Will Tashman
Co-Founder and CCO, Uncountable
Will Tashman is the co-founder and Chief Customer Officer at Uncountable. He leads strategic initiatives for new business development and oversees the customer solutions teams, having scaled to support hundreds of customers and thousands of users across various fields and industries such as advanced materials, specialty chemicals, and coatings technologies. With a degree in Materials Engineering from MIT and previous engineering roles at Apple, Will brings over a decade of expertise in materials informatics and enterprise technology.
Stick With Us Here: How To Benefit Chemists Using Data and Digitalisation
The integration of Artificial Intelligence into product development and quality management is no longer a future ambition, it is an operational imperative. Yet for many organizations, the promise of AI remains unrealized, not due to a lack of technology, but due to fragmented, unstructured, and disconnected data. This presentation addresses that gap directly, offering a practical framework for aligning R&D, Quality, and Product Development under a unified, AI-ready data infrastructure.
Historically, these functions have operated in silos: R&D captures formulation data in lab notebooks or ELNs, Quality manages test results in LIMS or spreadsheets, and manufacturing tracks process parameters independently. Each system was designed to solve a specific problem, and often does so effectively within its own domain. The challenge emerges when organizations attempt to derive cross-functional insight or deploy AI across this fragmented landscape. Without a consistent, connected data model, AI models are starved of the signal they need; quality issues cannot be traced back to development decisions; and institutional knowledge remains locked in legacy systems.
Drawing on experience with over 150 enterprise customers across specialty chemicals, pharmaceuticals, food and beverage, and coatings, this session identifies the three most common barriers to AI deployment, insufficient data volume, poor use-case fit, and eroded user trust from premature implementation, and presents actionable strategies to overcome each. Central to this approach is a shift in sequencing: rather than bolting a data model onto existing systems after the fact, organizations are encouraged to define their data architecture first, structuring data at the point of capture to ensure it is granular, machine-readable, and connected across functions.
The session also addresses the human dimension of digital transformation. Technology selection, while important, is rarely the deciding factor.
Breakout Session XIII – Data-Driven Innovation in Adhesives – 18 September 2026 – 11:00 – 11:30 – Room Fleming – F3