Check out these four technical sales use cases for GenAI in Chemical Manufacturing.
Sales AI
June 27, 2024
Chemical and Material companies embarking on the journey of implementing enterprise generative AI solutions typically begin by familiarizing themselves with the fundamental elements of Generative AI, such as AI models, Large Language Models (LLMs), GPTs, Chatbots, and CoPilots.
These artificial intelligence components and tools are crucial for reaping significant benefits for the most companies. However, chemical companies present a distinct challenge to adopting artificial intelligence due to their intricate technical data landscape that results in a complex data texture.
Consider the vast amount of data encompassing chemical product portfolios, including technical or safety data sheets, handbooks, SOPs, product specifications, and troubleshooting guides. The information within these documents is intricate, specialized, and nuanced. Product names may be similar, words could hold different meanings compared to a standard lexicon, and technical specifications are exact and not easily summarized.
Given the complexity of the technical sector, artificial intelligence solutions must address this data intricacy before it can provide valuable use cases. This is where AI Agents tailored for the chemical manufacturing industry come into play.
AI Agents operate by receiving an input or query from the user and analyzing it for context. The AI Agent then uses complex reasoning, advanced natural language processing, and AI capabilities to make a decision or perform specific tasks based on that information and context. The AI Agent then performs or takes action that typically results in an AI-powered workflow based on that deduction or the goal of the AI Agent.
AI Agents designed for the chemical industry perform workflows like finding substitute products based on specific needs or parameters, comparing products for a specific use case, assisting with product positioning for a specific customer, or even conducting competitive benchmarking.
For a chemical company, deploying an AI Agent could look something like this: A technical sales rep needs a sustainable product alternative for a product currently in use by one of their priority customers. The rep inputs the product and use case. The AI Agent uses contextual information, use case requirements, and its understanding of sustainability requirements to search the company’s product portfolio. It then analyzes the portfolio and returns the most suitable product options to the rep.
Chemical companies benefit from AI Agents' use of Retrieval-Augmented Generation (RAG) and the ability to leverage a large corpus of knowledge captured in their product portfolio data, competitive research, and market research to perform complex workflows.
RAG enables organizations to layer their enterprise data and expert knowledge on top of powerful LLMs. It also helps mitigate the risks associated with LLMs like hallucinations and inaccurate sources. As chemical companies explore GenAI solutions, AI Agents designed to leverage RAG systems and robust enterprise data should be at the top of their list.
Most people are familiar with AI Chatbots, having encountered them in some customer service contexts over the last few years. AI Chatbots are limited in their ability to reason, instead relying on tight controls and scripts to respond to specific questions or prompts. If the response or path isn’t programmed, there won’t be a response.
AI Agents offer a significant step forward, with the potential for transformative benefits in any enterprise and a wider array of use cases. They operate independently and have high levels of reasoning, making them ideal for complex use cases with high ROI.
Another term most are familiar with is GPT or Generative Pre-trained Transformer. GPT is a collection of OpenAI’s machine learning and large-language models. These models take text-based prompts and use natural language processing and natural language understanding to generate conversational answers.
GPT powers OpenAI’s ChatGPT, Microsoft CoPilot, Apple’s Siri, and thousands of other applications that wrap GPTs with specific prompts to deliver helpful workflows like generating a better email subject line or brainstorming some social media posts. In these cases, tailored prompts enhance productivity to provide value.
Over the last few months, more companies have announced the launch of artificial intelligence projects usually positioned as proprietary corporate innovation. In most cases though, these are simple ChatGPT pass-throughs with a corporate-branded interface. These instances usually don’t offer tailored workflows and offer employees a limited playground. Undeniably, ChatGPT is great. But chemical companies need to ask if it’s enough in today’s competitive environment.
Challenges are mounting for chemical companies, which makes tools like AI Agents more important than ever. Here are a few of the key benefits of AI Agents for chemical companies looking to maintain or gain a competitive advantage.
Technical sales, services, and marketing teams are on the frontline of generating revenue for chemical companies. As chemical product portfolios grow and change to meet customer demands for specialty products, sales, services, and marketing teams need solutions that help them leverage growing stores of product information fast.
