Agenda Day 2
8:00 am - 8:35 am
Breakfast & Networking
8:35 am - 8:40 am
Welcome & Opening Remarks from the Chair
8:40 am - 9:10 am
From Historical R&D Data to a Commercial Seed-Predictivity Solution
Finn Bauer -
Vice President of R&D, Life Science,
Croda
In agricultural R&D, useful predictions need to arrive in time to inform customer decisions. Croda's seed-predictivity case study will trace how long-term data and combined data science and knowledge-management capabilities supported the development of a commercially launched solution.
• Build on strong AI foundations: Discover how Croda's two decades of structured seed data, combined with seed expertise and integrated Data Science and Knowledge Management capabilities, laid the foundation for unparalleled product offerings in the market
• Cut lead time from months to weeks: See how AI supports priming, pelleting and X-ray analysis, reducing seed treatment time from up to nine months to around six weeks
• Enable same-season decisions: Learn how faster processing helps customers respond to demand, adjust supply plans and bring seed to market within the same growing season
9:10 am - 9:40 am
Maximise Scientific Learning by Transforming Experimental Data into Enterprise Assets
Experimental data often loses much of its future value when the conditions,
decisions and scientific reasoning behind it remain confined to individual
projects. Capturing this context consistently is critical to making
experimental results reusable across teams, programmes and future
research applications.
• Maximise the long-term value of experimental data by capturing the
conditions, decisions and scientific context required for future reuse
• Prevent repeated work through contextual search that surfaces relevant
results, failed past approaches and historical learnings at the point of
research
• Extend scientific learning beyond individual projects through a connected
digital thread that links experiments, decisions and subsequent outcomes
Sponsored by: Uncountable
AI opportunities across R&D continue to grow faster than organisations can
realistically pursue them. Determining where to invest, what to scale and
which initiatives to deprioritise has become critical to maximising value and
avoiding costly opportunity costs.
• Enhance AI value by identifying whether scientist productivity, knowledge
discovery, workflow automation or predictive science initiatives will create
the greatest impact
• Reduce investment risk by balancing near-term AI wins against
foundational capabilities such as data readiness, harmonisation and
knowledge management
• Rollout AI with confidence by prioritising use cases that can deliver value
across multiple scientific domains, functions and research programmes
10:20 am - 10:50 am
One-To-One Business Meetings & Networking
10:55 am - 11:25 am
One-To-One Business Meetings & Networking
11:30 am - 12:00 pm
From Risk to Readiness: Using Synthetic Data to Safely Scale AI in Regulated R&D
Helena Andres Terre -
Associate Director, AI and ML,
UCB
As AI initiatives move beyond pilots, R&D organisations must balance the need for
broader, higher-quality data with access restrictions, compliance requirements
and scientific rigour. Synthetic data can provide more than a safe alternative
to sensitive information, enabling teams to expand model development, test
decisions and accelerate AI deployment across regulated R&D.
• Expand the range of R&D challenges AI can address by generating
representative datasets where real-world data is scarce, sensitive or
fragmented
• De-risk AI-driven decisions through virtual experimentation and scenario
testing before committing resources to laboratory or development activities
• Scale synthetic data as an enterprise AI capability through governance
that assures scientific validity, traceability and regulatory compliance
12:05 pm - 12:35 pm
Reduce Scientific Uncertainty Through AI-Assisted Decision Intelligence
12:40 pm - 1:10 pm
One-To-One Business Meetings & Networking
1:10 pm - 2:00 pm
Interactive Networking Lunch
2:05 pm - 2:35 pm
One-To-One Business Meetings & Networking
2:40 pm - 3:10 pm
Advance Automated Assay Development from Hard-Coded Workflows to AI-Directed Experimentation
Carsten Pieck -
Director, Head of Bioassay Automation & Compound Logistic,
Merck KGaA
Automated laboratory workflows can reliably execute predefined assays,
but adapting experimental conditions and selecting the next action still
require considerable scientific input. This case study explores whether AI can
build on an established automation and data foundation to direct assay
development with less manual intervention.
• Establish an automation-ready foundation by designing standardised
proteomics data pipelines alongside flexible analytical routes for specialist
scientific questions
• Increase unattended laboratory throughput through barcode-triggered
workflows that select assays, prepare reagents and execute predefined
protocols automatically
• Advance towards AI-directed assay development by enabling an agent to
select reagents, adjust experimental conditions and determine the next test
required to reach a defined endpoint
