November 10, 2026
Collaboration in Action
Taking place just ahead of the annual fall conference, these focused workshops are designed to translate ideas into practical outcomes. Through collaborative, hands-on sessions, diverse Pistoia Alliance communities come together to address real-world challenges in the life sciences. Participants will explore topics ranging from digital transformation in pharma laboratories and the practical application of CMC process ontologies, to the pressing issue of AI ready data. Each workshop emphasizes shared learning, active participation, and tangible outputs, providing attendees with actionable insights and frameworks to support innovation, interoperability, and long-term impact across the ecosystem.
Digital Transformation: From Vision to Practice
Hosts: Anca Ciobanu & Farah Egby
Building on the discussions and insights from the Q2 London workshop on Digital Transformation, the workshop in Boston continues the conversation with the 3 Pistoia Alliance Communities of interest: Change Management, User Experience in Life Sciences, and Future Labs Evolution.
While companies across the industry widely recognize why digital transformation matters, many still struggle with how to make it work in practice. One of the key findings from the London workshop was that organizations understand the strategic importance of digital transformation but face significant challenges when it comes to implementation, particularly in aligning: people, processes, and data to support sustainable adoption.
Workshop Goal: This hands-on, strategy-meets-practice workshop will take a deeper and more focused look at three critical pillars that determine successful digital transformation: data foundations, change management, and workforce transformation. Participants will explore how strong data readiness enables scalable solutions, how effective change management drives adoption across functions, and how workforce transformation ensures teams are equipped with the skills, mindset, and operating models needed for long-term success. Through practical frameworks, interactive discussion, and peer exchange, attendees will create actionable insights to help bridge the gap between the digital transformation ambition and organizational readiness—turning digital transformation from a strategic aspiration into measurable operational impact.
From Recipes to Knowledge Graphs: A Hands-On Intro to the CMC Process Ontology
Host: Birthe Nielsen
This workshop aims to bring together Pistoia Alliance CMC enthusiasts — ontology practitioners, data leaders, and chemical engineers — to explore how the CMC Process Ontology can turn everyday, unstructured process descriptions into shareable, machine-readable knowledge. Using a real-world general recipe as our common sandbox, we’ll connect theory to practice and surface what’s needed to make CMC more robust to standardize CMC recipes.
By the end of the workshop, participants will:
- Be familiar with the CMC Process Ontology project (and links to ISA-88)
- Understand how the CMC PO represents recipe-relevant knowledge
- Explore pain points (e.g. informal language, cultural variants, uncertainty)
- Discuss extension needs (e.g. units, equipment, materials)
- Explore future opportunities: interoperability (e.g., with IDMP, IFO), validation profiles, and LLM-assisted annotation workflows
Defining AI-Ready Data for Life Sciences
Hosts: Vladimir Makarov, Rob Gill, Giovanni Nisato
This workshop will address one of the most pressing and often unclear concepts in today’s data and AI landscape: AI-ready data. While the term is increasingly used across life sciences, its meaning remains inconsistent or misunderstood. This creates friction in implementation, misalignment across stakeholders, and barriers to realizing the full value of AI-driven innovation. This workshop will bring together experts from two Pistoia Alliance communities:
- The FAIR data expert community
- The AI community of interest including LSAIE participants
We will establish a shared, actionable understanding of what “AI-ready data” truly means in the context of life sciences and participants will collaboratively:
- Review current interpretations and usage of the term
- Identify gaps, misconceptions, and areas of ambiguity
- Define key characteristics and requirements for AI-ready data
- Align on principles that support both FAIR data practices and AI deployment
We aim to develop a white paper synthesizing the workshop discussions and conclusions to provide the industry with a clear, consensus-driven definition and practical guidance to support AI readiness across the data lifecycle. By bridging FAIR and AI perspectives, this initiative aims to reduce ambiguity, accelerate adoption, and enable more effective, scalable use of AI in life sciences