When the competition moves in on a book of business, manually searching through old-school product finders doesn’t cut it. One engagement with an AI Agent can save hours. Multiply that across a team, teams, or enterprise, and the ROI adds up fast.
Insights. Knowledge. Experience. These are critical human components of sales and services strategies that drive success in the chemical industry. Generative AI can’t replace the human factor that’s critical in today’s sales environment.
AI Agents deliver the knowledge and insights teams need to do their best work in pursuit of growing the bottom line. Teams leverage organizational knowledge to spot trends, provide recommendations at the right time, respond to emerging supply chain issues, and troubleshoot product applications.
Year over year, customer experience continues to decline, making an improved customer experience a mission-critical opportunity to outsell the competition. However, the answer is not AI Chat Bots that offer flawed and often frustrating self-service experiences.
Instead, technical sales and services teams that leverage AI Agents facilitate better customer conversations directly with the information they need at their fingertips to position products, troubleshoot problems, and respond to customer inquiries faster.
AI Agents are tailored to take data, analyze that data, and deliver an output based on a specific goal. Enterprise GenAI solutions like Nesh deploy AI Agents built for the specific needs of teams at chemical manufacturing companies.
Here are a few examples of applications for Technical Sales teams:
As R&D teams deliver more and more sustainable product alternatives, CEOs across the chemical industries are pushing their sales and marketing teams to sell more of their sustainable product formulations. But this is no small task with portfolios made up of thousands of products and more entering the portfolio every day.
AI Agents can perform complex tasks like data analysis, evaluating market trends and customer behavior, and finding sustainable product alternatives. Technical sales reps can act faster to respond to customer questions, proactively provide suggestions to customers with sustainability initiatives, or pursue new lines of business ahead of the competition.
Competitive benchmarking is time-consuming but essential in building strategies to stay ahead of the competition with existing customers or pursue new lines of business. AI Agents can perform the time-consuming task of analyzing and comparing products and their features from multiple sources.
Instead of spending time gathering and compelling data, technical sales reps can use the insights delivered by the AI Agent to build a persuasive case for their products.
A wave of experts are retiring across the chemical and materials industry — making SME succession planning more urgent than ever. AI Agents offer the unique opportunity to capture that knowledge before an expert retires. This knowledge is ingested into the corpus that powers the Agent.
Long-time experts answer ad-hoc or a bank of questions that provide key insights required by sales and services teams every day. Once ingested, RAG leverages this knowledge to enhance product or technical information and amplifies the effectiveness of responses to queries from technical sales and services reps in perpetuity.
Training methods may vary across different sales teams, but the importance of ramping technical sales reps remains consistent. Chemical manufacturing sales demand a deep understanding of products, manufacturing processes, and external factors such as ESG considerations.
Onboarding new team members traditionally takes time and resources, with new reps shadowing experienced colleagues to absorb information gradually. AI Agents can support and accelerate this process significantly, providing instant access to a wealth of knowledge. This streamlined approach allows new technical sales reps to quickly contribute to revenue goals by building their pipeline faster than ever.
New reps aren’t the only ones that benefit from AI Agents. Seasoned team members also find value in staying updated with the latest information in the ever-changing chemical manufacturing sales landscape.
Want more? Check out our fact sheet: The Future of Chemical Sales: 6 GenAI Use Cases
Chemical and materials companies that aren’t considering artificial intelligence are already behind the competition.
AI Agents tailored for the chemical manufacturing industry are crucial for addressing the complexity of the technical sector and providing use cases that deliver tangible value.
Nesh specializes in AI Agents that offer enhanced productivity, better decision-making, and improved customer conversations, making them important tools for chemical companies.
Need more help choosing the right artificial intelligence solution for your chemical company? Check out our buyer's guide below.
Check out these four technical sales use cases for GenAI in Chemical Manufacturing.
Learn how to optimize your technical sales cycle with AI.
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Generative AI and retrieval-augmented generation (RAG) have become essential tools for chemical technical sales teams.
