Liyang Zhang is Co-Founder and CEO of SciOne AI, where he builds the operating intelligence for research and development organizations across life sciences, chemicals, and beyond. He has spent close to ten years delivering AI, digitalization, and IoT solutions to the world's most complex R&D and laboratory environments, including BASF, Frontage, Johnson & Johnson, and SGS. Previously he led large-scale cloud and SaaS transformation initiatives at Amazon AWS and served as Global Chief Application Architect at HSBC. His earlier consulting work at KPMG and PwC delivered digital transformation programs for Fortune 500 companies. He holds a master's degree from Carnegie Mellon University, and is driven by applying technology to the industry's hardest business problems.
Most lab operational data sits in siloed systems, spreadsheets, and on paper. Leadership fights to stitch it into reports, and even then those reports only describe the past, not what constrains throughput, where capacity is lost, or how next quarter will perform. This session makes the case for an operating intelligence layer above existing systems: an AI Lab COO that diagnoses constraints, forecasts risk, and schedules work against capacity, with people approving decisions. Drawn from enterprise deployments across pharma, CRO and chemicals, it shows how leaders trade reactive firefighting for higher utilization, less manual work, and lower scaling risk.
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