Estimating the economic potential of using AI to accelerate R&D

Our research finds that AI could substantially accelerate R&D processes across a set of industries that make up 80 percent of large corporate R&D expenditures. For industries whose products consist of intellectual property (IP) or whose R&D processes are closest to scientific discovery, the rate of innovation could potentially be doubled. For industries that produce complex manufactured products, R&D processes could be accelerated by 20 to 80 percent, depending on the industry (Exhibit 4). Overall, our analysis estimates that the potential annual economic value that could be unlocked by using AI to accelerate R&D innovation is about $360 billion to $560 billion. Next, we examine how this value capture could potentially play out across a range of different industry sectors.

IP product industries

Computer gaming and software are industries in which products fundamentally consist of IP. With no need for physical prototyping or manufacturing, all the AI-driven R&D acceleration levers can be directly applied. Furthermore, the application of gen AI for developing software and creating visual content are among the most mature use cases for that technology, which could lead to a potential doubling of R&D throughput, or even more. The bulk of the impact of AI in these industries stems from accelerating the process of generating designs—in this case, computer code and game visualizations.

The generation of design candidates in these industries is particularly well matched to the most advanced capabilities of gen AI models. Frontier gen AI foundation models have been advancing rapidly in their ability to generate computer code. At the time of writing, the CEOs of Google and Microsoft have both estimated that 30 percent of the new code produced at their companies was written by AI.

That said, while these generative-software-development tools are now widely available, the actual acceleration we have observed in many companies has been modest so far (although some start-ups have reported truly remarkable levels of software development productivity). This suggests that unlocking the full potential of AI will require a set of complementary organizational changes, above and beyond the technology itself—a theme to which we will return. But if these tools and the complementary organizational changes are put in place, we estimate that the software industry could double the rate at which it produces new products.

Another significant portion of the work in developing computer games, particularly those that are immersive, is the design and rendering of the virtual worlds in which game play takes place. Some of the earliest applications of gen AI, even before LLMs became widely available, consisted of being able to generate images. The rapidly evolving capability to generate visual content can directly accelerate the content creation process for computer games. Although the gaming industry is small, this illustrates that combining the ability to generate software and create content could increase its output by 150 percent.

Science-based product industries

Another set of industries where AI can accelerate innovation are those where the product development process is very close to scientific discovery, including industries like pharmaceuticals, chemicals, and alloys, composites, and building materials.¹¹

Leading companies in the pharmaceutical industry have already been deploying AI in their R&D processes. These companies are training, adapting, and customizing foundation models for omics-based target identification (determining what molecular processes in a disease could be modulated to mitigate its effects) and in silico molecule design of drug candidates. AI surrogate models are also being used for in silico screening, molecular optimization based on structure and property predictions, and potentially preclinical analyses of pharmacokinetics (what a body does to a drug) and pharmacodynamics (what a drug does to a body). Other applications of AI in the drug discovery process include the ability to mine the extensive databases and literature in the field and to apply techniques such as computer vision to enable high-throughput experimental screening.

AI also has the potential to reduce the total cost of drug discovery and increase the probability of success of candidates that reach the clinical trial stage—that is, the likelihood that a candidate will be approved through the clinical trial process. (While we estimate the relative impact of generating new candidates on the overall timeline of drug discovery to be relatively small—about 5 percent—generating higher-quality candidates could improve the probability of success.)

It is important to note that the actual potential for increasing the number of drug candidates that become approved therapies is constrained by the clinical trial process, which has its own challenges (including cost, patient recruitment, and clinical and regulatory capacity). While AI could potentially accelerate this process, we only examined the drug discovery process within the scope of this study.

