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

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, PhD, Enthought CEO


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.

Read the full article in Lab Manager here.

More resources about building the Lab of the Future here.

Share this article:

Related Content

Jupyter AI Magics Are Not ✨Magic✨

It doesn’t take ✨magic✨ to integrate ChatGPT into your Jupyter workflow. Integrating ChatGPT into your Jupyter workflow doesn’t have to be magic. New tools are…

Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over…

Read More

Real Scientists Make Their Own Tools

There’s a long history of scientists who built new tools to enable their discoveries. Tycho Brahe built a quadrant that allowed him to observe the…

Read More

How IT Contributes to Successful Science

With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at...

Read More

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

Read More

7 Pro-Tips for Scientists: Using LLMs to Write Code

Scientists gain superpowers when they learn to program. Programming makes answering whole classes of questions easy and new classes of questions become possible to answer….

Read More

The Importance of Large Language Models in Science Even If You Don’t Work With Language

OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....

Read More

4 Reasons to Learn Xarray and Awkward Array—for NumPy and Pandas Users

You know it. We know it. NumPy is cool. Pandas is cool. We can bend them to our will, but sometimes they’re not the right tools…

Read More

Leveraging AI in Cell Culture Analysis

Mammalian cell culture is a fundamental tool for many discoveries, innovations, and products in the life sciences.

Read More

Making the Most of Small Data in Scientific R&D

For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid...

Read More