Axendia’s Event Brief of Benchling Development Summit
The 2025 Benchling Development Summit, held in June 2025, brought together over 40 senior biopharma leaders at the company’s Boston headquarters for a closed-door conversation on the future of drug development. Against the backdrop of rising pressure to accelerate clinical timelines and translate scientific breakthroughs into real-world therapies, the summit served as both a mirror and a map – reflecting industry-wide challenges while showcasing actionable pathways forward.
Hosted by Benchling, a provider of cloud-based software for life sciences R&D, the event centered on one bold question: How can we digitally transform the lab to accelerate innovation, improve compliance, and empower scientists in a rapidly evolving R&D landscape? With sessions covering AI, data infrastructure and cultural transformation, attendees explored the technologies and mindsets necessary to reimagine pre-clinical and clinical development in an era of convergence.
Key Finding & Themes
Integrated systems are enabling end-to-end development
Culture and legacy systems remain the biggest barriers
Lab systems must evolve from record-keeping to insight generation
Trust is key to AI adoption in regulated labs
Merck’s modernization shows what’s possible
Biopharma development needs shared data language
Governance must guide, not hinder, technology adoption
Bringing Joy Back to the Lab
Roy Helmy, Associate Vice President, Regulated Bioanalytics at Merck Research Laboratories, offered a candid and compelling look at Merck’s vaccine modernization journey. Benchling and Merck recently entered into a collaboration with a focus on three key areas:
- Enhancing speed and throughput with automated data capture and integration
- Streamlining with a unified platform for managing workflows and data across the bioanalytical lifecycle
- Maintaining rigorous quality and compliance standards in a regulated bioanalytical setting
Helmy stated that Merck’s transformation was prompted by the company’s initiative to modernize their vaccine development program. Previously, Merck relied heavily on spreadsheets, manual testing and paper-based calculations. “We were using flip phone technology when what we needed was a smartphone,” Helmy remarked. The opportunity was holistic. Disparate systems, siloed teams and tedious, error-prone processes were symptomatic of a broader issue: an immature digital infrastructure that couldn’t keep pace with the demands of modern science or a global health crisis.
Helmy and his team advocated that the final leg of vaccine development – the bioanalytical lab – was a critical bottleneck. As the “last mile” for patient samples in clinical trials, the bioanalysis function must operate with speed and accuracy, while continuing to fulfill all standards for data integrity and compliance. In response, Merck’s management launched an ambitious modernization effort, with Benchling’s partnership a critical component.
This effort was not just about technology – it was about reclaiming the purpose and satisfaction of scientific work. “We want to bring joy back to the lab,” Helmy emphasized. Scientists were spending too much time ferrying data between systems, writing calculations by hand and performing rote tasks. They wanted to focus on what matters most: building better methods, conducting real science and making decisions based on timely insights.
Facets of the transformation are now well underway:
- Automated sample management and tracking
- Integrated robotics for assay execution
- End-to-end data management, including reagent tracking, assay setup and study design – all delivered through the Benchling Validated Cloud
- Security and data layers integrated with Merck’s API infrastructure
- Improved user experience through role-specific workflows and intuitive interfaces
With over 1.1 million clinical samples supported and more than six legacy systems phased out, Merck’s vaccine bioanalysis team is realizing measurable gains: a 10x efficiency boost, a 25% reduction in tedious tasks and a clear path to full lab automation. These outcomes are not just KPIs – they are cultural shifts, signaling a renewed alignment between scientific curiosity and digital capability.
Helmy’s story offered a powerful reminder: digital transformation in life sciences is not about replacing scientists – it’s about empowering them.
The Acronym Evolution for Laboratory Systems
The roundtable on regulated labs explored the shortcomings of traditional LIMS and ELNs. While these systems are foundational, the consensus was clear: they must evolve from transactional and siloed systems of record into fully integrated platforms that serve as a central nervous system for the lab.
Key themes included:
- Tailored configurations with low code / no code remains essential for successful deployment and user adoption – standard software cannot serve all organizational needs out of the box, especially for a platform shared by many labs.
