Discover how sales reps using Generative AI in procurement qualification are accelerating workflows and closing 50% more deals.
Sales AI
January 30, 2025
The chemical industry is facing a wave of transformation, driven by generative AI’s ability to enhance sales, accelerate R&D, and bridge growing knowledge gaps. In this fireside chat from Team Nesh, industry experts discuss real-world applications of GenAI, from improving sales efficiency to tackling workforce challenges and material innovation.
As companies move beyond experimentation and focus on ROI-driven implementations, the conversation sheds light on how AI is shaping the future of the industry.
Don’t miss this episode of Nesh Talks featuring Brendan Boyd, Sidd Gupta, and Jacqueline Wasem.
Catch the video or check out the transcript below!
Jacqueline Wasem: Hello, and welcome! Thank you for joining us today. We’re going to be discussing real-world use cases of generative AI in the chemical industry.
I’m Jacqueline Wasem, Head of Marketing at Nesh, a Sales AI platform designed specifically for chemical and materials companies and their commercial teams. Today, I’m joined by Brendan Boyd and Sidd Gupta. Would you both like to introduce yourselves?
Brendan Boyd: Hey, my name is Brendan Boyd. I’m an R&D leader in the chemical industry with about 25 years of experience. Most recently, I was a Vice President at a Fortune 500 chemical company, and now I own two companies.
Sidd Gupta: Excited to be here and talk with you and Brendan.
Jacqueline Wasem: Let’s dive right into the topic. Why is it important for companies to approach generative AI implementations with use cases in mind?
Brendan Boyd: Great question. One of the most compelling use cases I’ve seen for GenAI in the chemical industry is in supporting sales.
In my experience, leading teams that support sales through product development or technical service, there’s always a need to ensure that sales teams — who are on the front lines — have the information they need in a timely manner. Whether it’s putting together offers for customers, responding to competitive situations, or simply answering customer questions, getting the right information quickly is crucial.
A common challenge is that sales reps often don’t know who to turn to for answers. Should they escalate to their sales manager? Should they reach out to a contact in another department? This typically results in a lot of emails, texts, and phone calls — creating an asynchronous process with significant delays.
A great application of GenAI is capturing the expertise within an organization — whether from technical service, regulatory specialists, or product stewards — and making that knowledge easily accessible to sales teams when they need it.
Sidd Gupta: That’s a great point. Another major use case for GenAI is in molecule discovery, which falls more on the R&D side rather than the commercial side.
Traditionally, discovering a new molecule in the lab is a slow process. But with GenAI, companies can accelerate that process significantly. This use case has a distinct ROI tied to it, much like the sales use case Brendan mentioned. Both highlight how chemical companies are leveraging GenAI to drive efficiency and value.
Brendan Boyd: Absolutely. Another area where AI can add value is in material substitution. Regulations are constantly evolving, and companies often need to replace materials that are being phased out.
For example, there’s currently a lot of focus on replacing per- and polyfluoroalkyl substances (PFAS). AI can help by analyzing material properties and identifying suitable alternatives much faster than traditional R&D methods.
Sidd Gupta: Brendan, your point about sales support reminds me of another challenge in the chemical industry. We’re seeing two major workforce trends:
New sales reps struggle to onboard because they don’t have the same expert network around them. This makes AI-driven tools even more valuable, as they can provide new reps with institutional knowledge that would otherwise take years to acquire.
Brendan Boyd: That’s a great observation. I’ve heard this phenomenon called the “silver tsunami” — as experienced professionals retire, it becomes difficult to replace their 20–25 years of expertise with a six-month or even year-long onboarding program.
Another demographic shift is that fewer students are pursuing STEM degrees. And those who do often choose careers in finance, investment banking, law, or business rather than the chemical industry. As a result, companies are hiring people without a strong chemistry background, which makes onboarding and training even more critical.
With fewer subject matter experts available and increased pressure on cost reduction, new hires often struggle to access the knowledge they need —either because they don’t know who to ask or because the experts they need are overwhelmed. AI can bridge this gap by making institutional knowledge more accessible, reducing the time it takes for new hires to ramp up.
Sidd Gupta: That’s an interesting shift. Have you noticed a change over time in the background of salespeople in the chemical industry? Were there more salespeople with chemistry degrees in the past?
Brendan Boyd: Yes, definitely. When I started, most salespeople had backgrounds in chemistry or chemical engineering. Over the past 20 years, that has changed. Now, we see more sales professionals with adjacent scientific or technical degrees. As a result, organizations have had to rethink their training programs to accommodate people coming from different educational backgrounds.
Jacqueline Wasem: As we wrap up, how should companies think about ROI when deciding which GenAI projects to pursue?
Brendan Boyd: From a customer perspective, every GenAI investment needs a clear ROI. But the specifics depend on each company’s unique pain points.
For example, if a company uses AI to replace restricted materials, the ROI is clear — finding an alternative faster saves significant R&D time and costs.
On the sales side, reducing the time between a rep’s question and a customer’s answer improves efficiency. This can be reflected in sales metrics, such as the length of time opportunities remain open. If AI can accelerate decision-making, companies will see a tangible impact on their sales pipeline.
Sidd Gupta: I agree. I think 2025 will be the year when companies move beyond the hype and start focusing on real, ROI-driven applications of GenAI. Many companies have been experimenting with AI for the past few years, but now they need to determine which tools create real value and which don’t.
The key will be focusing on AI solutions that can either generate revenue or drive concrete cost savings. Without that, companies will struggle to justify continued investment.
Brendan Boyd: Exactly. And beyond direct ROI, there are also intangible benefits. AI can reduce the number of basic questions sales reps ask, allowing experts to focus on more complex problems. It can also enhance employee development by reinforcing training materials and providing on-demand learning.
Companies that position AI as both a productivity tool and a learning aid will see long-term benefits in sales performance and customer satisfaction.
Jacqueline Wasem: Thank you both for your insights today. This is just the beginning of our new series, so we hope everyone checks back for the next episode. Thanks again!
Want to learn more about GenAI use cases for the chemical industry? Check out the fact sheet, The Future of Chemical Sales: 6 GenAI Use Cases.
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