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The Lab of the Future: Finding the Right Recipe for Success

How to shift from a human-centric approach to a compute-centric one

by
Eric Jones

Eric Jones is the founder and CEO of Enthought, Inc. He holds a PhD and MS in electrical engineering from Duke University and a BSE in mechanical engineering from Baylor...

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The R&D laboratory of the future is here, and it’s powering scientific innovation and discovery faster and more efficiently than ever before. Yet in a 2021 survey of 200 global laboratory leaders, 64 percent admitted they weren’t investing enough in intelligent, connected technology, and 69 percent believed they would lose their competitive advantage if they didn’t find ways to connect and automate their labs. Of the science-driven companies who have started their digital initiatives, many are failing to achieve their connected lab aspirations as legacy systems with siloed data, insufficient resources, missing change agents, a growing skills gap, and a limited line of sight to business value hamper efforts. 

Despite these roadblocks, companies can and should prioritize upleveling their labs—particularly as competition in both existing and emerging markets is more intense than it ever has been. If organizations are going to realize the full potential of digital transformation, they must take a step back and think about the R&D lab differently. They need to shift from a human-centric approach to a compute-centric one.

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Reconsidering the human-centric approach to the R&D lab

R&D laboratories were originally built around the workflows and limitations of humans, and the majority of labs still follow this model. Researchers move files around via USB drives and record notes in lab notebooks. They then transcribe and record the most important findings in Excel spreadsheet templates, which are archived on a network shared drive. While these spreadsheets may be technically centralized and shared with other researchers, extracting the necessary data from a sea of elusive spreadsheets and project archives is difficult, resulting in these data files being rarely used for secondary analysis. This type of workflow is slow and inefficient, using too much manpower, time, and resources.

This traditional, human-centric approach not only limits what’s possible within the lab, but also determines the purpose of the work itself, which is to quickly get new materials or formulations out the door. However, with a compute-centric approach, the purpose of experiments sits at a higher level—to build intuition to continually make new discoveries. 

The goal of R&D in a compute-centric lab shifts to focus on acquiring data in a way that allows scientists to use computational tools to facilitate their discovery, and to shorten that period. It removes the redundancies and paves the way for better, more efficient analysis.

Overcoming barriers hindering progress in the R&D lab

What’s preventing organizations from replacing their old model with one that’s compute-centric? Often, companies think the integration of machine learning, artificial intelligence, or a one-size-fits-all software on top of their current processes and data will significantly reduce their experiment time and accelerate new discoveries. Technology-first initiatives, however, are typically expensive, unpredictable, and error-prone. Successful digital transformations require a holistic approach, which certainly includes investments in technology, but also in the people and processes around it.

Shifting an organization’s long-held mindset is no easy feat. Teams are often so entrenched in what they’re doing and how they’re doing it that it’s hard to see beyond the restrictions of their existing corporate or lab structure. For some researchers, particularly those who are tenured, resetting their mindset or accepting that some of their manual tasks will be automated can be daunting, or even threatening. It's important to have an internal stakeholder who understands both the science and the business' goals to serve as a liaison and identify knowledge deficits and digital skills gaps.

Ultimately, compute-centric R&D teams know that new digital tools and automated integrations not only change their work for the better, but they also change the relationship with their work. The advanced processes and infrastructure allow them to spend more time analyzing and innovating, and less time on mundane tasks. When the scientists closest to the research have strong digital skills, the impact is further magnified. A digital transformation solutions partner who has both domain and technical expertise can help guide and train scientists through a transition.

Embracing the compute-centric connected future

To effectively rethink the laboratory, leaders need to first take time to understand their unique organizational inefficiencies, systems, and processes—and then ask questions like: 

  1. What’s needed to enable my scientists to focus 100 percent of their time on discovery? 
  2. In a perfect world, what would be the optimal formulation and processing workflow? 
  3. Could a machine augment that process, and what would that machine look like?

The answers to these questions usually tie back to data. High-quantity, high-quality data is essential to feed the tools and technologies that will empower scientists to make new discoveries. Traditional data management tools, however, are rigid, inflexible, and woefully insufficient in leveraging complex scientific data required for these ideal conditions. Effective digital solutions are those purpose-built for R&D. They are dynamic, flexible, and fit how scientists think—allowing them to not only bring products to market faster, but to build the intuition needed to continually innovate. 

In a compute-centric lab, data generation is no longer a human-driven process. Not only is data cheaper and generated faster than ever before, but it’s also intentionally tailored for decision-making by scientists and lab leaders. Researchers become data-armed explorers in the automated lab, unlocking new discoveries and business potential.