The potential to accelerate the R&D process is also high in industries that produce materials used as inputs into other industries, such as chemicals and alloys, composites, and building materials. AI surrogate simulations—for example, for physicochemical modeling—can be used for property prediction and analysis in that it helps to predict properties such as structure, strength, toughness, ductility, permeability, conductivity and resistance, and corrosion, depending on the type of material and its intended application. These techniques can also be used to optimize the processes for synthesizing/manufacturing these materials. As in pharmaceuticals, LLMs can be used for market analysis and to mine scientific literature and databases during the initial conceptualization and specification phase. And in all these industries in which some degree of physical experimentation is still required (in addition to in silico simulations), there is a potential for agentic AI to automate the process of managing experiments, though this capability remains nascent.

Overall, the top end of the ranges of throughput acceleration ranges from 75 percent for chemicals R&D to more than 100 percent for pharmaceutical discovery.

Complex manufactured product industries requiring multidisciplinary engineering

There is a wide swath of industries in which the product design process requires a variety of engineering disciplines (for example, electronics, industrials, medical technology, semiconductors, automotive, and commercial aerospace). Designing a commercial aircraft or an automobile, for example, requires engineers who specialize in aerodynamics, as well as in areas such as structural dynamics, propulsion, and electrical systems, among other disciplines. In electronics, product development requires not only an understanding of electrical and electronic circuits (often including the intended and unintended effects of electromagnetic radiation) but also the ability to predict and manage the thermal properties of a product. And as software is increasingly embedded in and becoming a larger part of the value delivered by physical products, software engineering is becoming a critical capability.

Across these disciplines, AI-powered generative-design systems can create a set of design candidates, often more quickly and from a wider search space than would be considered without these techniques (akin to AlphaGo’s Move 37). Multiphysics AI-style deep learning surrogate models (those that incorporate multiple modalities of analysis, such as structural, fluid dynamics, thermal, and electromagnetic) can be used to predict the performance characteristics of design candidates more quickly than other numerical simulation methods (finite element analysis, computational fluid dynamics, and electromagnetic modeling). As engineering organizations develop an understanding of the relative strengths and weaknesses for each of these methods, they can better allocate their simulation and testing efforts across traditional numerical simulations, deep-learning surrogates, and physical prototypes.

In some of these industries, meeting the documentation and reporting requirements is critical, especially those in which safety and regulatory considerations predominate, such as aerospace, auto, and medical technology. LLMs can assist in meeting those requirements in a timely and efficient way, provided their outputs can be appropriately validated. As in other industries, LLMs and predictive machine learning can also assist in the initial concept development phases with market research and crafting product specifications.

Overall, while these industries share many characteristics (such as complex supply chains and integrated materials and electrical and software designs to create finished products), they also represent a wide variety of different products and markets. This is reflected in our estimates of potential impacts of AI accelerating R&D. In electronics, for example, the pace could nearly double, while in commercial aerospace the potential impact is 25 percent. One shared area of potential impact across all industries requiring multidisciplinary engineering is in the process known as verification (did I build the system right?) and validation (did I build the right system?). Some form of verification and validation occurs in all these industries, accounting for as much as half of the R&D timeline. The potential to transition from physical prototyping and testing to in silico testing in verification and validation could be one of the largest potential levers for accelerating the entire innovation process (though this is sometimes gated by regulatory requirements).

Consumer goods

The applications of AI for accelerating R&D in consumer goods (such as food and beverages and personal-care and household goods) parallel those for analyzing market trends and generating design candidates. LLMs and analytical AI can be used to generate and synthesize data-driven insights to provide direction for new-product development. These levers are becoming increasingly valuable as the quantity and variety of digital data about consumers continues to grow. Specialized foundation models, for example, can generate candidate recipes for food and beverage, candidate formulations for cosmetics, and candidate designs for other product categories such as apparel and household goods.

The potential for using AI surrogates for modeling consumer preferences (versus actual consumer testing) lies largely in the ability to create “digital twins” of consumers. Our estimates for the current potential of using AI surrogates (in place of consumer testing) to accelerate R&D in consumer product industries are conservative, though the figures likely will increase. About three-quarters of the AI impact we estimated for these industries comes from the generation of new-product candidates.