This article summarizes the presentation by Daniel Hocutt, web manager and professor at the University of Richmond, VA, at the IUNTC meeting on February 6, 2025.
How AI changes digital advertising
One of the most important developments in digital advertising is the deep integration of AI into advertising platforms such as Google Ads and Meta Ads (formerly Facebook). Creating ads used to be a manual process that involved writing copy, selecting images, and defining keywords. Today, AI-supported systems perform many of these tasks automatically. The advantages of these automated processes are efficiency and the ability to tailor content to individual users. Nevertheless, challenges remain: The human communicator must check and, if necessary, adapt the generated content to ensure that it meets quality requirements and target group needs, ensures data security, and takes ethical aspects into account.
The role of AI as co-author and partner
Collaboration between humans and AI takes place in the form of so-called "human-autonomous teams" consisting of human editors and autonomous systems that work together on communication goals. The collaboration can be described as "adversarial cooperation". The AI engine generates content based on machine learning and data analysis, while the human editor critically evaluates and edits these suggestions. This process requires a high level of editorial expertise as well as a basic understanding of how these technologies work. This raises several questions: To what extent can and should AI-generated content be trusted? What control mechanisms are required to ensure that the content complies with ethical and professional standards? These questions are particularly relevant in the context of marketing, where the presentation of one's own brand and the handling of user data play a central role.
Target group analysis and structured content
Target group analysis is a key aspect of AI-supported content generation. In both marketing and technical communication, it is crucial to know the target group precisely and understand their needs. Modern platforms use large amounts of data to analyze profiles and interests and to personalize content based on this. The process works as follows:
- Data collection and analysis: Platforms continuously collect data about user behavior. This includes information such as browser history and search queries, interactions with content (e.g., likes, comments, and clicks), and information from user profiles (e.g., interests and geographical locations).
- Machine learning for profiling: Based on this data, the platform uses machine learning to create a comprehensive user profile. The profile contains information about which topics and products are relevant to the user. This is a combination of explicitly stated interests (e.g., fields of interest in a social media profile) and those that are implicitly inferred through behavior.
- Personalization of content: Ads and other content are automatically adapted based on these profiles. For example, a platform such as Google Ads analyzes the content of a landing page and creates keywords, headlines, and descriptions for advertisements based on this. At the same time, it determines which user group is most likely to be interested in this offer.
- Automated decision making: The platforms perform a dynamic combination of ad elements that are individually compiled for each user. This content then appears in various formats (e.g., text ads, video reels, or banner ads), depending on the user's preferences and behavior.
- Optimization through continuous feedback: The platforms evaluate user interactions with personalized content to further improve their algorithms. For example, click rates and dwell time can be used as indicators of the relevance of the content.
Platforms such as Meta offer AI-powered targeting, while Google generates AI-powered suggestions for ad elements. However, these technologies can provide inaccurate or inappropriate suggestions, so critical editing remains necessary.
The importance of structure
In addition to target group analysis, structuring content is an essential part of both disciplines, technical communication and digital marketing. Well-structured content not only improves the user experience but is also crucial for search engine discoverability. The methods and techniques used are similar, such as a clear hierarchy of headings and subheadings, the logical arrangement of text and visual elements, and the division of information into easy-to-understand sections.
In technical communication, such structuring is particularly important to make it easier for users to find their way around complex documentation. It enables readers to search specifically for relevant information and grasp it quickly. The situation is similar in marketing, where content must be designed in such a way that users can grasp the core messages of an advertisement or website at first glance.
Findability by search engines (SEO)
Structured content also plays a crucial role in search engine optimization (SEO). Platforms such as Google use algorithms that favor structured content because it ensures better indexing and presentation in search results. Search engines analyze the structure of the website to find out which content is particularly important (e.g., headings and meta descriptions). A clear and consistent structure enables search engines to better classify the relevance and subject areas of a website and increases the likelihood that users will find and select the website in the search results. This plays a particularly important role in digital marketing, as placement in search results often determines the success or failure of an advertising campaign. A landing page that is optimized for search engines attracts more visitors and achieves better conversion rates.
Both technical communication and marketing campaigns benefit from a well-thought-out strategy that is geared toward the needs of the target group. However, there is a key difference in the objective: While technical communication aims to provide users with information and instructions, marketing focuses on persuasion and promoting calls to action (e.g., product purchase, contact).
Technological developments and new requirements
The integration of AI and machine learning into technical communication requires a rethink in practice and training. Three core aspects are of particular importance here:
1. Editorial control
When working with AI, technical communicators are increasingly taking on the role of editors and decision-makers. This means that they no longer just create content, but are also largely responsible for deciding which AI-generated content is adopted, changed, or discarded. Technical communicators must therefore be a kind of "counterpart" to the AI by evaluating and correcting the content and thus ensuring its quality and relevance for the target group. This role as a critical editor is crucial for compliance with technical standards.
2. Changed work processes
Platforms such as Adobe and MadCap Flare will increasingly integrate AI functions. These developments are fundamentally changing work processes and tasks such as text creation, automated formatting, and content management are becoming much more efficient. The role of technical editors will be increasingly shaped by technology. They need to familiarize themselves with the new platforms, understand their functionalities, and learn how to integrate them into their daily work. In addition, they need to develop the ability to use these tools effectively and critically evaluate their suggestions.
3. Ethical issues
Working with large amounts of data raises questions about data security and the protection of user rights. Technical editors and marketing experts also have a responsibility to protect user data when using these platforms. They must ensure that the content generated based on this data does not violate users' privacy or lead to discriminatory practices. For example, there is a risk that certain user groups are not taken into account in personalization due to missing or distorted data profiles. This could mean that important content is not visible to these groups or that their needs are not sufficiently taken into account in the content strategy.
To meet these challenges, clear ethical standards and guidelines are required, as well as a critical and reflective approach to AI systems in order to consider the long-term impact on user experience and social responsibility.
The increasing integration of AI into content management systems (CMS) and other digital platforms requires new skills from specialists. Key skills that should be taught in training include:
- Critical thinking and editorial competence: the ability to evaluate, adapt, and optimize AI-generated content.
- Technical understanding: a basic knowledge of how AI and machine learning work to be able to assess their potential and limitations.
- Data literacy and ethical awareness: knowledge about handling large amounts of data and compliance with ethical standards.
Challenges and opportunities
AI integration has far-reaching implications for the practice of technical communication and digital marketing. While automated processes bring efficiency gains, human expertise remains essential to maintain content and ethical integrity. Professionals must learn to work productively with AI systems and use them as tools to support their work. It is important to drive forward the development of standards and guidelines to ensure the sustainable and ethical use of these technologies.