When companies discuss adopting AI, they often start with the most specialized, mission-critical step: R&D teams want AI to assist with design, while production teams want AI to predict yield. Yet a closer analysis of working hours often reveals that a substantial amount of labor is spent on documents surrounding the core work, such as batch records, inspection reports, and regulatory submissions. These tasks require specialized knowledge but are also highly repetitive, and they are often handled through page-by-page reviews by the most experienced employees.
On October 27, 2026, Anthropic released preliminary results from its collaborations with six drug development organizations. The example most relevant to manufacturers in other industries involved drug developer Enveda using Claude to review manufacturing process documents. The company reduced more than 25 hours of manual review to under 3 hours, while catching every issue identified by human reviewers. Using this case as a starting point, this article explains how companies can identify similar work in their own processes and which decisions should remain with people during implementation.
Drug Development Bottlenecks Are Distributed Across the Entire Process
When people think about AI in drug development, the most familiar application is using AI to design new molecules. In its report, Anthropic noted that developing a new drug involves many different types of work, including identifying the target a drug should act on, designing molecules and refining their properties, developing a stable and scalable manufacturing process, preparing regulatory documents for clinical trial applications, and designing and conducting clinical trials. Time-consuming bottlenecks are spread across every stage. Solving any single problem is not enough to accelerate the entire process. Anthropic therefore chose to participate directly in real-world drug development projects and identify bottlenecks at each stage.
The cases in the report also cover different stages of development. Scientists at Generate Biomedicines used Claude to propose new designs, including one version that was approximately 70% shorter than the standard version yet performed better in laboratory testing. Divergence Labs had Claude generate approximately 35,000 candidate designs in about one month, after which the team screened and tested them. BigHat Biosciences connected Claude to its laboratory data systems to generate recommendations for modifications, producing and testing approximately 1,010 versions over about 10 weeks. Bristol Myers Squibb (BMS) used Claude to evaluate clinical trial plans for early-stage drugs and estimate their probability of success. Anthropic also emphasized that most of these projects remain in the preclinical stage—the research phase before a drug enters human trials—and still have a long way to go before reaching the market.
Molecular design and screening require extensive biological expertise, making them difficult for traditional manufacturers to replicate directly. Manufacturing process document review is different: nearly every manufacturer performs work of a similar nature.
How Enveda Uses AI to Review Manufacturing Process Documents
Before a new drug can enter clinical trials, a company must provide records demonstrating that every batch was manufactured according to strict quality standards. It must also submit an application to the relevant regulatory authority. Enveda’s process chemists spent as much as three-quarters of their working time handling these documents.
To reduce this burden, Enveda built a system that connected all its manufacturing process documents to Claude, allowing it to flag potential issues. Enveda tested the system on 1,061 pages of documents. Claude caught all 11 issues identified through manual review and flagged seven additional issues that human reviewers had missed. Manual review time fell by 89%, from more than 25 hours to under 3 hours. Claude also drafted two important regulatory documents. One draft was completed in four hours, compared with approximately three months of manual work. The other went through Enveda’s standard human review process, which found only one minor error.
This case offers two practices that companies can apply directly when adopting AI.
Validate AI against issues already identified by human reviewers. Enveda already had the results of its manual review, allowing it to compare how many issues Claude caught and which additional issues it identified. This gave management a clear basis for evaluating AI performance instead of relying solely on users’ impressions.
Keep people in the process. Review time was reduced substantially because Claude first flagged potential issues, allowing reviewers to focus their efforts on verification and judgment. Draft regulatory documents likewise continued to undergo the standard human review process.
Let AI Handle High-Volume Work While Professionals Retain Critical Decisions
The BigHat case illustrates the division of responsibilities between people and AI even more clearly. Midway through the project, the team suspected that commonly used indicators for thermal stability and aggregation—the tendency of molecules to stick together and form clumps—could lead to misleading conclusions. Claude and the researchers therefore returned to the raw data, reanalyzed it, and proposed alternative indicators and adjustments to the testing procedures. BigHat’s scientists then refined and implemented the proposed approach, and its main predictions were validated. AI handled the data review and hypothesis generation, but professionals familiar with the experiments still decided whether and how to modify the process.
