Learn how to leverage generative AI to enhance the everyday technical data sheet in the chemical industry.
Chemical Manufacturing
May 2, 2024
For those within the specialty chemical industry, AI implementation isn't just a technological leap. It’s a strategic move to stay competitive in the marketplace and meet ever-growing customer demands, satisfy regulatory compliance, and much more.
However, with great potential comes great complexity. Implementing AI into an ROI-rich sales strategy demands careful navigation through a myriad of questions and considerations.
Whether you're a seasoned executive seeking to optimize operations or a visionary leader poised to revolutionize your industry, this article is your roadmap to unlocking the full potential of AI in sales.
Before we dive into any frequently asked questions, it’s important to understand that GenAI technology is accelerating at such a rapid pace that nobody has all the answers.
This — coupled with the fact that each organization will have different needs and challenges — means we need to break down the FAQs into key big-picture questions.
Let’s explore the questions that must be answered for successful AI implementation in specialty chemical organizations.
Simple Answer — Only your organization can answer this question.
In our experience, the most successful organizations start with a narrow scope of AI implementation and then expand once their sales team is immersed.
Generative AI can be used in a wide variety of ways. But within the specialty chemical sales industry, the most common strategy revolves around knowledge sharing.
Accelerating technical sales in this space requires:
This leads new employees to experience a steep learning curve. Being able to bridge knowledge gaps is usually one of the first pieces of the puzzle for generative AI use.
With machine learning, sales reps can get accurate product recommendations, comprehensive competitor analyses, and detailed insights with the click of a button. Not only does this turbocharge the onboarding process, but it also prevents knowledge loss when subject matter experts leave the organization.
Simple Answer — If you want a prototype, build. If you want a solution that is ready to use, buy.
There are many factors that go into choosing whether you should build or buy:
There isn’t a right or wrong choice here. It will simply depend on your needs and how you plan to use the technology.
You may find that if your use case is limited, a solution already exists, therefore saving you time and money. Whereas if your organization is pushing the boundaries of what is possible, the better option may be to build rather than trying to fit a square peg into a round hole.
Simple Answer — Yes. But the cleaner your data is, the easier it will be to get the most out of AI.
The truth is that we’ve seen organizations at every stage of tech stack maturity find AI implementation successful. Once up and running, you can leapfrog between stages fairly quickly.
That being said, data used by sales teams in the specialty chemical industry is typically product-centric, which can be incredibly complex. Assessing your data and cleaning up inconsistencies or fragmented sources can help streamline the process.
Simple Answer — Executive leadership.
While use cases for specialty chemical companies may be hyper-specific, GenAI touches a wide number of systems, processes, and teams within the organization. This added complexity traditionally means multiple stakeholders must be included for a project of this scale to be approved and implemented.
You may find that sales leadership, IT, customer service, the Chief Technology Officer, and other key stakeholders like an AI Taskforce should be involved throughout the vetting, planning, and implementing process.
Simple Answer — It’s one of the biggest factors in a successful AI implementation.
Many specialty chemical sales leaders are focused on the ROI provided by artificial intelligence, and rightfully so. What can be easily overlooked is the onboarding process.
Understanding a vendor’s onboarding approach is crucial because it helps determine if their solution will ultimately align with your organization’s needs. For complex GenAI projects in the specialty chemical space, having a robust onboarding plan is vital. This plan clarifies your responsibilities and the vendor's role, ensuring a smooth journey toward project success.
Simple Answer — Yes. And we recommend it.
Once you have defined your use case and narrowed down potential vendors for GenAI, piloting is a great option. Most vendors will build this into their contract, allowing you to truly test the waters to ensure that the technology addresses your team’s needs and delivers ROI.
Depending on the scope of the project, pilots can last anywhere from 3-12 months.
Simple Answer — It varies from vendor to vendor.
Unlike buying a solution that’s based solely on a monthly subscription, generative AI has layers of complexity that go beyond setting up an online account and pushing “go.”
Enterprise AI solutions frequently feature intricate pricing models. Ensure you collect and compare a comprehensive breakdown of costs including:
It's essential to clarify the fees for both pilot programs and production stages, including any future licensing fees.
As mentioned above, we understand that the list of FAQs we provided only scratches the surface when you’re considering a GenAI sales solution for specialty industries.
You likely have questions specific to your organization. At Nesh, we work closely with technical sales teams across the specialty chemical industry and understand the challenges reps face day to day. This is why we’re pioneering the power of GenAI specifically in this space.
For more detailed information, additional questions, or exploration of GenAI use cases in specialty chemical manufacturing, download our eBook: The 2024 Guide to GenAI for Technical Sales.
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