Benchling report reveals diverging AI strategies between large and small biopharma

Benchling’s second annual State of Tech in Biopharma report is based on insight from 300 R&D and IT leaders across the U.S. and Europe at large and small biopharma companies (1,000+ and <1,000 employees, respectively). The report explores how technology adoption and AI readiness are unfolding in the industry.

Large biopharma bullish on AI, small companies face hurdles

AI’s full impact on drug discovery and the R&D lifecycle is still emerging, but large biopharma isn’t taking a wait-and-see approach. In this report, respondents at large companies adopt AI/ML at nearly three times the rate of small biopharma (67% vs. 23%) and rank it as their second-highest investment priority over the next three years, just behind R&D data platforms. Three-quarters of respondents at large biopharma companies expect AI/ML to significantly speed up their timelines to key milestones over the next 12–24 months. These companies benefit from strong support at the senior leadership level, with half of respondents reporting top-down initiatives to drive AI/ML adoption, compared to just 28% in small companies. Without this executive push, small biopharma firms are taking a more limited approach, leaning into AI/ML most prominently with pilots or proof-of-concepts.

Diverging paths in R&D modernisation

To meet modern R&D demands, biopharma is reshaping its tech strategy with key enabling technologies such as R&D data platforms, robotics and automation, AI/ML, connected lab instruments, and SaaS-based scientific applications.

Respondents at large biopharma, operating on a vast scale, lead in their adoption of enabling technologies, using robotics and automation at twice the rate and AI/ML nearly three times as those at small companies. At the same time, scale is a double-edged sword: it brings resources but can also weigh down change. Large companies have developed a vast network of internal software and systems to manage their breadth of specialised science, complex data handling, and computational needs, with 43% of IT at large biopharma supporting more than 20 scientific software applications, many of which are custom-built. Supporting this varied scientific software landscape is an ongoing and resource-intensive effort.

Meanwhile, small biopharma are focusing on foundational tech, adopting R&D data platforms (89% respondents citing) over AI/ML (23%) and robotics and automation (27%), citing ROI concerns as the primary barrier to adoption. At their stage of drug development, small company respondents state quality improvements and error reduction as the top R&D outcome expected from enabling technologies (69% citing high or significant impact on quality in next 12-24 months).

Across both large and small biopharma, low connectivity with lab instruments and software hampers data management. Instrument connectivity and cloud-based (SaaS) adoption remain low in biopharma: Only 37% of respondents at small companies report that more than 60% of lab instruments across their R&D orgs have automated data capture, and just 23% of small and 17% of large company respondents have adopted cloud-based scientific software or SaaS. Addressing these gaps is essential to managing the volume of data in today’s labs and making data findable, accessible, interoperable, and reproducible (FAIR) — all key for scaling AI.

Addressing skeptics and believers in technology adoption

In the journey with tech adoption, understanding “skeptics” and “believers” is key. Skeptics remain unconvinced of the need for new tech, while believers consider it impactful and prioritise investment, despite not fully adopting it.

For SaaS and connected labs, non-adopters are more than five times as likely to fall into the believers camp, signaling a “when, not if” stance for future adoption. AI/ML, meanwhile, shows a balanced split between skeptics and believers, with companies continuing to weigh its ROI and long-term impact. R&D data platforms already show high adoption across biopharma, with few remaining holdouts. Robotics and automation, however, present a wider gap; half of small biopharma non-adopters are skeptics, reflecting concerns over these technologies’ suitability in specific R&D workflows. Identifying these different groups early can help companies tailor strategies for adoption, change management, and enablement.

Tackling gaps in AI readiness in biopharma

Biopharma companies are building AI capabilities across three levels: foundational, scale-up, and advanced, with significant differences in readiness between large and small organisations. Only 25% of large and 9% of small company respondents report preparedness for a foundational level of AI across critical dimensions of talent, centralised data, high-throughput automation, and compute. When it comes to reaching advanced readiness, where continuous AI feedback loops can accelerate discovery, just 14% of large and 3% of small company respondents report preparedness. For large biopharma, the biggest gap is a connected wet and dry lab (just 41% citing preparedness).

Access to skilled talent remains a major bottleneck in biopharma’s AI journey. This foundational requirement is the second biggest gap for large biopharma, with less than half (46%) citing readiness, and affects small biopharma disproportionately, with just 17% of those at small companies citing preparedness around hiring the right AI talent.

Differing investment priorities: Large biopharma’s need for speed versus small biopharma’s focus on quality

Tech investment priorities differ across biopharma — smaller companies emphasise quality, viewing the reduction of human error through tech adoption as a top advantage. Under constant pressure to compress timelines, large biopharma see accelerating time to milestone as the top R&D outcome expected from fully adopting enabling technologies (72% citing high or significant impact on speed in next 12-24 months).

As biopharma looks to the future, investment is shifting toward technologies that support dry lab environments. For example, robotics and automation are rated highly impactful by large biopharma — improving speed, success, and scalability — but respondents at both large and small companies rank it as a lower investment priority due in part to high capital requirements. SaaS is the second highest priority investment for small biopharma in the next three years, showing strong potential to boost scalability and meet demands for flexible, data-driven workflows that bridge wet and dry lab environments. Although AI/ML’s full impact is still unfolding, large companies plan to prioritise AI/ML, alongside R&D data platforms, as the top two investment priorities.