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Updated on : 29 Jul 2025
The sheer volume of customer-focused and healthcare professional (HCP)-focused content within pharma organizations presents a significant challenge in effectively managing and utilizing this vast information. As pharma organizations continuously generate an enormous amount of content, ranging from scientific research and clinical trial data to marketing materials and educational resources, it becomes increasingly difficult to streamline, categorize, and tailor content to meet the specific needs and preferences of diverse audiences.
Elevating Content Intelligence in Pharma
A modern content approval workflow is essential for pharma organizations to ensure fast, compliant, and auditable delivery of scientific and promotional materials. AI-driven solutions can streamline the multi-stage review process, automate compliance checks, and facilitate real-time collaboration among stakeholders, reducing bottlenecks and minimizing the risk of regulatory delays. This efficiency not only accelerates content deployment but also enhances overall content quality and governance.
AI content tagging equips every digital asset with rich, precise metadata, making it far easier to search, retrieve, and personalize content at scale. In pharma, automated tagging powered by advanced artificial intelligence and machine learning models ensures scientific accuracy and regulatory alignment, while speeding up the organization and categorization of high volumes of medical and commercial information. The result is dramatically improved discoverability and agility in multichannel engagement.
Content atomization, driven by Generative AI, or GenAI, enables organizations to break down complex assets into bite-sized, reusable components or “atoms.” This approach unlocks true omnichannel engagement in pharma by allowing teams to quickly assemble and adapt approved messages for diverse audiences across digital platforms. Automated content atomization drives both consistency and compliance, while reducing content production times.
Content intelligence in pharma brings together advanced analytics, AI-driven insights, and workflow automation to help organizations maximize the impact of their scientific and commercial content. By monitoring tag usage, approval cycles, and audience interactions, teams can rapidly identify trends, address gaps, and optimize performance. This data-driven approach empowers smarter, more agile decision-making, resulting in measurable gains in engagement, compliance, and commercial effectiveness.
Indegene’s NEXT Commercial Content Intelligence (NCCI) is a proprietary artificial intelligence (AI)-based solution for content atomization and tagging, specifically engineered for pharma organizations. By utilizing robust artificial intelligence and machine learning (AI/ML) models, NCCI deconstructs an array of assets, including iDetails, representative triggered emails (RTEs), banners, webpages, videos, and more, into atomized versions and then helps generate relevant tags.
Serving over 10 pharmaceutical majors for over half a decade, NCCI has demonstrated capabilities in being an automated, consistent, and scalable solution, transforming multiple use cases, including content discoverability, analytics, personalization, and many more.
With the integration of Generative AI, NCCI has now further enhanced its capabilities. This blog post details how NCCI became a comprehensive AI–powered content intelligence tool for pharma, including the opportunities explored, challenges faced, and outcomes achieved.
Areas where GenAI can help improve content management and optimization
While NEXT Commercial Content Intelligence’s current supervised learning–based models were adequate, generative AI offered the opportunity to achieve fully automated content atomization and tagging, making the entire journey more efficient and effective. The steps involved in this journey and the potential impact of generative AI are summarized in the table below:
| Stage | Activities | Impact of generative AI | Impact level |
|---|---|---|---|
Taxonomy Design | Use case comprehension, tag requirements, category/definition crafting, overlap analysis, manual tagging, and client revisions | Expedites taxonomy creation, seamlessly handles overlaps, and streamlines revisions | High |
Model Development and Deployment | Dataset preparation, model creation, validation, and deployment | Bypasses conventional model generation steps by using intelligent prompts and optimizing model development | High |
Machine Tagging | Content upload, running the model, and output generation | - | - |
Subject matter expert (SME) Review | Asset audit, content comprehension, and tag validation | Generative AI enables content summarization, key area highlighting, and SME workload reduction | High |
Output Generation and Transfer | Reports and API tag transfers | - | - |
Model Retraining | Data selection, pipeline run, validation, and new version deployment | Generative AI–driven accuracy reduces retraining needs, allowing the effective use of intelligent prompts for efficient model enhancement | High |
Explore how Indegene significantly boosted lead conversion rates by 30% for a major pharma company with AI-driven content personalization.
Boosting content atomization and content tagging with GenAI
While powering NEXT Commercial Content Intelligence with GenAI is critical to staying future ready, the path to production involved several challenges. The following section attempts to explain the process of transitioning to GenAI for one core capability, that is, atom-level key message generation.
NCCI’s core contained an atom-level key message generation model that was built through supervised learning, utilizing 250,000+ content pieces specific to the pharmaceutical domain. This model included 17 categories (efficacy, safety, brand information, study design, etc.) and was continuously retrained over the past 3 years to attain an accuracy of 83%. The aim was to now improve this model with GenAI.
Pilot experiment
The NCCI team experimented using a limited sample size with GPT 3.5 Turbo to generate key messages for 100 atoms. Several prompting techniques were experimented with before narrowing down to the prompt with the highest prediction accuracy.
| Trial number | Prompt parameters | Accuracy with GPT-3.5 Turbo |
|---|---|---|
1 | Category names and OCR text of the atom | 33.2% |
2 | Category definition and OCR text of the atom | 51.8% |
3 | Category definition, OCR text of the atom, and role definition | 62.3% |
4 | Categories redefined (overlaps were identified and removed), OCR text of the atom, and role definition | 89.8% |
Since the pilot experiment now had an accuracy higher than the existing model, the team decided to productionize the feature with GPT-3.5 Turbo.
