The period since the public launch of ChatGPT 3.5 in November 2022 has been marked by an unprecedented wave of excitement and investment in Artificial Intelligence (AI) within the life sciences sector. As of July 2025, the narrative remains one of explosive potential. Yet, despite this enthusiasm the widespread adoption of truly impactful AI applications in clinical trials remains elusive.
Many initiatives are stuck in “pilot purgatory,” failing to scale beyond initial experiments. While use of generative AI tool for clinical documentation seems to have some adoption, success in other areas remain limited.
Beyond the First Draft: The Reality
The hype around generative AI often focuses on its ability to produce a first draft. However, these draft outputs often fall short of the quality and nuanced reasoning that experienced professionals bring.
But that is not the only problem. For most tasks involving biostatistics, programming, data management, and medical writing, an initial draft requires just minor effort for the overall task. True productivity gains come from managing the entire end-to-end lifecycle of a task—from specification and planning through generation, multiple rounds of collaborative iteration, and finalization. Most of these tasks happen in MS Word or Excel. These complex processes require maintaining an intricate web of connections between data, text, analyses, and people.
For most of the tasks, the critical bottleneck for productivity is not the initial generation step. A first draft produced by AI in minutes provides substantial yet small overall value if the subsequent review, correction, revisions, and QC processes still take months due to disconnected systems, manual data reconciliation, and a lack of traceability. True productivity gains can come from accelerating the entire interconnected lifecycle, not just one isolated part of it.
Building the Right Cake Layers
To enable such end-to-end acceleration, organizations need systems that are purpose-built to support the entire workflow. This means tightly integrating AI with document generation, structured data sources, collaborative editing, version control, role-based access, audit trails, and review workflows. By bolting AI onto disconnected systems, it is hard to achieve transformational gains.
Before AI can truly deliver productivity gains, it requires a solid foundation of application layers working together beneath it. Let’s break down the essential components that need to be in place for AI to function smoothly and deliver real value. These layers are the building blocks that enable seamless integration, collaboration, and control across the entire workflow. These include:
User Interface and Workflow Layer: A robust web application interface where users (e.g. data managers, biostatisticians, medical writers) interact with the AI. This includes task dashboards, spreadsheets, forms to input parameters, and a workflow enabling creation of deliverables from draft to final. For instance, a programmer should have a UI where they can request an AI-generated draft of programming specs, and then have tools to edit that draft in one place. Data Storage and Knowledge Management: Under the hood, the system needs secure data storage for all the content involved – protocols, datasets, reference documents, templates, etc. Moreover, an AI in isolation (just a language model) lacks up-to-date factual knowledge about your specific domain. Knowledge management integration is critical: connecting the AI model to relevant internal databases and document repositories so it can pull in accurate, context-specific information. This often involves Retrieval Augmented Generation (RAG), where the AI fetches facts from a curated knowledge base before answering. The foundation must include systems to manage these knowledge sources and feed them into the AI in real time. Document Management and Editing Tools: Since outputs like reports or analyses will be documents, the platform needs built-in document management capabilities. This means version control (every AI-generated draft saved), collaborative editing (multiple people can comment or edit, with changes tracked), and templates or style guides to ensure consistency. Essentially, an AI-assisted word processor tailored for clinical content. Without this layer, an AI might generate text that then has to be copy-pasted into Word and manually managed – losing efficiency. A good foundation bakes the content management right into the app. Collaboration and Review System: Clinical trial work is highly collaborative and requires rigorous review/approval cycles. The AI platform must support multi-user workflows – for example, after AI assists in drafting a specs or document, it might go to a statistician for technical review. The system should facilitate varying approaches to review workflow required across organizations, tracking approvals, and capturing an audit trail of who did what. Audit trails and traceability are not optional in this industry; they are required for compliance. So the platform’s architecture must include audit logging at every step – including recording AI actions (what prompt was given) so that there is traceability of the AI’s contributions. Cloud Infrastructure and Scalability: A modern AI-driven application usually is built cloud-native or uses cloud services to handle spikes (e.g., generating a massive report with an LLM might momentarily need lots of CPU/GPU power). The foundation includes the cloud infrastructure, containerization, and scaling mechanisms to ensure the AI features run quickly and reliably. It also involves managing costs, since AI usage (tokens, etc.) can be expensive – so monitoring and optimizing the infrastructure is part of the game. Access Control and Security: Because we’re dealing with sensitive data, the platform needs enterprise-grade access management. That means integration with single sign-on, role-based permissions (e.g., a data manager can trigger data cleaning AI tools but maybe cannot generate a certain report without authorization), and encryption of data both at rest and in transit. Continuous Improvement & AI Model Management: The AI field is evolving at breakneck speed – new model versions and features are released frequently. For instance, OpenAI’s latest API in May 2025 introduced powerful new tools and features for developers. To leverage such improvements, an AI-driven application needs a flexible AI integration layer. This means designing the software in a modular way so that underlying AI models can be upgraded or swapped with minimal disruption.
