This article was originally published on Forbes and can be found here.
By Michael Connell, EdD | Chief Operating Officer, Enthought Inc.
AI is reshaping every industry, but scientific research and development—drug discovery, materials innovation, specialty chemicals and more—is about to undergo one of the most profound transformations. McKinsey identified R&D as one of the four sectors that would see the greatest positive impact of generative AI in the near term, with R&D currently being the one lagging the most, but with perhaps the greatest ultimate potential benefit.
Here are six predictions for how AI will reshape R&D in the next decade.
1. Vertical, AI-first scientific solutions will dominate.
Tomorrow’s indispensable scientific tools won’t just be traditional software "enhanced" by AI—they’ll be entirely redesigned around AI. Because scientific R&D is highly "domain-dense" and regulation-heavy, a vertical focus is essential. Expect revolutionary ELNs, LIMS and informatics tools custom-built for specialized fields like medicinal chemistry, polymer design and catalyst discovery.
Given the variability between labs and the unique nature of R&D work, custom, niche AI tools are going to become much more prevalent. Success requires internal and/or external domain-plus-AI experts who understand R&D workflows, scientific data and how to integrate the new technologies into legacy systems. Those with access to this expertise will be the ones to succeed.
2. Predictive AI will become more valuable than generative AI.
Generative AI may grab attention for its content creation capabilities, but the real goldmine in R&D is knowing which experiments not to run. Predictive AI models will enable researchers to eliminate months of physical experimentation, replacing slow trial-and-error cycles with overnight virtual explorations of vast design spaces.
To capitalize on this shift, companies will need to prioritize the development of robust predictive models and engineering design models that leverage them. Researchers must also be trained to interpret and develop confidence in the predictions made by AI in order to integrate them seamlessly into their experimental design and product development workflows.
3. Laboratories will operate as autonomous workflows.
Lab automation is about to leap from managing instruments to managing teams. AI-powered agents will handle scheduling, reagent procurement, quality control and even supervise experimental execution. As AI orchestrates routine tasks, scientists will increasingly focus on higher-order decision-making and creative hypothesis generation. Labs should begin to integrate AI into their operational processes, starting with automating repetitive tasks and gradually expanding AI's role to more complex lab functions and expectations.
4. Creative ideation will be the ultimate differentiator.
When AI can generate thousands of plausible drug molecules or materials overnight, human ingenuity becomes the most precious commodity. Companies must forge strategies that seamlessly blend human insight with AI-generated outputs, creating a powerful synergistic approach to innovation. Those who cultivate originality, unconventional thinking and "scientific taste"—the ability to pick the most promising ideas out of the noise—will lead the market.
5. Data ethics and ownership will spark significant backlash.
As proprietary data from labs, clinical trials and private research increasingly feeds AI models, ethical and IP battles will intensify. Expect heightened scrutiny around data privacy, ownership and consent, leading to major shifts in how research data is valued, protected and compensated.
On the other hand, the benefits of pooling data across organizations to train specialized models and agents are likely to be substantial for all parties involved, including end users. Organizations and consortia that figure out how to do this successfully (such as through a federated data model, where the data can be used for training without being accessible to others) will have a significant competitive advantage.
6. New R&D roles will emerge.
The most sought-after hires in research will soon be "AI workflow designers" and "head of AI operations." AI workflow designers will be expected to expertly translate human scientific processes into highly automated, AI-driven workflows. Heads of AI operations will be responsible for the integration of tools and overseeing outcomes of all the AI initiatives across the R&D organization.
For many organizations, developing these roles internally will be a significant undertaking. The ability to effectively leverage external expertise in AI, automation and scientific workflows will be a key factor in successfully navigating this transition.
What does the future look like?
These predictions sketch a compelling future where predictive AI transforms R&D, automation elevates human ingenuity and innovative platforms redefine scientific discovery. Forward-thinking organizations that embrace these trends early will set the agenda for breakthroughs in scientific R&D domains like life sciences, materials science and energy for years to come.
Connect with the Author
Want to learn more about the future of AI in scientific R&D? Connect with our COO, Mike, on LinkedIn.
More > Materials Science & Chemistry Resources
Click here to subscribe for news, resources and events
Related Content
Concurrent Materials Design, Accelerated by AI
The 'acceleration' through Concurrent Materials Design is not incremental; it's transformative.
6 Predictions: How AI Will Transform Scientific R&D In The Next Decade
AI is reshaping every industry, but scientific research and development—drug discovery, materials innovation, specialty chemicals and more—is about to undergo one of the most profound...
Reshaping Materials R&D: Navigating Margin Pressure in the Specialty Chemicals Industry
Since undifferentiated portfolios can no longer deliver required returns for growth, specialty chemical product portfolios and R&D strategies must change.
The Emergence of the AI Co-Scientist
The era of the AI Co-Scientist is here. How is your organization preparing?
Revolutionizing Materials R&D with “AI Supermodels”
Learn how AI Supermodels are allowing for faster, more accurate predictions with far fewer data points.
Understanding Surrogate Models in Scientific R&D
Surrogate models are reshaping R&D by making research faster, more cost-effective, and more sustainable.
R&D Innovation in 2025
As we step into 2025, R&D organizations are bracing for another year of rapid-pace, transformative shifts.
What to Look for in a Technology Partner for R&D
In today’s competitive R&D landscape, selecting the right technology partner is one of the most critical decisions your organization can make.
Digital Transformation vs. Digital Enhancement: A Starting Decision Framework for Technology Initiatives in R&D
Leveraging advanced technology like generative AI through digital transformation (not digital enhancement) is how to get the biggest returns in scientific R&D.
Digital Transformation in Practice
There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.