Scaled experimentation
The experiment was then extended to 1,000 atoms, and 3 runs were conducted. The following observations were noted:
Consistency between the runs was only 30%, that is, the same prompt given 3 times resulted in the same output only in 30% of the cases
Unintended responses were received in 20% of the cases. This included
Response outside the given/defined categories
Response with no categories
To improve the response and make the model more deterministic, hyperparameter tuning was used. After several batch tests, the temperature and top P values were optimized. The consistency improved to 87%.
To avoid unintended responses, a condition was set to verify the response before acceptance and a prompt was defined to resend and revise the output. This reduced the unintended responses to <2%. For the cases where the output was still unintended, the platform was modified to provide default values.
Final run and deployment
After hyperparameter tuning, a run for 1000 atoms was conducted and an accuracy of 84.8% was achieved. This model was cleared for deployment because the results were significantly higher than the earlier available model.
The deployment architecture was designed to integrate NEXT Commercial Content Intelligence’s content atomization workflow with the GPT-3.5 Turbo–based atom-level key message identification. This deployment also took into consideration the data privacy and security concerns of the pharmaceutical majors, making the information exchange completely secure and protected.
Custom taxonomies and use cases
Every NEXT Commercial Content Intelligence customer requires additional custom categories in addition to the default set already present in the core model. Experiments were run to accommodate new categories and modify existing categories to ensure that similar accuracies could be achieved for custom categories with prompt tuning.
Benefits of using GenAI to boost content atomization and content tagging
Reduced time to deployment: A 70% decrease in time to deployment was achieved because the model could be tuned with just prompts and did not require custom dataset preparation and model customization.
Reduced total costs: The total cost of deployment and operations for 5000 pages of content is summarized in the graph below. While model training, deployment, and retraining costs were lower for GPT-3.5 Turbo, the operational charges were slightly higher. However, the total cost of tagging reduced by a staggering 76%.
Next steps and planned refinements
The NEXT Commercial Content Intelligence team noticed that GPT-3.5 Turbo produced lower accuracy in one key area, that is, key message identification for graphical components with unstructured optical character recognition (OCR) text such as logos, graphs, illustrations, diagrams, and so on. Experiments are now being conducted to improve results in this area.
Additional GenAI–powered content management capabilities
With similar replacements to the core models, NEXT Commercial Content Intelligence is now powered by GenAI for most of its capabilities. A few of these key capabilities and their performance are summarized in the table below:
| Stage | Activity | Performance* |
|---|---|---|
Taxonomy Design | Taxonomy category definition Taxonomy definition validation Taxonomy overlap analysis and redefinition | 90% reduction in efforts required for a taxonomy design with generative AI support |
Model Development and Deployment | Content atomization | New models (e.g., Segment Anything) were able to maintain the contours of graphics and identify atoms with a coverage of 93%, compared with the earlier model’s coverage of 78% |
Brand identification | The GPT-3.5 Turbo–based model improved accuracy from 83% to 93.33% | |
Therapy area identification | The GPT-3.5 Turbo–based model improved accuracy from 79% to 83.33% | |
Keyword identification –English | The GPT 3.5-Turbo–based model improved coverage from 76% to 93.33% | |
SME Review | Tag validation | The GPT 3.5-Turbo–based summary generation and attention optimization reduced SME review efforts by 54% |
*Results are based on limited experimentation.
GenAI-driven Content Intelligence in Pharma: Insights That Drive Action
Content intelligence in pharma brings together advanced analytics, AI-driven insights, and workflow automation to help organizations maximize the impact of their scientific and commercial content. By monitoring tag usage, approval cycles, and audience interactions, teams can rapidly identify trends, address gaps, and optimize performance. This data-driven approach empowers smarter, more agile decision-making, resulting in measurable gains in engagement, compliance, and commercial effectiveness.
NEXT Commercial Content Intelligence was built to transform pharmaceutical content tagging and make it more automated, consistent, scalable, and affordable. Today, GenAI presents a transformative opportunity to further enhance these core goals.
However, using GenAI to deliver consistent and reliable output for business use cases comes with a set of factors that must be considered. These include:
Model reliability: The degree of control and understanding of model characteristics in a generalized large language model (LLM) model is much lower than a supervised learning model. This makes it necessary to regularly run scaled experiments to verify and sustain reliable output.
Version updates: LLM models are continuously updated and upgraded, and as observed during our experimentation, the output from each version is different. This requires continuous monitoring to ensure consistency and continuous improvement.
Consistency-Accuracy balance: When GenAI is used for classification, low randomness affects the accuracy of the prediction, while high randomness affects consistency. Therefore, a balance between accuracy and consistency should be maintained for reliable output.
With newer and more powerful generative AI releases upcoming, the NEXT Commercial Content Intelligence team will continue to identify and experiment with more transformative changes that deliver performance gains to life sciences businesses. Watch this space for more updates from us in the coming months.
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