That’s a long list of application layers, and it underscores why very few existing tools have cracked the code on productizing AI in this space. Most legacy eClinical systems were built years ago (often a decade or more) with a focus on specific functions (EDC for capturing data, CTMS for tracking sites, etc.), and they lack many of these modern layers or are not compatible for AI integrated workflow.
To truly leverage AI, companies must establish an interoperable data, document and analytics infrastructure, creating a strong, well-designed foundation that empowers innovation. In other words, you need to have the cake ready before you can decorate it with AI icing.
On the flip side, starting from scratch with a new AI-native platform is an opportunity to “bake in” all the needed layers from day one, but it’s also a significant undertaking. One has to assemble all the fundamental capabilities (the layers we described) and the AI smarts together – which is why not many have done it yet. It’s a tall order. But this is likely the path to success if we want truly transformative AI applications rather than incremental add-ons.
In essence, legacy vendors are in a difficult position: rearchitecting their products for AI is tedious and expensive, but not doing so means falling behind. New entrants or forward-looking organizations have the advantage of starting with a clean slate, but they have to build a lot from nothing and prove themselves.
We at Nimble have embarked on this journey of taking the bold approach of building an AI-powered platform from the ground up, specifically designed to overcome the challenges we discussed (rather than layering AI onto a legacy stack). Without legacy constraints, Nimble’s platform is aiming to integrate all the necessary layers – from robust data management, visualization, and document automation to the latest generative AI capabilities – in one cohesive solution. The idea is to deliver holistic applications that truly boost productivity in biostatistics, data management, and medical writing tasks, rather than just offering a chatbot or quick-n-dirty drafts.
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The period since the public launch of ChatGPT 3.5 in November 2022 has been marked by an unprecedented wave of excitement and investment in Artificial Intelligence (AI) within the life sciences sector. As of July 2025, the narrative remains one of explosive potential. Yet, despite this enthusiasm the widespread adoption of truly impactful AI applications in clinical trials remains elusive.
Many initiatives are stuck in “pilot purgatory,” failing to scale beyond initial experiments. While use of generative AI tool for clinical documentation seems to have some adoption, success in other areas remain limited.
Beyond the First Draft: The Reality
The hype around generative AI often focuses on its ability to produce a first draft. However, these draft outputs often fall short of the quality and nuanced reasoning that experienced professionals bring.
But that is not the only problem. For most tasks involving biostatistics, programming, data management, and medical writing, an initial draft requires just minor effort for the overall task. True productivity gains come from managing the entire end-to-end lifecycle of a task—from specification and planning through generation, multiple rounds of collaborative iteration, and finalization. Most of these tasks happen in MS Word or Excel. These complex processes require maintaining an intricate web of connections between data, text, analyses, and people.
For most of the tasks, the critical bottleneck for productivity is not the initial generation step. A first draft produced by AI in minutes provides substantial yet small overall value if the subsequent review, correction, revisions, and QC processes still take months due to disconnected systems, manual data reconciliation, and a lack of traceability. True productivity gains can come from accelerating the entire interconnected lifecycle, not just one isolated part of it.
Building the Right Cake Layers
To enable such end-to-end acceleration, organizations need systems that are purpose-built to support the entire workflow. This means tightly integrating AI with document generation, structured data sources, collaborative editing, version control, role-based access, audit trails, and review workflows. By bolting AI onto disconnected systems, it is hard to achieve transformational gains.
Before AI can truly deliver productivity gains, it requires a solid foundation of application layers working together beneath it. Let’s break down the essential components that need to be in place for AI to function smoothly and deliver real value. These layers are the building blocks that enable seamless integration, collaboration, and control across the entire workflow. These include:
User Interface and Workflow Layer: A robust web application interface where users (e.g. data managers, biostatisticians, medical writers) interact with the AI. This includes task dashboards, spreadsheets, forms to input parameters, and a workflow enabling creation of deliverables from draft to final. For instance, a programmer should have a UI where they can request an AI-generated draft of programming specs, and then have tools to edit that draft in one place.
