Artificial intelligence is being woven into multiple stages of clinical development and regulatory activity across the pharmaceutical sector, and that adoption could materially improve profitability for major drugmakers, according to Bernstein.
The research firm reported that AI tools are now applied to trial design, patient recruitment and the automation of regulatory submissions. These applications are helping companies tighten protocols, find and enroll patients more quickly and cut the time spent preparing regulatory documentation. Collectively, Bernstein estimates these gains could shorten drug development timelines by roughly 18 months and reduce research and development spending by about 5% over the next several years.
Developing a new medicine traditionally takes well over a decade and requires heavy upfront investment before a product reaches the market. A large share of the time and cost burden falls in the clinical trial and regulatory phases, where trial enrollment, data management and regulatory filings drive complexity and expense.
Bernstein highlighted several concrete ways AI is applied in these phases. Machine analysis of historical trial data can inform protocol improvements, while algorithms can support better selection of clinical sites and more effective monitoring of patients during trials. These changes can cut delays, reduce the need for costly protocol amendments and accelerate the preparation of regulatory submissions.
The firm argues that faster development not only reduces costs but also extends the effective revenue window for new drugs by bringing products to market earlier - ahead of patent expiry and the onset of generic competition. When combined with lower R&D expenditures, earlier launch timing could drive a meaningful gain in operating profit for large pharmaceutical companies, Bernstein said.
Bernstein expects that the largest, globally integrated pharmaceutical companies will be the primary beneficiaries. Those organizations typically possess the scale, accumulated datasets and infrastructure needed to deploy AI tools effectively across international clinical programs. The research note cited specific examples of companies it sees as well positioned to capture these efficiencies, naming Daiichi Sankyo, Takeda and Astellas.
Despite the productivity upside, Bernstein cautioned that AI is unlikely to overturn the industry’s fundamental economics. Drug development will remain capital-intensive and subject to strict regulation, the firm said, meaning the sector’s business model is not expected to change in a structural way even as AI raises productivity.
Implications
For investors and market participants, the combination of shortened timelines and modest cuts to R&D spending implies a potential lift to profitability for major pharmaceutical firms, particularly those with global clinical development capabilities and deep data assets.