Last week, I had the privilege of attending the Lab of the Future USA 2025 conference, where some of the brightest minds in biopharma R&D and digital transformation explored the industry’s evolution toward "Industry 5.0"—characterized by cognitive systems, agentic AI, and human-robot collaboration.
Industry Transformation: The Shift to Data-Centric Ecosystems
One of the most striking themes across presentations was the industry-wide pivot from electronic lab notebook (ELN)-centric workflows to data-centric ecosystems. This shift goes beyond digitizing paper processes—it's a fundamental rethinking of how scientific data flows through an organization.
As Dr. Hans Bitter from Takeda eloquently put it during his session, organizations need to embrace standardization that enables end-to-end digitalization across the R&D lifecycle to generate predictive knowledge across functions and lifecycle stages. This is influencing a "right-first-time" approach that is dramatically improving speed, agility, quality, and R&D efficiency.
Digital Infrastructure to Support Innovation
Cathy Kuang from Takeda emphasized that robust digital infrastructure serves as the cornerstone empowering innovative pipelines throughout the therapeutic asset journey. Takeda is moving from legacy architecture with multiple system interfaces toward a decoupled platform with a unified data layer. This transformation creates an informatics ecosystem centered on data access rather than system dependencies.
Their unified data layer strategy evolves from current capability frameworks to a future state incorporating strategic applications, semantic technologies with harmonized ontologies across R&D functions, and outputs that enable researchers to develop drug candidates more efficiently. This approach aligns with Hans Bitter's vision of standardization enabling end-to-end digitalization to improve speed, agility, and R&D efficiency.
AI Implementation: Right-Fitting Technology to Scientific Challenges
A critical insight from the conference from Mickey Atwal at Flagship was that AI/ML success in biopharma hinges on applying the right technologies to the right scientific challenges with the right data. It was fascinating to observe the maturity curve across the value chain: AI adoption is highest in early discovery phases and tapers off in development stages, presenting a significant opportunity for innovation.
Mohit Agnihotri and team from AbbVie shared some impressive results in this area, noting their ADME models for endpoints for ~250,000 compounds are delivering substantial time savings both in pre-synthetical stages and laboratory workflows. Their chemists are now focusing their time only on the most promising compounds and they’ve managed to replace in-vitro assays when appropriate to accelerate timelines. Their pragmatic approach—accepting that "80% solutions are okay for now"—is driving rapid progress.
The Digital Maturity Journey
Most biopharmaceutical organizations are transitioning from "islands of automation" toward integrated systems, with fully autonomous labs projected for 2027-2028, according to Kausheek Nandy from Boehringer Ingelheim. Currently, most companies are at the systematic integration (L2) and AI-assisted systems (L3) stages.
Dr. Timin Hadi from Amgen presented a particularly insightful "hierarchy of data needs" that clarified why infrastructure and automation must precede AI/ML implementation—a grounding perspective that resonated widely.
Organizational Transformation
Digital transformation isn’t just about technology—it requires co-ownership between scientific teams and IT partners. Dr. Diana Bowley and Dr. Jordan Stobaugh from AbbVie echoed this sentiment and emphasized identifying and developing champions who simplify workflows and drive change. They focused on creating an end-to-end data integration solution for bioprocess development with fit-for-science digital solutions. They demonstrated how their end-to-end data automation combined with their Espresso data model enables full experiment traceability across cell culture development, reducing scientist time spent on experiment setup, data transcription, and documentation by an impressive 90%.
Similarly, Dr. Mohan Boggara and Christelle Le Beaudour from Sanofi shared their company’s shift for AI/ML from siloed use cases to transversal, process-area-focused workflows driven by business-led product teams. Their end-to-end digital workflows for upstream cell culture development that has been scaled across 3 modalities and 5 sites have cut manual data wrangling from 6 hours to just 30 minutes—a 90% productivity gain. This has also resulted in structured, contextualized, and harmonized data sharing with other sites.
Key Takeaways
- The era of isolated digital tools is ending. Organizations now recognize that end-to-end data automation with harmonized data models across departments and sites is essential for generating predictive insights.
- Business-led product teams with C-suite sponsorship are replacing siloed approaches; this cross-functional collaboration ensures digital initiatives align with strategic objectives.
- The Minimum Viable Product (MVP)-Scale-Optimize approach is gaining momentum as companies move away from "boiling the ocean" to more agile implementations that deliver incremental value.
- Cross-industry collaboration through consortiums like the Enabling Technologies Consortium (ETC) is accelerating progress by sharing best practices.
- Code agents represent the next frontier in digital transformation. An expert panel led by Dr. Julie Huxley-Jones from Vertex likened them to generative AI three years ago—making them a space to watch closely.
Looking Ahead
As digital transformation advances in the biopharma industry, the most successful organizations will be those that prioritize replatformed data in a central location, harmonized data models, integrated visualization, and strategic AI applications. The near future lab won't just be automated—it will be anticipatory, proactively suggesting next actions based on complex data analysis.
I'm excited to apply these insights within our organization and would love to hear your thoughts on these trends. Special thanks to the thought leaders who shared their expertise and vision.