eWizard Auto-Tagging for Content Operating Excellence Across Brands and Markets

None of our habits developed overnight, and almost none of them stay unaltered throughout our lives. This same postulate pertains to content experience. The way we used to consume content just a decade ago is no longer the same. Hardly anyone can now imagine not having a smartphone at their fingertips, checking news, answering messages, and consulting with AI chatbots on all sorts of topics.

Today’s always-on, multi-channel environment demands more than just creating content — it requires making that content instantly discoverable, in particular by AI chatbots, relevant, and ready to use across platforms. This is where auto-tagging comes in.

What is Auto-Tagging? 

As many pharma companies undergo digital transformation, they are increasingly turning to automation to streamline content-related processes, including content tagging.

Before we explain auto-tagging, let’s answer the “What is content tagging?” question.

Content tagging is the process of organizing any type of content, such as images, videos, or texts, in a certain way that allows sorting it into groups and categorizing it based on certain characteristics.

Basically, content tagging is the same thing as labeling items in order to describe them. Let’s take a look at some content tagging examples: 

• Assigning tags like “Education”, “Technology”, or “News” to different blog articles to help readers quickly find relevant content; 
• Labeling videos with descriptions like “Tutorial”, “Demo”, or “Interview” for quick identification of specific types of video content; 
• Tagging content based on details, with tags that stand for products, services, partner names, target audience, etc. 

Content tags are what you can use to quickly organize all content items and mark the content to make it easier to search. Content tags can be easily customized to fit your business objectives and specifics, providing customers with a more personalized experience and improving the navigation through your website. 

Metadata tagging is similar to content tagging, but while the latter focuses on tags visible to users, metadata tagging mostly involves adding descriptive metadata to the aforementioned content.

By combining both metadata tagging and content tagging, you can enhance the discoverability of your content and make it easier to manage. 

Another term to keep in mind is content tagging taxonomy, which is used to organize and classify content.

Without taxonomy, it would be much harder to deliver personalized content and optimize it for search. Content tagging taxonomy can ensure that your content is more accessible and easier to find. 

Auto-tagging is the same as content tagging, but automated, meaning that there is no need to spend time manually creating and assigning tags to your content.

By using various auto-tagging tools and solutions like AWS auto-tagging, you can save a lot of time that can be dedicated to other activities and projects.

How Does eWizard Auto-Tagging Work?

eWizard’s auto-tagging solution is an optional function to structure enterprise-level modular content and metadata with auto-tagging, auto-recognition, and taxonomy support. 

eWizard auto-tagging aims at two main goals: 

• to process brand digital content (audit, auto-tag, visualize, and analyze); 
• to help optimize the workflows, such as brand content planning, MLR approval, content development, content distribution, and content performance analytics. 

eWizard AI-based auto-tagging can automatically extract and tag data from digital content, providing contextual navigation (smart search) and helping to implement a personalized customer experience. Simply put, the algorithm is trained to apply relevant and descriptive tags by “understanding” the content behind the asset. 

Auto-tagging solves the main challenge for users of finding the relevant content in a few clicks, removing the need for manual search. eWizard tagging solution automatically scans different content types and extracts key metadata, as well as assigns tags to the new content types that pair with similar topics. 

To summarize, auto-tagging helps optimize: 

• content discovery 
• content reusability 
• metadata management 
• content creation workflow 

Why AI-Powered Tagging Is Becoming the New Standard

AI content tagging quickly becomes widespread, as it uses artificial intelligence to analyze and tag content of any type. AI content tagging commonly involves several related technologies, such as Machine Learning and Natural Language Processing.

By delegating content tagging to AI, teams can manage, search, retrieve, and share content across all sorts of platforms faster, without compromising quality. AI can enhance content tagging strategy in numerous ways, such as: 

• Increased efficiency. AI-driven content tagging is capable of handling large data volumes much faster compared to humans, making it a few times more effective and time-efficient. 
• Real-time analysis of tags. Artificial intelligence is capable of analyzing and tagging content in real-time, meaning that it will also stay up to date without the need to do it manually. 
• Reduced errors. “To err is human”, everyone has heard that. Unfortunately, when it comes to something as scrupulous as content tagging, it becomes extremely easy to make a mistake. Moreover, many details are hard to catch for the human eye. AI, however, can capture virtually anything. 
• Availability in different languages. AI can create and process tags in various languages, which can help teams across the globe. AI auto-tagging also helps to keep tags in various languages more organized without the need to onboard multiple specialists who master respective languages. 
• A better understanding of your content. Automated content tagging with the help of AI not only labels content and sorts it out. It can also compare different documents, categories, or formats and find similarities humans might have missed. Artificial intelligence can offer fresh insights into your content and audience. 

