Andrew Wyatt, Chief Growth Officer at Sapio Sciences
Generative AI can process information at incredible speed, but it cannot yet think like a scientist. In biopharma R&D, its real value lies in assisting scientists, automating routine tasks, interpreting data in context, and moving research faster. The next generation of AI-native lab notebooks goes further, turning AI into an active collaborator that helps scientists move from “what if” to “let’s find out.”
The Limits of Generative Artificial Intelligence
Generative AI (Gen AI) and the large language models (LLMs) behind it have recently become some of the most talked-about technologies in both business and everyday life, sparking widespread debate about the future of work. They have inspired countless predictions and generated excitement across many industries for their potential to automate knowledge-based tasks.
In biopharma R&D, however, the question is not simply what AI can do, it is how it should help. Since the release of modern generative AI tools, there has been speculation about whether these systems could one day replace scientists, with suggested use cases ranging from accelerating literature reviews to protocol drafting. While these models are capable of impressive analysis and pattern recognition, they struggle to apply true scientific reasoning, understand experimental intent, interpret results in context, and link data to hypotheses.
But the real opportunity for AI today is not as a replacement; it is as a complement to the tools and scientists already driving innovation. The issue is not that generative AI models aren’t powerful, it’s that they are designed to be broadly useful across many domains. They are trained using public content and generalised data, not the proprietary, structured, and experimental data that drives biopharma R&D.
Generative AI may excel at handling language, but it still lacks scientific fluency. These models often fail to distinguish between a sample and a reagent, and cannot interpret assay results in context or anticipate whether a protocol step is valid or flawed. They know a lot, but do not think like scientists.
From Assistance to Agency
What has emerged is a set of practical, targeted uses. These are applications where the AI’s rapid processing and recall can be precisely directed to assist scientists. AI can help streamline repetitive or administrative lab tasks, assist in drafting workflows, and suggest possible interpretations of structured data. For most scientists, decision-making and creative problem-solving are core to their work. There is little appetite and no current need to give that up.
Some of the most impactful AI applications today are focused on accelerating the gap between idea and execution. Rather than automating science itself, AI can reduce the manual burden of tasks that pull scientists away from research. This helps scientists stay focused on the science itself, rather than getting pulled into administrative or technical detail.
For example, AI can:
- Translate high-level experiment descriptions into structured steps and protocol templates
- Retrieve data based on contextual, natural-language queries, rather than requiring complex filters or forms
- Track materials and consumables based on protocol logic
- Guide scientists through unfamiliar lab software, reducing the learning curve for new tools
But to do this well, the AI must not only understand language. It must understand science, and it must be embedded in scientific software that reflects real-world research environments.
From Passive Recording to Active Collaboration
This need has led to the emergence of third-generation Electronic Lab Notebooks (ELNs), sometimes referred to as Artificially Intelligent Lab Notebooks (AILNs). These are AI-native platforms that go beyond documenting experiments. They actively participate in the process of doing science.
Rather than forcing users to adapt to rigid workflows, these tools are built to understand the intent behind scientific work. They can help researchers plan and refine experiments, support decision-making, and automate many of the steps that traditionally required specialist support. AILNs are unique in that they understand both science and themselves, combining awareness of scientific logic with an understanding of the data structures, workflows, and context of real-world research environments.
AILNs can assist with:
- Suggesting or validating complex tasks such as retrosynthesis, codon optimisation, or molecular docking
- Capturing actions and decisions in a traceable, reviewable format, supporting compliance without adding friction
The System That Learns The Science
The key evolution in these platforms is that they reverse the traditional dynamic. Earlier ELNs required users to learn how to use the software. Third-generation ELNs are built to learn how scientists work. They allow researchers to interact naturally, often using simple text prompts, and automate the underlying complexity of the task.
With the help of natural language processing (NLP), these systems can:
- Search and retrieve historical or cross-database data.
- Interpret SOPs and convert them into structured experiment templates.
- Manage experiment-related files and track material usage.
- Guide users through software without requiring prior training.
The result is a more intuitive, less disruptive way to run research.
A New Foundation for Scientific Work
Third-generation ELNs represent a fundamental change in how digital lab tools support science. Instead of acting as passive record-keepers, modern ELNs are becoming intelligent collaborators, making it easier to turn a new idea into an executable workflow and reduce the effort required to explore novel directions.
By accelerating the path from “what if” to “let’s find out,” these platforms help researchers spend more time thinking and less time formatting, searching, or switching tools. AI will not replace scientists, but it will reshape what scientists expect from their tools and fundamentally accelerate the boundaries of discovery.
To find out more, visit https://www.sapiosciences.com/
