AI in R&D: Why accelerating adoption is key to a competitive edge
R&D teams are under increased pressure to innovate rapidly and deliver business value. Failure to do both can lead to a lost competitive edge. It's well-documented that AI is a powerful technology with the potential to transform research, literature reviews and more, altering how decisions are made by leadership.
As of 2025, 78% of organizations surveyed by McKinsey use AI in at least one business function. But in complex regulated R&D environments, adoption remains uneven. Many organizations are unsure where to begin or how to evaluate the right tools.
Why adopting GenAI in R&D can’t wait
The pace of innovation is accelerating, and so are expectations. Shareholders and executive teams increasingly demand faster development cycles, better use of data and clearer return on digital investment. In science-driven sectors, where experimentation is time- and resource-intensive, the competitive advantage now belongs to those who act early and responsibly.
Failing to adopt AI in R&D doesn’t just mean missing opportunities. It means falling behind competitors who are already reshaping R&D timelines, reducing cost and gaining insight faster than ever before. AI-first organizations are already redefining benchmarks for performance and scale.
Current estimates indicated that by the end of 2025, more than 30% of new drugs and materials are expected to be discovered using GenAI techniques. In biopharma alone, GenAI is projected to drive $4–$7 billion annually in value through cost reductions, productivity gains and enhanced quality.
What GenAI can accomplish
Adopting GenAI is not about trialing the latest technology. It’s about solving persistent operational challenges and driving real businesses value.
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Accelerate speed to market and faster innovation cycles
- Automate repetitive and time-intensive tasks such as literature reviews, data search, documentation summaries and experiment write-ups
- Use data-driven insights to reduce early-stage experimentation timelines
- Free up expert researchers to focus on strategic problem-solving and hypothesis development
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Drive insights from complex data and enhance decision making
- Combine internal and external datasets to identify trends and novel patterns
- Forecast potential outcomes and prioritize research projects with higher confidence
- Support faster cross-functional decision-making through clear, accessible summaries
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Reduce business risk and optimize cost and resource use
- Use AI simulations and predictive models to refine hypotheses and reduce trial-and-error cycles
- Identify weak signals or missed correlations earlier in the research process
- Improve reliability and reproducibility of insights, reducing costly rework
Business outcomes from successful GenAI adoption in R&D
Productivity
Firms with strong GenAI capabilities report up to 7.8% productivity gains
Speed
AI has the potential to accelerate R&D throughput by 75% in chemicals and more than 100% in pharma
Value
Could unlock between $360 billion - $560 billion in economic value for key industries
Efficiency
Average improvements to throughput and cost-efficiency reach 20.1%
Risks and guardrails
Like any emerging technology, integrating GenAI is not without potential pitfalls. Without the right foundation, GenAI can introduce additional risk, not remove it. Organizations that adopt tools without proper alignment to their domain, workflows or compliance requirements may face regulatory, operational or reputational consequences.
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The list below sets out the most common pitfalls and the guardrails needed to avoid them. |
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What can go wrong? |
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What to look for in a solution? |
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Poor or incomplete data |
High-quality, curated data |
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Opaque “black box” outputs |
Explainability and traceability |
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Security vulnerabilities |
Robust security controls |
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Uncontrolled bias or unfair outcomes |
Responsible AI principles |
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Success stories: GenAI in R&D
While broader adoption is still ramping up, early adopters are already reporting measurable gains:
“The AI features on ScienceDirect have significantly boosted my efficiency. I’d rate these features a solid 9 or 10 for how much they enhance my workflow, especially when I’m starting from scratch.”
– Director of Medical Affairs, leading biotech company
“What once took three weeks now takes just a week and a half, saving me over 50% of my time. Unlike keyword-based searches, ScienceDirect AI delivers well-structured references directly relevant to my research question.”
– R&D Manager, leading global food manufacturer
“I estimate I’ve increased my paper reading from two to three to nearly 10 per week, enhancing both the quality and depth of my research. Overall, I rate the impact of ScienceDirect AI on my work efficiency an 8 out of 10.”
– Scientist, leading biotech company
“Quick access to regulatory precedents through PharmaPendium AI helps me design better (nonclinical) strategies.”
– Head of Nonclinical Toxicology and DMPK, leading biopharmaceutical company
“Embase AI can save us more than 50% of the time we spend answering physicians’ questions.”
– Medical Affairs Manager, large pharmaceutical company
“Embase AI is changing how I think about the problem. It helps me ask the right questions.”
– Product Manager, large MedTech
Your partner for progress
Even as AI’s role in R&D has grown, the reality remained that the majority of corporate AI initiatives fail. However, it has been found that the majority of AI tools developed as part of a strategic partnership succeed – twice as much as purely internal builds.
In the AI age, it’s clear: with the right partner, you can go further, faster. Whether you are looking to bring in third-party data to support in-house initiatives, or if you are looking for a secure, ready-to-use AI solution based on trusted content.
Elsevier is a global leader in seamlessly integrating trusted quality information, technology and expertise to provide R&D solutions for better outcomes. Our platforms and tools are designed for today’s fast-paced R&D workflow. No matter where you are in your AI adoption, we are prepared to partner with you and help your R&D teams succeed.
Elsevier's AI portfolio can support your R&D through the following:
- Broad, general literature search
Connect researchers to relevant insights and information - Specialist search and analysis
Access quick, relevant responses to complex research questions - Deep full-text reading
Summarize complex literature quickly - Semantic and AI search solutions
Create a custom AI search interface to extract insights from in-house and external data - Datasets, semantic software and expertise
Curate and organize external and internal data for your AI platforms
Learn more about Elsevier’s solutions to support AI success:
Watch now: AI in R&D panel
Earlier this year, Elsevier held a panel with three experts on AI’s role in corporate R&D transformation. They discuss real-world applications of AI in research, navigating AI policy and regulation, and success stories from their companies on how AI is already impacting their business.