Data Storage and Knowledge Management: Under the hood, the system needs secure data storage for all the content involved – protocols, datasets, reference documents, templates, etc. Moreover, an AI in isolation (just a language model) lacks up-to-date factual knowledge about your specific domain. Knowledge management integration is critical: connecting the AI model to relevant internal databases and document repositories so it can pull in accurate, context-specific information. This often involves Retrieval Augmented Generation (RAG), where the AI fetches facts from a curated knowledge base before answering. The foundation must include systems to manage these knowledge sources and feed them into the AI in real time.
Document Management and Editing Tools: Since outputs like reports or analyses will be documents, the platform needs built-in document management capabilities. This means version control (every AI-generated draft saved), collaborative editing (multiple people can comment or edit, with changes tracked), and templates or style guides to ensure consistency. Essentially, an AI-assisted word processor tailored for clinical content. Without this layer, an AI might generate text that then has to be copy-pasted into Word and manually managed – losing efficiency. A good foundation bakes the content management right into the app.
Collaboration and Review System: Clinical trial work is highly collaborative and requires rigorous review/approval cycles. The AI platform must support multi-user workflows – for example, after AI assists in drafting a specs or document, it might go to a statistician for technical review. The system should facilitate varying approaches to review workflow required across organizations, tracking approvals, and capturing an audit trail of who did what. Audit trails and traceability are not optional in this industry; they are required for compliance. So the platform’s architecture must include audit logging at every step – including recording AI actions (what prompt was given) so that there is traceability of the AI’s contributions.
Cloud Infrastructure and Scalability: A modern AI-driven application usually is built cloud-native or uses cloud services to handle spikes (e.g., generating a massive report with an LLM might momentarily need lots of CPU/GPU power). The foundation includes the cloud infrastructure, containerization, and scaling mechanisms to ensure the AI features run quickly and reliably. It also involves managing costs, since AI usage (tokens, etc.) can be expensive – so monitoring and optimizing the infrastructure is part of the game.
Access Control and Security: Because we’re dealing with sensitive data, the platform needs enterprise-grade access management. That means integration with single sign-on, role-based permissions (e.g., a data manager can trigger data cleaning AI tools but maybe cannot generate a certain report without authorization), and encryption of data both at rest and in transit.
Continuous Improvement & AI Model Management: The AI field is evolving at breakneck speed – new model versions and features are released frequently. For instance, OpenAI’s latest API in May 2025 introduced powerful new tools and features for developers. To leverage such improvements, an AI-driven application needs a flexible AI integration layer. This means designing the software in a modular way so that underlying AI models can be upgraded or swapped with minimal disruption.
That’s a long list of application layers, and it underscores why very few existing tools have cracked the code on productizing AI in this space. Most legacy eClinical systems were built years ago (often a decade or more) with a focus on specific functions (EDC for capturing data, CTMS for tracking sites, etc.), and they lack many of these modern layers or are not compatible for AI integrated workflow.
To truly leverage AI, companies must establish an interoperable data, document and analytics infrastructure, creating a strong, well-designed foundation that empowers innovation. In other words, you need to have the cake ready before you can decorate it with AI icing.
On the flip side, starting from scratch with a new AI-native platform is an opportunity to “bake in” all the needed layers from day one, but it’s also a significant undertaking. One has to assemble all the fundamental capabilities (the layers we described) and the AI smarts together – which is why not many have done it yet. It’s a tall order. But this is likely the path to success if we want truly transformative AI applications rather than incremental add-ons.
In essence, legacy vendors are in a difficult position: rearchitecting their products for AI is tedious and expensive, but not doing so means falling behind. New entrants or forward-looking organizations have the advantage of starting with a clean slate, but they have to build a lot from nothing and prove themselves.
We at Nimble have embarked on this journey of taking the bold approach of building an AI-powered platform from the ground up, specifically designed to overcome the challenges we discussed (rather than layering AI onto a legacy stack). Without legacy constraints, Nimble’s platform is aiming to integrate all the necessary layers – from robust data management, visualization, and document automation to the latest generative AI capabilities – in one cohesive solution. The idea is to deliver holistic applications that truly boost productivity in biostatistics, data management, and medical writing tasks, rather than just offering a chatbot or quick-n-dirty drafts.