Another year is almost in the books, so we thought it would be interesting to lay out some of our expectations for the year ahead. Enjoy!
- Robotics and automation business cases will be less and less about labor cost savings, and more and more about other benefits such as precision, repeatability and data capture. Labor savings will continue to be an important component, but additional important benefits will grow as drivers of bringing robotics to labs.
- Sustainability will become part of the automation conversation, and influence how people think about system architecture, increasing interest in system flexibility and reconfigurability. Static monolithic systems only capable of doing one thing well and not suited to evolving with changing science will increasingly be seen as unsustainable approaches.
- Software platforms will become increasingly interconnected, with inventory management to automation to data collection and analysis systems being able to converse interchangeably as a baseline requirement.
- Organizations will move from thinking about automation on a project by project basis, to thinking more holistically about their platforms at least on a site if not enterprise basis. The benefits of thinking beyond a single project are fairly clear, and some organizations are already quite good at this level of enterprise thinking, but many lag behind. As automation systems continue to grow and advance, and as software becomes more connected, more and more scientific research organizations will begin to seek an overall automation strategy.
- Mobile robotics will continue to advance largely as a function of automated warehouse robot development (e.g. Locus Robotics), but although there will be some buzz and early prototyping, mobile lab robots will still not be very capable or interesting next year.
- Instruments and devices not historically integrated into automation work flows, and thus not particularly automation friendly, will begin adapting to a world in which automation readiness is a requirement. This could include chromatography systems, bioreactors, and other process instruments that have been outside the typical automation work streams.
- Interest in machine learning and artificial intelligence in the biopharma discovery space will begin influence how scientists record and organize data, as many organizations with interest in using machine learning realize that their existing data sets are not properly set up to work with machine learning. This will result in some key partnerships between global research organizations and companies like In Sitro, who understand the data generation and organization issues. Like 2018, we will continue to see large numbers of deals done in the AI/pharma space.
- Synthetic biology will continue to advance at a significant pace, driving additional needs for robotics and automation as the experimentation via production and evaluation of numerous novel DNA constructs requires robotics.