Remember that artificial intelligence is not an ultimate solution that will completely get rid of manual tasks. AI auto-tagging is a step toward automation, and it’s best to take this step as early as possible. 

eWizard Content Formats that Can Be Auto-tagged

Standard content auto-tagging is a highly scalable solution that applies tags to imagery, video, and text-based digital assets. You can audit your brand’s digital content, identify objects, people, text, scenes, and activities. Besides, it can detect any legal risk data and supplement the content with additional information (metadata). 

Another type is custom auto-tagging. It allows distinguishing the objects and scenes in images specific to your business needs. Custom solutions offer a more personalized approach tailored to business-specific taxonomies (pharma and life sciences, in our case). 

How eWizard Enables Content Tagging

eWizard’s centralized tagging solution, powered by eVa AI, assists pharma and life sciences companies in tagging content and making this process as simple as it can be without compromising quality. With eWizard, teams were able to achieve a tagging accuracy of 92.3%, increase reuse to up to 80% in just 6 months, tag 78% of assets available, and decrease review cycles by 30%. Let’s review the main capabilities of eWizard’s automated tagging solution.

AI-powered tagging engine

For many companies, tagging — even when automated — does not always account for the full range of data they manage. This can include content tailored to specific audiences, campaign materials adapted for different markets, and other highly specific, context-dependent assets.

eWizard addresses this gap by going beyond the standard capabilities of traditional tagging solutions. It enables companies to add tags and manage metadata across all content assets — regardless of whether they are created within eWizard or in external systems. This allows you to structure and organize content more effectively around key messages, modular components, and overall strategic objectives. Other eWizard’s auto-tagging capabilities include:

• Multilingual content analysis
• Custom taxonomy support for micro-segmentation
• Historical content back tagging with bulk processing capabilities
• GDPR-compliant, with encryption, audit trails, and user permissions

Integration with DAM, Veeva, and CRM

One of the most important features of eWizard is its integration with multiple other systems, allowing companies to connect their content with strategy without losing relevant tags and content pieces in the process. eWizard turns tagging from a search function into a strategic engine that connects content, compliance, and customer engagement across digital asset management and CRM systems. eWizard ensures:

• Consistency of tags across assets
• Better reuse of approved content
• Faster MLR review and retrieval

The tagging process goes from being generic to tailored to the specific needs and goals of a company, taking into account all of the specifics of not just the industry but also the area the client specializes in.

End-to-end tagging workflow

From capturing data to tag synchronization, eWizard auto-tagging solution covers all stages of the tagging workflow, including:

• Content ingestion. Content enters the system, either uploaded manually or detected from existing sources.
• AI-powered tagging. Advanced AI analyzes the content and generates relevant tags based on context and not just keywords.
• Human validation (optional). Subject matter experts can review, refine, or approve tags to ensure accuracy, compliance, and business relevance.
• Tag synchronization. Approved tags are automatically synced across connected systems, ensuring consistency everywhere the content lives.
• Monitoring and optimization. Dashboard provides insights into tagging performance, usage, and coverage.

In eWizard, behind every asset, there is a battery of automated tagging processes that ensure accuracy, easier search, and readiness for use.

Auto-tagging vs. Manual Tagging

Auto-tagging and manual tagging share a lot of differences, and they go beyond just speed and cost. The comparison below highlights how each method performs across critical dimensions:

Dimension	Auto-tagging	Manual tagging
Definition	Tags are assigned automatically, with the help of AI, metadata extraction, or NLP	Tags are assigned by humans
Speed	Extremely fast, can process thousands of assets instantly	Slow, depends on human capacity
Scalability	Highly scalable	Limited scalability
Consistency	High consistency if the taxonomy is well-defined	Often inconsistent across teams and markets
Accuracy (basic metadata)	Very high for structured data	High, but depends on attention and knowledge of the person tagging
Accuracy (context and nuance)	Can struggle with complex context	Strong, as humans understand tone, nuance, and intent
Cost over time	High initial setup, low long-term cost	Low setup cost, high long-term operational cost
Governance and compliance	Requires strong compliance to avoid wrong automated labels	Easier to control but harder to enforce at scale
Adaptability	Can adapt quickly if models/rules are frequently updated	Slow; requires retraining people
Human effort required	Minimal after setup	Continuous effort required
Best use cases	Large content supply chains, omnichannel delivery, MLR-heavy environments, and modular content creation	Small libraries, highly specialized content, early-stage programs
Risk	Risk of wrong tags at scale if automation is poorly configured	Risk of inconsistency and missed tags
Integration with DAM / CMS / MLR / modular content	Strong — enables automation, reuse, and personalization	Weak — manual work slows reuse and automation
Knowledge capture	Knowledge stored in the system	Knowledge stored in people
Time to publish	Faster approval and reuse	Slower due to manual steps
Recommended approach	Best as a primary method with human validation	Best as support for automation, not replacement

