketing leaders know the value of creating content that speaks directly to their users’ needs, preferences, and goals. Through the implementation of finely-tuned AI prompts, emotional language, storytelling, and data-driven insights, AI technology supports content marketers in creating text that deeply resonates with users’ experiences and desires. The benefits of humanizing and personalizing content in this way are numerous.

Personalizing content:

• Builds trust between users and a brand

• Increases engagement

• Helps users feel seen and understood

• Enhances a brand’s identity and authenticity

• Boosts the quality and relevance of content

• Improves the overall user experience

AI-powered personalization solutions analyze user data such as purchase history, browsing history, and social media use to generate valuable insights that inform content creation decisions, enabling marketers to deliver more tailored and personalized experiences to their customers at different stages in their customer journeys.

Personalization tools can be divided into three groups: Traditional AI (product recommendations, tailored search results), Conversation AI (NLP and machine learning to understand user queries and provide accurate responses), and Generative AI (new content creation based on human prompts).

Stitch Fix, the online personal styling service, is a great example of an organization using generative AI to personalize its service. Using client feedback regarding style preferences and fit, the company uses generative AI to suggest a range of clothing items that will match the user’s needs. Combined with the input of a human stylist, the client is then provided with a curated, personalized list of pieces that truly match their desired look and style.

Every content marketing team relies heavily on eye-catching images to capture the interest and imagination of users and provide context and relevance to text-based content, whether that be to draw in users on social media platforms, provide visual page breaks in blog posts, or demonstrate complex concepts or processes in white papers.

Thanks to AI, image creation and enhancement can be supported by AI tools such as MidJourney, DALL-E 3 , DreamStudio , and Adobe Firefly , which can generate realistic images based on inputted descriptions, using algorithms such as generative adversarial networks (GANs) to create appealing illustrations, graphics, or photograph-like images.

Organizations using AI image generators fall slightly behind those using the technology for text-based functions. 19% of adults in the US report that their employers are using AI image generators compared to 28% of employers using generative AI or similar tools.h DreamStudio alone has generated one billion images created by users as of July 2023, and the AI image generation market is set to be worth a staggering $917.4 million by 2030.

Coca-Cola has been a frontrunner in experimenting with AI image and video creation. Most recently, the conglomerate used AI to create a platform where customers can design Coca-Cola advertisements solely using AI, called Create Real Magic. The results are an example of the stunning results that can be achieved through AI technology and human collaboration.

Nestlé has also used image generators in its advertising, creating innovative and attractive campaigns such as its “La Lechera Taste The A.I” campaign which fused AI with culinary artistry to showcase Nestlé’s commitment to both innovation and culinary excellence, while simultaneously highlighting the brand’s Colombian heritage.

One of the key areas of performance analysis in which AI can support marketing teams is the automation of data collection and processing. Valuable insights such as trending topics and audience behavioral patterns can be extracted from text and visual content by natural language processing algorithms while machine learning algorithms working with historical data can predict the performance of specific pieces of content with target audiences.

AI-powered analytics programs can also offer real-time reporting so marketers can see minute-by-minute the effectiveness of their strategies. Burberry and Starbucks are just two major brands using AI to analyze and enhance their performance, with Starbucks using customer data to display an individual’s favorite orders to their baristas before the customer has reached the counter.

57% of IT leaders cite workflow automation technology for saving 10-50% on the business costs associated with manual processing. 42% of business leaders agree that workflow automation accelerates the completion of repetitive tasks. Marketing teams are increasingly looking to experience these benefits for their own workflows and are looking to AI for workflow automation support.

Through the optimization of different content marketing tasks, such as content creation, distribution, and analysis, AI is automating many key elements of the content marketing workflow, leaving content marketers with more time to focus on strategy, innovation, and allocation management.

