The cost of waiting: why delaying AI adoption is a strategic risk for R&D

While many in science-led organizations are cautiously observing the AI boom, early adopters are already accelerating innovation and discovery cycles, trimming costs and shortening time-to-market. In science-driven sectors, where experimentation is time- and resource-intensive, the competitive advantage now belongs to those who act early and responsibly.

In biopharma alone, GenAI is projected to unlock $4–$7 billion in annual value through cost reductions, productivity gains and improved quality. It’s also been shown to cut time-to-market for new compounds and products by as much as 20%. In fast-moving, research-intensive industries, delaying GenAI adoption doesn’t just slow progress – it risks higher costs, longer development cycles and missed opportunities to secure valuable intellectual property.

Below, we explore the strategic risks of waiting too long and examine five scenarios where GenAI is already reshaping workflows, accelerating decision making and driving greater business value.

Drug discovery: Stopping literature overload from delaying early-stage target evaluation. Drug discovery teams spend weeks manually reviewing hundreds of publications to assess target novelty and competitive landscape. Using GenAI-enabled search tools enables reviews to be completed in days, moves ahead with lead selection and secures IP earlier.

Specialty chemicals: Helping R&D to avoid getting stuck in trial-and-error testing. Manual formulation testing adds time and money to DMTA cycles. GenAI-guided simulation models allow researchers to prioritize the most promising compound combinations before lab testing to reduce iteration cycles and reach commercial viability faster.

Regulatory affairs: Reducing delays caused by manual reviews of legacy precedents. Preparing regulatory filings requires access to prior FDA/EMA decisions often buried in legacy documents. GenAI can extract relevant precedents via natural language search and complete filings with fewer rework cycles to accelerate compliance readiness.

Materials science: Eliminating cross-disciplinary insight bottlenecks. Developing a new polymer needs data from both chemistry and mechanical engineering literature, which is often siloed. GenAI can support cross-domain literature synthesis to identify key material properties and trade-offs early on to shift direction as needed.

Technology and energy: Modelling and predicting performance to reduce time-to-feasibility. Battery and semiconductor R&D is often slowed by the need to manually test material properties like conductivity and thermal stability. GenAI can rapidly model these performance indicators to identify viable material combinations earlier and reduce time-to-feasibility.

Checklist: Getting started with AI

To realize these kinds of benefits, R&D organizations need to choose the right tools. Here are the questions R&D leaders must ask to be confident that GenAI tools align with their research goals, data environment and risk tolerance.

Strategic fit

• Does this solution directly address a defined research challenge or business priority?

• Have we clearly articulated what success looks like scientifically and operationally?

• Will this solution integrate into existing research workflows, systems or platforms?

Data and model foundations

• Is the AI trained on data that is high-quality, curated, relevant and appropriate for our domain?

• Can we trace and verify the sources of the model’s outputs?

• Are there safeguards to prevent the model from using sensitive or proprietary information for training?

Governance, ethics and compliance

• Are the model's outputs explainable, auditable and reproducible?

• Does the solution comply with applicable regulatory, IP and data privacy frameworks?

• Are Responsible AI principles (fairness, accountability, transparency, human oversight) embedded?

Security and risk mitigation

• Does the solution protect against prompt poisoning, model leakage or unauthorized access?

• Are there clear controls around data access, versioning and user permissions?

• Have we evaluated how the system will behave under adversarial or unexpected inputs?

Scalability and adaptability

• Can the solution scale with our research portfolio or team structure?

• Is the model regularly maintained, updated and benchmarked?

• Will it evolve as our needs, data sources or scientific priorities change?

What R&D leaders are saying

For those adopting AI, it has been an ongoing process to identify where the technology has the potential to unleash real, measurable value. And it’s clear that the real impact of AI is going to continue to change over time.

“We shouldn’t be afraid of AI,” says Abhishek Roy, global R&D leader of AI at Cargill. “Be tinkerers. Be open about how it’s used. And never forget the ethics that underpin good science – they still matter.”

For others, it is not just about finding new efficiencies but empowering their workforce to be more impactful than ever before.

“AI makes complex things easier to do,” says Dr. Alexei Lapkin, director of Chemical Data Intelligence (CDI) and professor of sustainable reaction engineering at the University of Cambridge. “It’s not about replacing jobs but enhancing capabilities. With the right tools, you can turn an average engineer into an excellent one. And while AI can bring about this democratization of science and technology, we also need to learn how to use these tools properly, understand what they can and cannot do, what we can trust, and what requires verification. In this way, AI is changing the practice of science and engineering; it’s not changing the essence of these fields.”

Ultimately, R&D is about making the right decisions at the right times, and that is something that AI is well-positioned to support. It will not do the work for you, but instead help your teams make the right choices, and ultimately bring new innovations to market faster than before.

As Brice Hoffman, principal scientist and head of computational chemistry at Iktos, says, “AI is not magic. It’s a tool that will help you decide what to make — and that’s already good enough.”

Make Elsevier your partner for progress

Even as AI’s role in R&D has grown, the reality remains that most 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.

Discover more about keeping a competitive edge through AI.

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