Manual tagging ensures understanding, while auto-tagging ensures scalability. Future-ready companies must make the most out of both, letting the auto-tagging tools take care of the “heavy lifting” and process large volumes of data, while humans make sure that all of the taxonomy is covered.

Possible Limitations of Automated Tagging

Even though automation of the tagging process offers numerous benefits, without human oversight, it might cause quite a few issues. Here are some possible challenges that might arise:

Risk of inaccuracy and inconsistency

Auto-tagging is heavily dependent on the consistency that your models will be able to provide. If the final results are not consistent, it can lead to unreliable tagging across the entire content ecosystem, creating fragmentation in the metadata. This can happen because of differences in wording and formatting, model sensitivity to context and phrasing, and updates or changes in the model over time.

Lack of contextual understanding

Automated systems, even those driven by the most advanced AI, often struggle with tone, nuance, and intent. Sarcasm and irony might be misinterpreted, and many AIs can fail to understand the differences between similar concepts. For example, cold can be interpreted as either illness or temperature, which even the smartest models might mix up in some cases.

Overreliance on training data

Automated tagging tools and models are only as good as the data they were trained on. Even if you use high-quality data and put a lot of effort into training your AI, there is no guarantee that it will always have a correct tagging process. For example, biases in training data can result in biased training. Limited exposure to different topics can lead to poor performance in niche areas. Because of these possibilities, companies risk causing misclassification of their content.

Lack of governance and standardization

Without strong oversight, you might not have complete control over the tagging process. Tags may not follow the same conventions, synonyms might not be consolidated, and hierarchies may not be respected. Without human input, you might end up with a fragmented ecosystem that’s hard to scale, in which some bottlenecks might remain undetected for months.

Key Use Cases for Automated Tagging in Pharma

Automated tagging makes it possible for teams in the pharma and life sciences sectors to manage vast volumes of content fast and efficiently.

These use cases highlight how auto-tagging supports key functions across the pharma content ecosystem:

• Content management and retrieval. Automated tagging enables fast, scalable organization of large volumes of content. Companies can divide tagging by disease area, molecule/brand, audience (HCP, patient, payer), and content type.
• Medical information and MSL support. Automated tagging process can help teams navigate a complex scientific context without having to spend hours manually going through all of the materials by tagging clinical endpoints, study types, off-label and on-label content, and other data, allowing MSLs to quickly retrieve relevant information in the field.
• Compliance and regulatory classification. Automated tagging enables companies to distinguish between scientific exchange and promotional content by classifying materials based on promotional vs non-promotional intent, risk level, on- vs off-label status, and intended audience — supporting consistent, compliant content use across markets.
• Omnichannel marketing and personalization. Automated tagging makes it possible to quickly determine what kind of content should go where, making publishing across multiple digital channels much easier and more flexible. Tagging can be divided by HCP specialty, stages in the customer journey, therapy interest, and engagement history.
• Content supply chain optimization. Content creation and management can become much more efficient with auto-tagging, since it can also help tag even the smallest content components, including modular content blocks like claims, references, and visual assets.

When combined with strong taxonomy and human validation, automated tagging becomes a powerful tool that makes content development and delivery a faster and more efficient process.

Get Started with eWizard Auto-Tagging

By relying on a robust technology stack, our company is helping Life Sciences and pharmaceutical companies worldwide accelerate content experience and deliver the best-in-class customer engagement techniques. We are pioneers of creating digital ecosystems that lead the way to agnostic content management, superior client experience, and data management.

Our team is ready to cover all phases of your omnichannel project workflow: starting by developing and localizing a global brand communication strategy, preparing the operational foundation, orchestrating customer engagements, and finishing with measuring the results and performance of digital assets.