Another case involved administrative coordination outside R&D. Mithril Medicine began as a one-founder company researching treatments for two rare pediatric muscular dystrophies. The founder used Claude to coordinate contracts, logistics, and experiments across 11 external laboratories, while also delegating many operational tasks to Claude. In less than 18 months, one treatment advanced from an initial concept to preliminary results in mouse studies. Anthropic later acquired the company. In resource-constrained teams, coordinating contracts, tracking progress, and communicating across organizations can consume more time than the specialized work itself. This type of work is also well suited for initial organization by an AI Agent.
Identify Similar Document-Intensive Work in Your Company
Traditional manufacturing processes include many tasks similar to Enveda’s manufacturing process document review. Examples include quality assurance teams checking batch records against inspection reports, sales teams assembling documents requested during customer audits, chemical plants updating Safety Data Sheets (SDS)—documents that describe chemical hazards and handling procedures—and R&D teams comparing drawings and specifications before issuing design change notices. Four criteria can help determine which task should be prioritized for AI adoption: a large volume of documents, clearly defined review rules, a high cost of errors, and a current process that depends on experienced personnel reviewing documents page by page. Tasks that meet all four criteria generally deliver the fastest visible results.
Once a task has been selected, companies should begin with the following three steps.
Establish the current time requirement. Record how long it takes to review a document from receipt to completion, who performs the review, and the most common reasons documents are returned. This creates a baseline for future comparisons. Enveda could report an 89% reduction in review time because it first established that the existing process took more than 25 hours.
Test with historical documents that have known answers. Select a set of documents that have already undergone human review and for which all identified issues have been recorded. Have an AI Agent review them again, then compare which issues it missed and which additional issues it flagged. Additional findings should also be verified by a specialist to determine whether human reviewers genuinely overlooked them or the AI produced a false positive.
Retain human approval. After deployment, the AI Agent can flag issues and prepare drafts, but qualified personnel should continue to make decisions about approval, submission, and external publication. With EgentWrX, companies can upload their policies, checklists, and historical review records to a knowledge base, enabling an AI Agent to review documents according to the company’s own standards. Repetitive review tasks can then be configured as tasks and connected into workflows, with mandatory human approval required before handoff. When the workflow reaches that stage, it pauses and proceeds only after a person approves it.
FAQ
Can AI document review replace existing reviewers?
This is not recommended. Enveda’s approach uses AI to flag potential issues first, after which reviewers verify the findings and make the necessary judgments. This significantly reduces review time. Draft regulatory documents likewise continue to undergo human review. AI saves the time previously spent searching through documents page by page, but final judgment and accountability remain with people.
How can a company verify that AI reviews are accurate enough?
Test the system with historical documents that have already undergone human review. Enveda used the 11 issues identified by human reviewers as a benchmark, confirmed that Claude caught all of them, and then examined each of the seven additional issues it flagged. Companies can follow the same approach and build a body of comparative results before deployment.
Are drug development cases relevant to companies outside the pharmaceutical industry?
Yes. Molecular design requires highly specialized expertise and is difficult to apply directly elsewhere. However, manufacturers commonly work with batch records, quality documents, and regulatory submissions. The key is the nature of the work. Review tasks involving large document volumes, clear rules, and a high cost of errors can all benefit from Enveda’s approach.
Have these results proven that AI can bring new drugs to market faster?
Not yet. Anthropic stated that most of these research projects remain in the preclinical stage and have not entered human trials. The figures cited in this article reflect results achieved by specific partners under specific conditions. Companies can learn from their methods, but they must validate performance again using their own data.
References
- Early progress on accelerating drug development with Claude — Anthropic’s official research article describing preliminary results from its collaborations with six drug development organizations.
- Enveda Technical Report on Manufacturing Process and Regulatory Document Processing — Enveda’s official technical report describing its manufacturing process document review system and test results.
- BigHat Biosciences and Anthropic Collaboration Report — BigHat’s official technical report describing the candidate drug optimization process and the reanalysis of evaluation metrics.
