Chaos in content management might be frustrating for many companies, but for pharma and life sciences specifically, it can become a critical liability. Timing and accuracy are everything for these industries, and when a product manager can’t quickly locate up-to-date promotional materials for a key stakeholder meeting, it will likely lead to missing a big opportunity. This scenario isn’t a make-believe, it’s a common reality in pharmaceutical marketing departments. And it’s hardly surprising as the sheer volume of content that life sciences companies produce can quickly become overwhelming.
AI auto-tagging can seriously simplify and organize content management, which is why it is gaining significant interest across various industries, including pharmaceuticals. We have the proof of auto-tagging’s efficiency right here, in this article. Let us explore how AI auto-tagging is addressing the content management challenges plaguing the pharmaceutical industry.
What is AI Auto-Tagging?
AI auto-tagging is an advanced technology that uses artificial intelligence and machine learning algorithms to automatically assign descriptive labels or “tags” to various types of digital content.
The main goal of AI auto-tagging is to organize and categorize large amounts of digital content quickly and accurately, making it easier to search, manage, and utilize. It can be applied to various digital assets, including images, videos, text content, audio files, and web pages.
How Does It Work?
In pharmaceutical marketing, an AI-powered auto-tagging feature works like this:
- A new asthma medication brochure is created. The trained AI system analyzes it and automatically assigns relevant tags such as #AsthmaManagement, #BronchodilatorRx, #FDA_Approved, and #PatientEducation. It also tags content type (#PatientBrochure) and any clinical data (#EfficacyStudy).
- This process ensures consistent tagging, makes the brochure easily searchable in the digital asset management system, and aids in quickly identifying materials for different purposes (e.g., patient education vs. physician information). It also helps with regulatory compliance by flagging sensitive content.
- The marketing team can then efficiently retrieve and use this brochure for campaigns, updates, or healthcare provider requests, simplifying their workflow and content management.
Why is Interest in Auto-Tagging On the Rise?
Interest in auto-tagging is surging as organizations grapple with the exponential growth of digital content and the pressing need for more efficient content management. The proliferation of unstructured data, dispersed across various platforms and existing in multiple formats, has become a significant barrier to optimal performance. Companies increasingly recognize that traditional manual tagging methods are no longer sufficient to handle the sheer volume and complexity of digital assets being produced daily.
Out-and-out efficiency
AI-powered auto-tagging cuts most of the manual tagging efforts, allowing organizations to process vast quantities of media assets efficiently. By leveraging this automated approach, companies can rapidly categorize and organize large batches of content, freeing up valuable human resources. This streamlined workflow allows teams to shift their focus from tedious manual tagging to more impactful, big-picture initiatives. With auto-tagging, you’re getting reliable results across the board, which makes finding, using, and reusing your content so much easier down the line.
Consistency up, mistakes down
Another huge benefit is that auto-tagging algorithms significantly boost the accuracy and consistency of metadata tagging, noticing things that our eyes might miss. These systems minimize human error and ensure uniform categorization across all assets, which is critical for effective digital asset management.
Actionable insights
On top of all this, auto-tagging goes beyond simple organization — it provides valuable insights into digital asset usage patterns. By analyzing the tags and metadata automatically assigned to content, organizations can gain a clear picture of how their assets are being utilized across different channels and departments. This data-driven approach enables companies to make informed decisions about content management strategies.
For instance, auto-tagging can reveal which types of assets are most frequently accessed, which tags are commonly searched for, and how content usage varies over time. These insights can guide content creation efforts, inform resource allocation, and highlight opportunities for repurposing existing assets. Moreover, it can help identify underutilized content that might benefit from increased promotion or retirement.
Harnessing the Power of AI Cognitive Engines for Auto-Tagging
So how AI can make sense of huge media files? It’s pretty amazing — let us tell you about the smart tech behind auto-tagging that’s changing the game.
Computer vision engines
Computer vision engines are essential for AI auto-tagging, automatically analyzing visual content to assign relevant tags.
These engines use deep learning algorithms to process images and videos, performing various functions. They detect specific objects, categorize scenes, and extract meaningful features from pixel data. Through object detection, they can identify multiple items in a single image, while face detection capabilities support demographic analysis and privacy compliance.
Advanced systems incorporate brand recognition for marketing applications and optical character recognition (OCR) to extract text from images. Many engines also support custom model training, allowing organizations to develop specific tagging capabilities tailored to their needs. This combination of features enables comprehensive and accurate automated tagging of visual content.
Audio pattern recognition
Audio pattern recognition, or audio fingerprinting, AI engines identify and tag audio content using unique acoustic signatures. They analyze audio files to create digital fingerprints for efficient recognition and retrieval.
The process involves extracting distinctive features from audio signals, generating compact digital fingerprints, and matching these against a database. Many engines can process audio in near real-time, making them suitable for live broadcasts or streaming services.
These engines recognize audio even in noisy environments or when altered, and can identify various content types like musical tracks, spoken words, and environmental sounds. Some allow custom libraries for specific content, enhancing accuracy. The output typically includes searchable metadata tags for efficient audio file categorization.
How to Utilize AI for Auto-Tagging
The auto-tagging process begins with data collection, where large datasets of content requiring tagging are gathered. Once collected, the data undergoes preprocessing to ensure its suitability for analysis. This step involves cleaning the data, removing duplicates, correcting errors, and standardizing formats.
Following preprocessing, feature extraction takes place, where artificial intelligence algorithms analyze the content to identify relevant characteristics. For images, this might include recognizing objects, colors, or patterns, while for text, it could involve identifying keywords, phrases, or sentiments. A machine learning model is then trained using labeled data to learn relationships between content features and tags.
