Various digital technologies such as robotics, sensors, and artificial intelligence (AI) are converging with the potential to greatly change the way laboratory research is done.
Momentum for digital technologies in lab research is growing and was accelerated by the COVID-19 pandemic, which caused teams to rapidly adopt digital tools and rethink their current processes. While there are many digital use cases and technologies available to enhance lab research, they can be broadly broken up into three categories: modeling and informatics, knowledge management, and lab automation.
Based on primary interviews, we identified key technologies and use cases for each segment that range from those that are ready to adopt now (e.g., property optimization) to those that should be explored or monitored (e.g., quantum computing). Overall, we found modeling and informatics to have the greatest impact and expect lab automation to take longer to mature. Lastly, we found that cultural barriers pose as significant a challenge to adoption as technology barriers do.
In this report, we develop a roadmap for building the lab of the future by introducing new forms of automation, knowledge management, informatics, and other digital tools to improve the efficiency, quality, and cost of research. We also highlight key lessons learned in digitally transforming the lab by interviewing leaders in this space.
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Le laboratoire du futur
Par :
Analyste
Vice-président et directeur du groupe
Various digital technologies such as robotics, sensors, and artificial intelligence (AI) are converging with the potential to greatly change the way laboratory research is done.
Momentum for digital technologies in lab research is growing and was accelerated by the COVID-19 pandemic, which caused teams to rapidly adopt digital tools and rethink their current processes. While there are many digital use cases and technologies available to enhance lab research, they can be broadly broken up into three categories: modeling and informatics, knowledge management, and lab automation.
Based on primary interviews, we identified key technologies and use cases for each segment that range from those that are ready to adopt now (e.g., property optimization) to those that should be explored or monitored (e.g., quantum computing). Overall, we found modeling and informatics to have the greatest impact and expect lab automation to take longer to mature. Lastly, we found that cultural barriers pose as significant a challenge to adoption as technology barriers do.
In this report, we develop a roadmap for building the lab of the future by introducing new forms of automation, knowledge management, informatics, and other digital tools to improve the efficiency, quality, and cost of research. We also highlight key lessons learned in digitally transforming the lab by interviewing leaders in this space.
Téléchargez le résumé pour en savoir plus.
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Le laboratoire du futur
Par :
Analyste
Vice-président et directeur du groupe
