July 18, 2024

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Ahead of the Curve: Using AI Tools in Research and Development to Leverage Data  

Over the next ten years, Bloomberg predicts the generative artificial intelligence (AI) market will grow to $1.3 trillion from a market of $40 million in 2022. What’s more, corporations are investing robustly in AI technologies to streamline operations and assist in new innovations — with more than 63% of organizations prioritizing AI over other digital technologies, according to TechCrunch

So, what do these trends tell us from a corporate efficiency standpoint? The cost of not using AI because of potential risks or privacy concerns outweighs the risk you assume by experimenting with it. Let’s break down what this means, and how companies can strategically navigate a changing landscape in data science.

View AI as a Resource, Not a Threat

It’s no secret there is a lot of hesitancy around AI. Inc. reported many employees fear they will lose their jobs to AI applications. However, contrary to widespread concerns regarding job security, AI actually has the ability to enhance existing jobs, and more importantly, create more jobs in spaces such as data science, machine learning, and materials informatics. With the emergence of tools such as ChatGPT, AlphaCode, and more, AI is here to stay, and for a good reason — we can use it by integrating it into our workflows for optimization. 

Leverage AI and Emerging Technologies to Maximize Efficiency

By 2025, Gartner Insights predicts more than 30% of new drugs and materials will be systematically discovered using generative AI techniques, up from zero today — proof that we’re actively seeing the tool utilization explode in spaces like manufacturing and product design.

In the research and development landscape, AI — in both traditional and generative models — can assist in developing algorithms that carry out tasks and analysis. 

Ask the Right Questions

Before diving in head-first, make sure you’re thinking critically as to how AI can best be a value-add to your company. Tools are costly, and there are a variety of models and applications that require you to make key decisions.

ChatGPT is a relatively cost-effective option for smaller operations looking to test the waters, but there are other AI tools such as PyTorch and Weka for advanced data analytics and more. So, to be strategic about your approach, do your research and find the right tool that’s the best investment for your needs. 

Develop the Internal Know-How; Train and Reassure Your Employees

Internally, it’s extremely important to use AI with care to prevent situations such as data leaks, especially in industries that work with intellectual property and confidential information. This requires educating your team on best practices. 

In addition to the technical aspect, being aware of employees’ collective fears and hesitations toward AI, and getting ahead of them, matters. Informing employees about how the company intends to use AI for efficiency, and not as a replacement, will make for easier integration across the board. 

Stay Up-to-Date on New Capabilities and Methods

As a continually evolving sector, and one that promises to continue changing as data and technologies progress, the best way to stay ahead of the curve is to immerse your company and team in its processes and stay up to date with new machine learning tools and advancements.

Picture of By Vishwanath Hegadekatte

By Vishwanath Hegadekatte

Vishwanath Hegadekatte, PhD, is the Senior Manager of Future Technologies at Freudenberg North America. With an extensive background in developing research and development strategies and implementation methods, Dr. Hegadekatte has played a crucial role in the growth of research staff among many roles throughout his career—in North America, Europe, and Asia.

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