The chemical industry is evolving rapidly, and generative AI is at the forefront of this transformation — reshaping sales, streamlining onboarding, and preserving critical industry knowledge. In this fireside chat from Team Nesh, industry experts dive into how AI is revolutionizing sales enablement, helping commercial teams navigate complex product portfolios, and unlocking new efficiencies.

As companies shift from AI experimentation to real-world impact, this discussion highlights the most promising applications and key challenges ahead.

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!

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AI Across the Chemical Industry Value Chain

Jacqueline Wasem: Welcome back to Nesh Talks! In this episode, we explore how generative AI is shaping the chemical industry—particularly in sales. I’m Jacqueline Wasem, Head of Marketing at Nesh, a sales AI platform built specifically for chemical and materials companies and their commercial teams.

Today, I’m joined by Brendan Boyd, an R&D executive with 25 years of experience in the chemical industry, and Sidd Gupta, Founder and CEO of Nesh.

Let’s dive in!

Brendan Boyd: We hear a lot about how AI is revolutionizing different business processes. How do you see AI impacting the various functions within the chemical industry?

Sidd Gupta: If you look at the full value chain in the chemical sector —starting with R&D, then moving through process development, supply chain, legal, product management, sales, and customer service — there’s an opportunity for AI, and particularly generative AI, at every stage.

• R&D: Companies are already using AI to assist in new molecule development. For example, firms like Uncountable are leveraging GenAI to help create novel chemical compounds.
• Process Development & Manufacturing: AI tools (not necessarily generative AI) help predict equipment failures and maintain product quality in factories.
• Supply Chain: AI is being used to forecast demand and optimize logistics, though these applications lean more toward traditional AI than GenAI.
• Sales & Marketing: This is where generative AI can have a major impact — helping onboard new sales reps, educating them on complex product portfolios, and automating the creation of marketing collateral.
• Customer Service & Legal: AI can assist with contract management and regulatory compliance, making back-office operations more efficient.

The potential use cases are vast, and I believe we’re just scratching the surface.

The Role of Generative AI in Chemical Sales

Brendan Boyd: The sales application of AI really stands out to me. In my experience, sales teams don’t work in isolation — they collaborate with marketing, R&D, technical services, regulatory, and product management to ensure customers get the right information.

Given that, how do you see generative AI improving sales operations?

Sidd Gupta: Sales is one of the clearest areas where AI can deliver a strong return on investment. If AI can help sales teams sell faster and more effectively, it directly impacts revenue growth.

Some key benefits include:

• Accelerating Onboarding: A new sales rep can use AI to quickly learn a company’s product portfolio. This is especially valuable when companies go through mergers and acquisitions, inheriting large, complex product lines.
• Competitive Positioning: AI can help reps understand how to position their products against competitors.
• Knowledge Retention: Sales teams rely heavily on internal experts, many of whom are retiring. AI can capture and distribute that institutional knowledge more efficiently.

Measuring AI’s Impact in the Chemical Industry

Jacqueline Wasem: What do you both predict for AI adoption in the chemical industry over the next year?

Sidd Gupta: Right now, we’re seeing a lot of experimentation. I was recently in a room where almost every company had a generative AI pilot in progress. But when asked how many had AI in full production, only two hands remained up.

That tells you where the industry is — 2025 will likely be the year where companies start to identify which use cases provide real, measurable value.

A challenge in the chemical industry is the long sales cycle — sometimes 18 to 24 months — which makes it difficult to quantify AI’s impact.

Companies will need to track alternative KPIs, such as:

• Onboarding Time: How quickly new sales reps ramp up with AI vs. without it.
• Deal Velocity: Whether AI shortens the time it takes to close deals.
• Content Utilization: How effectively AI-generated materials support sales teams.

Brendan Boyd: That’s a great point. The chemical industry constantly evolves —w hether due to regulatory changes, trade restrictions, or shifts in market demand.

AI can play a key role in helping sales teams adapt and communicate effectively. If generative AI can streamline knowledge-sharing across a large, dispersed salesforce, that’s a game-changer.

Jacqueline Wasem: Absolutely!

Thank you both for sharing your insights. This wraps up our latest episode of Nesh Talks.

Stay tuned for more discussions on how AI is transforming the chemical industry.

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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.