- The value of a laboratory system shared between research and regulated labs is centralized data powered by a scientist-focused user experience. Modern software capable of adapting to the activity type and data security needs should replace the historical perspective of systems separated for regulatory requirements.
- A new LIMS or ELN will not drive a modernization transformation within a lab or organization. Instead, a cultural transformation is required that is focused on how the data supports the business outcomes. This will both drive system selection and implementation to enable insight capture at every scientific step.
In line with what Axendia has been hearing from the industry, participants at the Benchling Summit advocated for a curated, streamlined lab experience—one that maximizes contextualized data, reigns in tool sprawl and removes barriers between data producers and consumers.
Building Trust in AI
The promise of AI and large language models in regulated labs is clear, but trust must be earned.
Attendees agreed that the best near-term applications are in internal, non-regulated decision-making contexts. This low-risk exposure builds confidence in the technology’s reliability and outputs.
Three promising use case emerged:
- Querying data through natural language interfaces
- Suggesting relevant historical experiments during protocol creation
- Generating summaries and consolidating large datasets
There was resounding agreement that transparency is essential. AI outputs must include traceable links to original data sources. Without persistent lineage, confidence in AI-generated content — and regulatory acceptance — will falter.
Advancing Process Development: Connected Systems and Practical AI
Discussions on Process Development (PD) and CMC emphasized the need for a unified digital platform spanning Research, Development and Manufacturing. While the benefits — such as streamlined tech transfer, data integrity and improved collaboration — are well understood, the lack of a consistent data ontology remains a major obstacle.
Participants agreed that efforts to standardize ontology remain fragmented. Suggestions included pivoting the Allotrope Foundation toward this effort or launching a time-bound open-source initiative. Any successful ontology must support translation rather than impose rigidity, evolve through high-value use cases and include governance that adapts over time.
In parallel, interest in AI and ML is growing, but participants emphasized moving from theoretical discussions to tangible applications, such as automated reporting, embedded help-bots, schema-aware forecasting tools and adaptive templates. These tools can alleviate training burdens and increase efficiency, but they require data lineage clarity and trusted governance.
Early wins in PD are expected in narrow AI applications like contextualization and search. Participants envision a near future where scientists work in labs with intelligent, automated systems that capture data in the background, enabling them to focus fully on science, rather than on data entry.
Balancing Risk Aversion with the Adoption of Emerging Technologies
The event made clear that transitioning to modern, integrated lab systems is not constrained by regulation, but more so by legacy thinking.
Separate systems for research and regulated workflows have traditionally been deployed to ensure compliance and data integrity and without slowing down non-regulated teams. However, the demand for new technology platforms to allow unified lab execution systems that meet ALCOA+ (Attributable, Legible, Contemporaneous, Original, Accurate + Complete, Consistent, Enduring, Available) and FAIR (Findable, Accessible, Interoperable, Reusable) principles continues to grow.
Key success factors discussed:
- Clear QMS frameworks, including risk-based decision-making
- Transparent data access with appropriate controls
- Tailored user experiences that encourage, rather than enforce, adoption
- Providing the same user experience to end users and only enforcing regulatory requirements where required
The conversation on data security mirrored this sentiment. While IT teams are eager to adopt new technologies, there is hesitation due to security and regulatory uncertainty. As one participant put it, the industry is “building the airplane while it’s flying.”
Still, the path forward is clear: responsible, phased adoption of digital tools, grounded in strong governance, will enable faster, safer and more collaborative scientific progress – without having to rip and replace legacy systems.
In Brief
The Benchling Development Summit showcased the industry’s shared desire to reimagine the lab: digitally connected, data-driven and ready for the future. From Merck’s modernization journey to cross-industry discussions on AI, data security and unifying platforms, the event marked an important step in furthering the understanding of the industry’s needs toward transforming Development functions across biopharma.
We will continue to provide updates on Benchling as they become available.
Related Content:
- Why Paper Won’t Cut It In FDA’s Remote Oversight Era
- Accelerating the Trajectory of Digital Sustainability in the Lab
- FDA Shares Approach to Regulating AI: What Life Science Companies Need to Know
- The State of Generative AI In Life Sciences: New Market Research
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.