While there are endless solutions for transformation in the digital space, it is the responsibility of lab leaders to also drive transformation in the physical space. Creating organisational buy-in, cross-coordinating between departments, and spreading new systems to all labs, can become tremendous challenges if the organisation needs to be fought every step of the way.
Join this panel to gain expert insight into how to secure organisational buy-in, then split into smaller groups at our discussion tables to consider how these strategies can be applied to your specific industry. With the framework of the panel discussion in place, smaller groups can dive deeper into the ideas brought up with the panellists, and your peers:
• Building use cases for new digitalization technology to guarantee C-suite support and investment
• Transforming systems from the ground up, building modular showcases to spread effective digitalization strategies across your company
• Finding pain points and working backwards to implement digital tools that improve laboratory efficiency
• Cooperating across laboratories to take a strategic view of digitalization
• How each industry approaches digitalization and what they can learn from one another
Amit Bhowmik - Global head, Process, Chemical, Capital Projects Advisory – Tata Consultancy Services
Debashish Ray - Industry Advisor – Manufacturing - Tata Consultancy Services
Artificial intelligence is going to remain a cornerstone of lab automation until labs are being operated with the lights off. However, for many, use cases and system wide adoption take time to build so the idea of an automated lab lies far in the future. This is a future that will only be reached if the foundations can be laid early. If the high volume of data labs collect can be standardized and annotated correctly. If reasonable explanations can be rolled out so that modular A.I adoption can spread across the industry. If methodology between experimental and computational teams can be standardised. Join this plenary to learn:
· Sharing methods between different teams to allow for adoption of modular A.I. methods
· Coordinating collaboration between experimental and computational teams that standardize workflows and unite teams implementing A.I.
· Annotating and cleaning data so that future A.I. models can be easily scaled and tested
· Spreading successful cases between research and lab teams to test upcoming models in differing environments