AI is no longer experimental in biotech—it’s becoming the operational backbone of modern R&D. The companies gaining an edge aren’t just adopting tools—they’re redesigning how discovery works.

Benchling’s 2026 report offers a deeper understanding and insight into the implementation and overall usage in the biotech and pharma industries. AI tools have the capability to uncover patterns that most human researchers are unable to do on their own. Most of these tools have been applied to any stage of the drug discovery pipeline. From target identification and validation, hit-to-lead optimization, absorption, distribution, metabolism, and recruitment strategies. 

Despite the slowdown, teams still rely on these tools, and their impact is clear. In this post, we’ll explore the tools and platforms shaping biotech research and development—and what you can learn from them.

The research shows that apps are making strides in AI-driven literature review with 76% adoption, protein structure prediction at 71%, scientific reporting at 66%, and target identification at 58%, and delivering real impact. The expectation is to reduce costs as AI automates experiments and data workflows, so scientists can focus on critical decisions. 

Let’s take a look at the tools. 

Biotech companies today leverage a variety of AI-driven tools to speed discovery. 

Every month of delay in drug discovery can cost millions. Yet many biotech teams are still slowed down by fragmented data, manual workflows, and underutilized AI.

Benchling’s cloud research platform: One of the main issues in biotech is static, siloed data environments that were normal but now present as the biggest bottlenecks in the industry.  Many data pilots fail due to poor data quality, so building robust data pipelines is essential to unlocking AI’s potential. Benchling turns biotech from experiment-driven to data-driven, making AI actually usable at scale.

Benchling’s AI agents are used to automate data gathering and reporting. Researchers use their deep research agent to pull experimental details across years of notes, cutting days of manual work into minutes. It also integrates prediction models so that individual scientists can run protein structure predictions without needing scripts. 

AlphaFold: Dubbed as the workhorse in biotech, AlphaFold’s models have “revealed millions of intricate 3D protein structures” with the capability to predict a protein’s fold “in minutes”. AlphaFold didn’t just improve protein modeling—it collapsed years of structural biology into minutes. This fundamentally changes how early-stage discovery timelines are planned.

Digital pathology AI: The Boston-based PathAI offers the AI Sight platform and algorithms to automate slide analysis. The digital workflow would save pathologists time by automating routine tasks and analysis, yielding faster, quantitative reads of issue samples. PathAI’s tools deliver standardized, structured data that improves consistency and feeds directly into the R&D pipeline. 

Recursion Pharmaceuticals: They use automated microscopy and deep learning to screen billions of cell images for drug hits. Recursion’s “Cell Painting” platform and AI model reportedly accelerated its pipeline. Recursion turns biology into a high-dimensional data problem, where AI—not human throughput—becomes the limiting factor.

Insilico Medicine uses generative chemistry algorithms to propose new molecules. Reports note that these AI-driven platforms can potentially halve discovery timelines by predicting absorption, distribution, metabolism, excretion, and toxicity (ADMET) and virtual screening scale. Insilico moves drug discovery from trial-and-error to prediction-first design, cutting down wasted cycles and enabling faster progression to viable candidates.

Implementation Strategies and Best Practices

Preparation drives every successful AI adoption—and biotech is no exception. Start by asking the right questions and revisiting the implementation basics. Just as important, understand why this groundwork matters.

Confirm Data Readiness

AI relies on clean, well-structured data. Labs must audit and harmonize decades of experimental records, ELN entries, and assays into standardized formats. Using the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles is a good foundation. Bear in mind that rich metadata is especially critical for IA to make sense of lab results. Using integrated platforms helps break down silos so models can draw on a complete data history. Poor data quality and availability are a top reason for AI pilots’ failure, so addressing this early is key. 

Build Technology Infrastructure

Deploying AI demands scalable computers and secure systems. Many firms are moving research operations to the cloud. However, any AI framework must integrate with ‌existing technology and comply with biotech regulations. In practice, this means encrypted data, validated software for analysis, and reproducible pipelines to satisfy auditors. 

