The recent BioTechX conference in Basel was a vibrant hub of 3,500 life sciences professionals in meaningful dialogue and showcasing emerging trends. After energizing face-to-face conversations with customers and industry colleagues, we’re now pondering three significant themes that we’re convinced will shape the biopharma industry's trajectory.
Better Data Foundations Are Badly Needed
The importance of a solid scientific data infrastructure to achieve AI-powered outcomes was number one on everyone's lips. We can’t overstate how many organizations still grapple with the "data utility gap" – a critical disconnect between their terabytes of instrument and informatics data and their ability to use it effectively.
A global crop sciences executive highlighted this challenge: "Hiding all the complexity of instrument integration and accelerating data onboarding are the multibillion-dollar problems in our industry." This number undercounts the value locked away behind the data management hurdles facing life sciences companies.
The industry is not standing still. Collaborative efforts between companies and innovative partners will help accelerate the production of the required scientific data models for AI-powered use cases. Companies that bridge the data utility gap by establishing comprehensive data foundations will be the ones to lead the way in improving patient outcomes.
AI Is Moving From Hype to Hope
Artificial Intelligence in life sciences is entering a new maturity phase beyond the initial excitement and inflated expectations. Up next is a focus on practical, scalable applications of AI technology.
Patrick Moeller, CIO of Bayer, captured this shift succinctly: "AI is today – who doesn't leverage it now will be left behind." This sentiment reflects the growing urgency for life sciences companies to move AI initiatives from experimentation to production.
However, this transition has its challenges. Venkatesh Moktali, PhD, Director of Product Management at QIAGEN, cautioned against the rise of "AI washing" – superficially applying the AI label to products without substantive capabilities. To counter this, organizations must adopt a product-oriented approach to their AI initiatives, emphasizing utility, repeatability, and swift progression from concept to implementation to results widely shared.
As the industry navigates this phase, we anticipate entering a period of more realistic expectations of and practical approaches to realizing AI's actual value in life sciences beyond drug discovery. One of the improvements we’re excited to see is how AI can and will improve the quality of drugs being developed (i.e., more effective, targeted therapies). The potential implications for patient care and treatment outcomes are substantial and far-reaching.
The Rise of the Sciborg
A recurring theme throughout the conference was the criticality of promoting people in the industry with interdisciplinary skills. As our co-founder Spin Wang highlighted in his keynote, "Future leaders in our industry will live at the intersection of science, data, and business outcomes. We call them Sciborgs." Spin’s keynote hit a nerve with his Sciborg slide (below), with several folks coming to our booth after declaring themselves Sciborgs, too.
We like to hire many of them (“Where do you find these people?” our customers often ask), but the biopharma industry has hundreds more. As our VP of Scientific Applications Ken Fountain called out in his February 2023 blog post, Sciborgs deserve to be identified and celebrated for their ability to design, build, and test prototypic scientific workflows, juggle multiple workflows, and deeply intuit and understand customer needs.
The challenges of using AI, machine learning, and automation to achieve orders of magnitude improvements in speed, quality, and impact will require the best of human creativity paired with technical innovation to discover new ways of using the number one asset of global biopharma: scientific data. This interdisciplinary approach is not just a nice-to-have; it's becoming essential. As Michael Sanky, Global Industry Lead for Healthcare & Life Sciences at Databricks, noted, "Giving data scientists access to deep scientific data is a long overdue step forward for the industry." Integrating deep scientific knowledge with advanced data science capabilities opens new frontiers in research and development.
Navigating What’s Next
As we look ahead, several vital areas demand attention:
Data Integration. As a global crop sciences firm executive highlighted over dinner, "Hiding all the complexity of instrument integration and accelerating data onboarding are the multibillion-dollar problems in our industry." Solving these integration challenges will be crucial for unlocking the full potential of scientific data.
Operational Intelligence. As an example of what happens when data is re-engineered to serve business outcomes, a data science leader from a global pharma expressed excitement about "capturing bioprocess data from different instruments and harmonizing it into a single dashboard." The demand for integrated insights is growing across drug research, development, and manufacturing.
Comprehensive Data Strategies. Organizations must develop holistic data programs that serve as foundations for successful AI initiatives. As Alistair Hankin, Industry Principal for Life Sciences at Snowflake, emphasized in his presentation, "There is no AI strategy without a data strategy."
The BioTechX conference reinforced the transformative potential within the life sciences industry. By focusing on robust data foundations, practical AI applications, and enhancing drug quality, the sector can accelerate scientific progress and fundamentally improve our ability to address critical health challenges.
Getting medicine to patients is a lifelong endeavor. We won’t solve the challenges with one model or data set. But we can pick parts of the process to improve that will have a meaningful impact. A lot of minor effects, a tightening of a thousand screws at once, add up to a collectively enormous impact. As we continue to bridge gaps, break down silos, and push the boundaries of what's possible, we move closer to a world where data and AI are seamlessly integrated into every aspect of life sciences, driving innovations that will significantly impact global health outcomes.