This talk explores how digital technologies are transforming laboratory environments. We will discuss how advanced software solutions and cloud-based services enable automation, data integration, and collaboration in scientific research. Key topics include AI-driven analysis, electronic lab notebooks (ELNs), laboratory information management systems (LIMS), and secure cloud platforms. These innovations streamline workflows, enhance data accuracy, and support remote access, making labs more efficient, connected, and future-ready.

Translational research increasingly depends on secure and compliant management of complex patient sample data, including radioactive and isotopically labeled assays. To overcome workflow fragmentation and data integrity challenges, we developed CAMEO, an AI-ready digital platform that streamlines secure data exchange, automates device interactions, and standardizes review and approval processes. By enabling interoperability, compliance, and future AI integration, CAMEO accelerates data reliability and digital transformation across experimental and clinical workflows.

R&D teams want to use AI capabilities, but regulated software processes typically can't keep up to the speed of AI innovation. This presentation explains how a shared software substrate can close that gap with reusable building blocks, or primitives. With a solid foundation in place, the result is faster deployment of GxP, clinical, and executive AI workflows without compromising validation, governance, or compliance.

Public data and software infrastructure remain foundational to biomedical innovation, but the conversation has shifted. Rather than broad instability, recent policy and funding changes are creating targeted adjustments in priorities, governance, and access that are reshaping how scientific data is generated, shared, and sustained. This session focuses on how leaders across academia, government, and biopharma are actively adjusting their strategies in response. The discussion moves beyond awareness to execution, examining how organizations are changing how they generate, access, and rely on scientific data.

The session opens with insights from the Global Alliance for Open Science (GAFOS), launched at the 2025 Bio-IT World Conference & Expo. Drawing on input from more than 170 professionals across academia, biotech, pharma, government, and nonprofits, the survey highlights key concerns, emerging risks, and practical challenges related to public data and software infrastructure. Short talks and discussion will then examine how perspectives and strategies have evolved over the past twelve months. Where are risks still real? Where has the narrative corrected? What signals are driving decisions now? The session concludes with a moderated, interactive discussion between speakers and attendees.

Attendees will leave with a clear understanding of how data strategy, funding reliance, and infrastructure decisions are changing across the ecosystem, and what actions are required to operate effectively in this new environment.

• How are knowledge graphs supporting reliable scientific intelligence
• Lessons from early deployments
• Remaining challenges and barriers to scale​

• Learn how leading global pharma companies are applying quantum principles to real-world research. 
• Discover the quantum companies transforming traditional processes around drug development and drug discovery.
• Discuss how quantum, along with HPC and AI, is poised to help researchers tackle historically intractable problems and potentially find new treatments for diseases like cancer, Alzheimer’s, and diabetes.​

• Where are we today? Are AI tools truly integrated into workflows, or still operating in silos?
• What changes with agents? How do LLM-based agents shift drug discovery from prediction to decision-making?
• What is blocking autonomy? Data quality, validation, trust, or organizational readiness for AI-driven discovery?​

• Where are the highest-value collaboration opportunities between academia/TTOs and industry (biopharma, startups, AI/data, investors), and what makes them actually work?
• What types of partnerships are most effective in practice (licensing, co-development, startup creation, sponsored research), and how do both sides evaluate quality and fit quickly?
• What specific formats, access points, or structures at the Bio-IT event would actually drive follow-up, partnerships, and deal-making?

• How do CIOs enable rapid AI and data innovation without breaking governance, compliance, or data integrity frameworks?
• What separates pilot-stage innovation from scalable, production-grade systems? Where do most organizations fail?
• How do leaders manage emerging risks (model bias, data provenance, regulatory exposure) while still pushing competitive advantage?
  

• How real-world data is being used alongside clinical and experimental data
• Challenges in standardization, access, and regulatory acceptance
• Opportunities to improve outcomes, access, and long-term patient insights

• Why critical experimental knowledge is lost across ELNs, LIMS, and siloed teams, and the cost of reinventing work 
• How structured experimental memory makes past decisions, failures, and context reusable across teams
• How organizations can make scientific knowledge reusable for both humans and AI

• How to think strategically about IP early in biotech and AI-driven research
• Common pitfalls in patent and trademark applications and how to avoid them
• Navigating ownership, protection, and commercialization of innovations
  

• Why do current workflow systems fail most scientists in practice? 
• Can no-code and AI-driven workflows meet standards for reproducibility, validation, and clinical readiness? 
• What would a “production-ready” workflow ecosystem actually look like across teams and organizations?

• The gap between AI ambition and the reality of data foundations, platforms, and operating models
• What “AI‑ready data” actually means in practice—beyond pilots, dashboards, and proofs of concept 
• Where AI initiatives most often break down: data integration, data quality, metadata, and governance at scale​

The focus on compliance continues to intensify, spurred by a growing list of data privacy, security. and publication mandates such as the recent changes to the NIH Genomic Data Sharing Policy. Research organizations are looking for solutions to meet these new requirements without unduly burdening investigators. We will discuss how the Globus platform enables definition and enforcement of diverse policies that enable researchers to confidently work with PII, PHI, and other sensitive or controlled-access data without compromising compliance requirements. We will present common use cases and review features that address compliance with frameworks such as NIST SP 800-171.

While AI continues to dominate discussions in project management, many Project Management Offices (PMOs) struggle to move from conceptual curiosity to real implementation. This talk introduces a practical AI Readiness Roadmap specifically tailored for PMOs. It will walk participants through the maturity stages, from assessing organizational data, processes, and tools, to piloting AI use cases, and eventually scaling AI adoption responsibly.

Most AI initiatives die between the demo and the dashboard. This talk focuses on what happens next. Jason Alexander outlines how organizations move beyond pilots by embedding AI directly into enterprise workflows, connecting data, systems, and teams to enable scalable execution. Through real-world examples, he shows how to operationalize AI in complex, regulated environments and drive measurable business impact.

This talk presents a reverse engineering–driven approach to modernizing legacy systems by extracting core business rules, workflows, and data dependencies and exposing them through API-led architectures. It shows how this incremental strategy preserves critical functionality while enabling real-time access for downstream AI and LLM-driven applications. Attendees will learn how to convert static, siloed systems into structured, interoperable, AI-ready platforms without disrupting existing operations.