Test Pilot and Iterate

Biotech leaders advise starting with small, high-impact pilots. Good initial use cases are those with repetitive, well-defined tasks. From automating literature reviews, extracting structured data from CRO reports, or performing simple analyses. The key is to choose low-risk projects that free scientists from tedious work and can be measured for time saved or error reduction. Every pilot should define clear success metrics and capture lessons learned. Document both the wins and the failures and use the failure analysis as valuable insights for scaling. 

Engage cross-functional teams and change management

Companies say 67% of their AI talent comes from upskilling current employees—not hiring externally. The most successful organizations build interdisciplinary sprint teams that co-develop AI solutions. They also involve end users from the beginning instead of forcing a tool on them. This approach builds trust and ensures the solution matches real workflows.

Clear communication is essential. Set expectations for what each AI tool can and can’t do, and reinforce that it’s a co-pilot designed to speed up work—not a surveillance tool.

Assess governance and iterative improvement 

As pilot programs prove successful, scale by integrating tools into broader workflows. Use agile sprints, create monitoring dashboards, and enforce strict version control. Industry reports show that leading organizations follow a simple rule: build what sets you apart, and buy what scales.

Business and Operational Impact

Pulling from Benchling’s report, 50% of biotech companies reported faster time -to-target, and 56% expected cost reduction within two years as AI scales up.  The report cites quick-win use cases like literature search, data summarization, and routine reporting, which have already boosted productivity. AI is poised to shave a year off discovery. The efficiency gains translate into major cost savings, as each month of savings saves millions in carrying costs. For example, Recursion used its AI platform to advance a cancer drug candidate from concept to clinical trials in just 18 months, which is about half the industry norm of 42 months. 

AI allows scientists to focus on innovation. The ISPE white paper observes that collaborative AI frees experts to spend more time improving processes instead of data wrangling. Over time, firms expect that these productivity gains should lower unit research costs. Similarly, in manufacturing and scaling, AI-driven process optimization promises better yields and reduces waste. While the exact ROI numbers will vary, the trend is clear: strategic AI adoption in biotech has started to deliver measurable business impact on speed, quality, and cost of research and development.

Regulatory and Compliance Considerations

Companies must ensure data integrity, traceability, and validation at each stage of their process when deploying AI in research and development. Any AI system that influences experimental conclusions needs documented lineage and validation protocols similar to other lab instruments. Biotech forms should monitor the FDA’s good AI practice principles, which cover human oversight, context of use, and lifecycle management. 

In addition, any patient or proprietary data used for training must comply with laws like GDPR or HIPAA. Clear agreements on data sharing and ownership are also needed for collaborative initiatives. 

Cultural and Organizational Changes

Experience shows that people embrace AI faster when they see tangible benefits and understand the technology’s limits. It helps to position AI as a partner that accelerates work rather than a tracking system. Leaders must foster trust and AI literacy for the culture to shift. To do that, users must be involved from the start, with training and adding new skill sets, moving into a more agile mindset. At some point, governance will evolve, with AI steering committees or task forces to oversee strategy, data governance, and tool selection. 

What’s Next For You?

As artificial intelligence shifts from a promising idea to a practical research partner, biotech organizations that adopt it thoughtfully can gain efficiency, deeper insights, and a stronger competitive edge. No matter your focus, you can use AI tools to strengthen nearly every part of your business. Studying how larger biotech companies apply AI is a smart place to start—but every rollout needs a clear strategy.

Creating an AI-ready culture starts with sharing data openly, testing new ideas, and shifting the focus from using technology to building new capabilities together. Where could this take root in your company? 

Whether your team is just beginning its AI journey or looking to scale successful pilots, we can help you map this out. WDB’s long-standing work with biotech and pharma companies helps us understand where help might be needed. Taking the next step means partnering with a team dedicated to helping you move from indecision to practical solutions. 

Are you ready to unlock the full potential of your biotech team? Explore WDB’s AI consulting services for biotech companies and book a consultation to start building your AI roadmap today. Schedule your discovery sprint.

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