In rapidly evolving sectors, R&D divisions are in increasing need of innovative solutions. Innovation processes like experimental review, design and analysis are not only lengthy, but also extremely tedious. With the use of Agentic AI, R&D divisions get maximum benefits and researchers now have the space to think deeply, as it will take over the repetitive tasks of knowledge discovery.  

What qualifies Agentic AI as Agentic?  

Agentic AI stands out from other AI tools because it does not require a set of fixed procedures to follow. It is similar to a modern research assistant as it possesses an understanding of the objectives, undertakes planning, data collection and decision-making from numerous data sources and even continues the work in the absence of human supervision. 

One can call it as a collaborator in computation rather than mere software or a tool.  

AI Enhances Innovation Efficiency 

An AI study from a large R&D lab in the U.S. indicated that with AI-enabled tools to assist in the exploration of materials, researchers were able to invent 44% more materials, file 39% more patents, and improve product innovations by 17%.  

With improvements in innovation, AI is able to solve nearly 57% of the problems related to idea generation, thus relieving researchers of the burden to focus on evaluation and decision making. (Ref: https://theaiinsider.tech/2024/11/12/nber-study-artificial-intelligence-may-boost-research-lab-output-but-could-exacerbate-existing-inequalities/) 

Practical applications in R&D 

• Autonomous Research & Hypothesis Generation: Contemporary agentic AI tools are capable of managing intricate research processes in life sciences. For example, OpenAI’s DeepResearch is proficient in managing detailed research tasks that involve careful planning, data evaluation and providing summaries, which allows for automation of life sciences processes with minimal human intervention. Agentic AI has proven to be effective with platforms like Causaly (Ref: https://www.causaly.com/), which in turn, significantly reduces the time required to manually review scientific literature in life sciences by reading, mapping and correlating scientific documents and proposing actionable hypotheses. 
• Scientific Discovery via Multi-Agent Systems: The Aleks system is a multi-agent AI designed for plant science. Given a research question and dataset, it iterates independently formulating problems, exploring modelling strategies and refining solutions demonstrated via successful grapevine disease modelling. 

Another example is AI Scientist-v2 which automatically drafts scientific papers that meet peer reviewed workshop standards. It writes papers at these ‘v2’ level with no manual intervention, starting at hypothesis, through experimentation, and ending with analysis and writing. (Ref: https://arxiv.org/html/2502.14297v2) 

Why this matters for R&D Teams 

It has multiple advantages like:  

• Efficiency: Repetitive tasks like searching or summarizing are handled autonomously. 
• Scale of Exploration: AI can simulate thousands of hypotheses or materials rapidly. 
• Human Focus on Strategy: Researchers spend time on validation, creativity, and decision-making. 
• Quantifiable Outcomes: 44% more discoveries and 39% more patents show measurable value.   

Getting started with Agentic AI in R&D 

• Identify a pilot in high-impact materials discovery or hypothesis generation. 
• Implement a proof of concept in agentic AI for example, have DeepResearch or a multi-agent tool in your specific domain conduct automated knowledge searches.  
• Evaluate and track results: assess the amount of time saved, insights generated, or acceleration of innovation.  
• Maintain oversight and collaboration to have researchers validate and guide the AI outputs. 

In markets that are continually shifting, the most beneficial advantage is not in foreseeing what’s coming next, but in speeding up the rate at which discoveries are made and in adapting in a responsive fashion. Agentic AI is the solution for this as it’s able to make R&D divisions more efficient not by replacing the human’s inexhaustible curiosity, but by deeply amplifying it. From the effortless excavation of insights from data to the autonomous brainstorming of ideas and experiment designing, Agentic AI makes the collaboration between AI and specialist researchers scalable, allowing innovation as well as impact to exponentially increase.