While NLP algorithms automate research, ideation, outlines, and even drafts, AI-powered management systems support the scheduling and publishing of content across a range of platforms at times that are shown to be both optimal and aligned with audience preferences. Zapier, Sprout Social, Drip , Pardot , and Brevo are all popular tools for streamlining and automating content marketing workflows.

Let’s take a look at the host of benefits that using AI can bring to your content marketing strategy, execution, and overall performance.

As we have seen, AI can relieve a content marketer of many routine tasks such as content research, drafting, ideation, and analysis, activities that consume their time, eat up valuable resources, and take them away from higher-level strategizing and planning. When these tasks are automated with AI, teams can instead focus on strategic initiatives, creative ventures, and engage closely with the target audience, assured in the knowledge that AI technology is taking care of the manual work.

In addition to ensuring content is error-free and produced in a consistent tone of voice, AI can provide data-backed insights into audience preferences and reading histories which ensures the team is only publishing or enhancing content that their audience will find relevant and which they will genuinely want to engage with. It can also ensure consistency in tone of voice and brand communication guidelines.

Through the analysis of keyword trends, AI can provide valuable insights to content marketing teams that can be used to inform and improve the SEO of their content, boosting search engine rankings.

Additional activities that AI can perform, which further contribute to higher search engine rankings, include:

• Content quality and relevance

• User experience metrics

• Identification of link-building opportunities

Thanks to the streamlining of processes and automation of routine tasks, AI tools support the scalability of content distribution and creation as teams are freed up to handle larger volumes of content efficiently. From content generation and schedule distribution across multiple platforms, to analyzing performance metrics at scale and reducing manual workload, teams can focus their efforts on strategizing how to scale their marketing campaigns and leave the manual labor to processes automated by AI.

54% of organizations have reported witnessing cost savings and efficiencies from using AI in their business processes, with those in the marketing industry citing a 6.2% increase in sales and 7.2% lower marketing costs. This is in part down to the automation of tasks that would otherwise require significant resources, therefore reducing the need for manual labor, streamlined processes, and improved efficiency. Wasted ad spend on campaigns is also reduced due to AI algorithms optimizing strategies and enhancing existing content.

Here are our top tips for maximized generative AI performance.

Your technology must be aligned with your company and team’s goals and strategies. This helps you to deliver measurable results as well as ensure you achieve meaningful business outcomes.

To address the specific challenges faced by your content marketing team, you’ll need to ensure you choose tools that align with your needs and goals. Whether you’re streamlining your workflow, or improving the relevancy of your content, you can maximize the impact of your AI tools within marketing strategies when selecting tools tailored to your objectives.

To leverage the most strength out of the content marketing team and AI tools, human-AI collaboration needs to be actively nurtured and encouraged. Human creativity and intuition are crucial to crafting content that is truly compelling to readers and that empathizes with their challenges and needs, in addition to understanding the nuances and subtleties of audience responses and preferences.

There are still some hurdles to overcome when we use AI to support our marketing processes. We’ve outlined the most common ones here and how you can resolve them.

A common concern of marketing teams is that when AI is used to generate or enhance content, the quality of the output might suffer. To overcome this challenge, a content marketing team should regularly review and, where necessary, tweak its algorithms to ensure full alignment with the brand’s existing voice and values. It’s a good rule of thumb to promote human-AI workflows and integrate human proofing into any AI-based process to ensure any inaccuracies are quickly spotted and addressed.

With AI gathering and analyzing significant quantities of user data to help inform and improve content, teams must be fully transparent with audiences about the use of AI in content creation and how their data is being collected and used.

Teams should consider implementing robust data protection measures and anonymization practices and ask for explicit user consent before collecting data for content personalization purposes, as well as partnering with generative AI providers that offer rigorous security standards.

Algorithm bias, when systematic errors occur in AI algorithms due to the adoption of human prejudices or stereotypes during training, is of great concern to both users and organizations as AI becomes more routinely adopted. To overcome this, content teams need to diversify their AI training materials and data sources, utilizing a wide range of demographics and perspectives.