After training, the model can generate tags for new, unlabeled content based on the patterns it has learned. This process involves predicting the most relevant tags for each piece of content. Advanced models may incorporate natural language processing to better understand the context of the content, leading to more accurate tagging.
Many AI-based auto-tagging systems operate in real-time, allowing for immediate tagging of content as it is created or uploaded. This ensures that tagging remains current without the need for manual intervention.
The auto-tagging engine is typically integrated with content management or digital asset management systems, enabling seamless tagging and retrieval of content.
Auto-Tagging vs. Manual Tagging
The choice between auto-tagging and manual tagging depends on the specific needs and resources of your marketing team, so let’s do one versus the other.
Efficiency
- Auto-tagging is highly efficient as it reduces labor.
- Manual labeling is time-consuming and requires significant effort to manage.
Error rate
- Auto-tagging rarely makes errors and is capable of noticing human mistakes quickly. However, keep in mind that they are not completely faultless, and can sometimes add irrelevant tags, especially when analyzing complex content.
- Manual tagging is prone to human error and, hence, can lead to data inconsistency.
Ease of management
- Once auto-tagging is set up, it doesn’t require much oversight.
- Tagging manually needs more complex management, especially if you have a lot of custom tags.
Future-proofing
- When you have your AI auto-tagging system enabled, your future content is categorized automatically with the necessary metadata tags. So you can be sure no file has fallen through the cracks and you will be able to quickly find it later. The system can also suggest tags based on the provided text or media files, empowering you to label the content with helpful tags.
- Manual tagging is not as scalable, as the older your company gets, the more marketing content you’ll have. This approach might work for smaller companies, but for medium and large businesses doing it manually can drain significant resources.
AI Tagging Use Cases
AI tagging is revolutionizing various fields with automation capabilities. This technology enhances the features of searching, workflow streamlining, and user experiences in multiple domains. Some of the major uses of AI tagging are summarized below:
- Content publishing
In the realm of content publishing, AI tagging is a game-changer for managing large volumes of digital assets like articles, images, and videos. Media outlets leverage AI systems to automatically add tags to their content, making it easier to organize and retrieve information. This enhances discoverability and accelerates editorial processes, enabling content creators and publishers to manage their assets more efficiently.
- e-Commerce
e-Commerce platforms rely heavily on AI tagging to classify products and personalize shopping experiences. By analyzing customer behavior and preferences, AI can automatically tag products, offering personalized recommendations that enhance the shopping experience. This not only improves user satisfaction but also drives sales by providing relevant product suggestions.
- SEO and web optimization
AI-generated tags are also important in helping improve the search engine optimization of websites. Correct content tagging enables web pages to top the list in search results. Such rankings of the pages in search engines increase organic traffic coming to the site and boost visibility and user engagement, building a successful online presence.
- Metadata management
AI tagging is essential for managing metadata in industries like healthcare, pharma, and legal services. In healthcare and pharma, it automates the classification of patient records, research data, marketing materials, and clinical trial documents, ensuring that critical information is easily searchable and compliant with regulations. This improves efficiency and decision-making by enabling quick access to relevant data. In legal settings, AI tags legal documents and case files, streamlining data retrieval and reducing the time spent on manual categorization.
AI Tagging: Trends & Developments
AI tagging continues to see phenomenal trends and developments that are making a sea change in the way content organization and management is happening across industries. In our opinion, these are the four key trends that will become even more widespread in the future:
Cross-language and cross-cultural tagging
The development of the new AI systems will enable tagging in different languages and cultural contexts, making the content more accessible and relevant in today’s globalized environment.
These advancements enable organizations to connect with diverse audiences more effectively, ensuring that the content is appropriately tagged and relevant, even across the boundaries of language or culture.
Multimodal AI
AI tagging is evolving to incorporate multiple types of data, including text, images, and audio. This integration allows for a more comprehensive understanding of media assets and enhances the contextual relevance of tags.
Solutions that can analyze a video’s audio track while simultaneously assessing its visual content can generate more accurate tags that reflect the overall context.
Personalization
AI systems are becoming increasingly customizable, allowing users to tailor tagging models to their specific preferences and requirements. This leads to more accurate tagging results that align closely with user needs, improving overall satisfaction.
Sentiment tagging
Future AI developments may include sentiment analysis, allowing systems to tag content based not just on keywords but also on the emotions conveyed within the text or images. This could be particularly beneficial for brands looking to gauge public sentiment about their products or services through social media analysis.
Implement AI tagging with eWizard
An effective content management system is critical to a company’s ability to adapt to market changes and maintain a competitive edge. eWizard has demonstrated its capability to bring visible, measurable results to organizations facing challenges in managing their digital assets.
For instance, when a leading pharmaceutical company partnered with Viseven to address inefficiencies in their manual tagging process, the implementation of AI-powered auto-tagging transformed their workflow. The results were significant: manual tagging was reduced by 60%, freeing up thousands of hours for strategic tasks. Additionally, time-to-market for brand campaigns accelerated, and content reuse improved due to better searchability and tagging accuracy.
With a return on investment of over 340%, the solution proved not only effective but highly cost-efficient. By enriching content assets with strategic and contextual tags, the AI-driven solution became a cornerstone of the company’s digital transformation, driving efficiency, collaboration, and stronger business outcomes.
Through advanced tools like eWizard, companies can streamline content management, improve searchability, and achieve real-world results, making it an indispensable asset for any pharma business looking to enhance their digital content strategy. If you want to find out how eWizard can match your organization’s needs, don’t hesitate to drop us a line and one of our experts will reach out.
