Generative AI could be the catalyst for the chemical industry’s next major age of innovation. However, its efficacy as business problem-solver and life-changing innovator is predicated on a whole lot of ifs.
To get past the hype, the industry will rethink product discovery, revamp supply chain planning and management, and address data quality along with the environmental impact of this new technology.
After listening to leaders from some of the world’s preeminent chemical companies at the ASUG Best Practices: SAP for Chemicals event held in Texas earlier this year, I reached out to a couple of experts for their thoughts on how the chemical industry can close the gap between generative AI’s wildest claims and eventual reality.
In the hands of a supply chain expert in the chemical industry, generative AI is an intelligent assistant that empowers people to think in new ways and discover fresh ideas for solid business gains.
Gen AI reinvents chemical industry discovery
Generative AI is tailor-made for the kind of discovery that underpins the chemical industry’s greatest contributions to society. Equally important, the sector’s rigorous fact-based methodologies are ideal for separating fact from fictions that ChatGPT and other Large Language Models (LLMs) are prone to deliver.
“Industries like chemicals are focused on recombining nature’s molecules to invent exciting new things, and refine applications for existing products and components,” said Joshua Greenbaum, principal at Enterprise Applications Consulting. “They can use generative AI tools to explore and analyze possibilities based on the company’s library of chemical compounds and factory operations. And unlike the humanities and other sectors where absolute parameters aren’t clear, the chemical industry can scientifically test LLM’s suggestions.”
Human-centric demand planning
Market fervor surrounding generative AI may have people thinking that it’s suddenly made other types of AI obsolete. Not so for the chemical industry.
“Demand planning is a great example of where companies can bring in external data from generative AI to augment forecast predictions from traditional machine learning models,” said Matt Reymann, global vice president for chemicals at SAP. “Generative AI contextualizes the data for supply chain, demand, and other planners. This speeds up informed decision-making based on relevant shared information, giving people a next-level user experience that’s less transactional and more human-centered. People would prefer to spend 10 minutes thinking as opposed to 10 minutes clicking.”
Gen AI parses complex supply chain data
Like other complex industries, chemical companies can use LLM tools to brainstorm their fastest route out of supply chain disruptions in an unpredictable global economy. When unexpected events like extreme weather, geopolitical conflicts, and clogged ports threaten production and delivery timeframes, organizations can quickly explore alternative scenarios to keep the customer promise.
“Gen AI won’t solve every business problem and take over everyone’s job,” said Greenbaum. “What it can do brilliantly is parse huge amounts of interconnected data across the chemical supply and logistic chain, including the materials in product recipes, supplier networks, and transportation logistics. In the hands of a supply chain expert, generative AI is an intelligent assistant that empowers people to think in new ways and discover fresh ideas for solid business gains.”
Customer-centricity from AI
Despite technology’s infiltration of every sector, business depends on human communication and collaboration. Gen AI can accelerate self-service queries, boosting employee productivity and the customer experience by combining fragmented data company-wide.
“Chemical companies can use generative AI to support end-to-end business processes for growth and innovation,” said Reymann. “We recently announced Joule, a natural-language, generative AI copilot that will be embedded into SAP applications including HR, finance, supply chain, procurement, customer experience, and SAP Business Technology Platform. It will deliver proactive and contextualized insights to enterprise users across SAP’s cloud portfolio driving productivity and informed investment decisions.”
Ethical and sustainable AI
Organizations will need to figure out the right AI-based strategy to solve their business challenges and mitigate the technology’s risks.
For example, generative AI’s higher computing power fuels significantly higher CO2 emissions across the cloud-based environment. At the same time, it could help prove compliance to growing sustainability regulations.
“A chemical company could run a potential recipe or bill of materials through an intelligent agent that analyzes regulatory compliance to local restrictions,” said Greenbaum. “You want to mitigate the risk of your palette being seized on the dock on arrival for non-compliance.”
Unleashing this technology’s power also requires companies to use it for greatest impact while building in responsible use safeguards.
“As Gen AI accelerates the time to process information, organizations have a responsibility to protect data privacy, and prevent bias and untruths,” said Reymann. “SAP is focused on embedding AI that is relevant, reliable, and responsible by design. Like every technology, generative AI isn’t the all-purpose answer to every business problem. Chemical industry leaders are exploring use cases where it can deliver value based on their unique situation.”