AI in the Chemical Industry – the New Table Stakes
Some might see AI as a new tool, but it has actually been around for about a decade. AI is no longer just an ace in the hole—a competitive advantage used only by a select few. The average number of technology cycles workers go through in their careers is swiftly increasing, making it crucial to use tools that support rapid technological advancement. AI is one such key solution and has now become table stakes. There is a huge risk of falling behind in innovation and technology; AI is an instrumental tool for developing and maintaining a competitive edge.
At the World Chemical Forum (WCF) in Houston earlier this year, experts from LyondellBasell, Citrine Informatics, and McKinsey & Company presented various uses for AI. They debated the best practices, advantages, and challenges inherent to AI deployment in the chemical industry.
AI brings major improvements to efficiency and effectiveness, thereby enhancing productivity. Productivity gains in industrial projects implemented to date range from 15–35%. Engineering cost reductions of 30% and a decrease in human error by 40% were some of the AI-driven metrics quoted by industry participants.
That said, only a small percentage of employees reportedly use AI on a daily basis, so there is huge potential for further adoption. The rise of GenAI can exponentially speed up the process.
What kind of solutions does AI bring to the table?
Several key areas in the chemical industry can be improved with AI. Some examples are:
- R&D. Identifying materials needed to make the product and optimizing product design and formulation costs, accelerating innovation on a product level, and incorporating sustainable feedstreams in process design.
- Supply Chains and Operations. Competitively and reliably sourcing RMs, diversifying sourcing strategies, optimizing inventory, maintenance planning and reliability, and product mix optimization.
- Competitiveness. Implementing product quality control, demand forecasting, order processing, dynamic pricing.
One major AI application is pertinent to product and process design for sustainability. AI allows companies to consider all factors when designing a product, starting at the earliest stage of development (e.g., cost and sustainability). Today, most production processes are designed, and then they are cost-adjusted and reengineered; only after are sustainability concerns assessed and addressed. AI can be implemented at the research and development and process design stage, resulting in a cost-effective and competitive market-ready product that is designed for sustainability.
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Another important application is the use of predictive models for planning operations based on price projections and supply-demand dynamics. AI models feed on data and on implementing the closest-to-reality algorithms. Chemical Market Analytics has been building our chemicals database for decades; as market research and analysis experts, we are poised to be a key partner for implementing these predictive solutions.
Best moves for assessing and implementing AI-based solutions
There is no lack of examples of how AI can support chemical industry businesses; the opportunities for incorporation are virtually limitless. However, businesses need to be cognizant of best practices for planning AI projects in order to ensure the most efficient and effective implementation. Best practices for how to tackle these projects, according to experts at WCF, include:
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- Bring in the experts. There’s a huge talent gap—very few people have the knowledge to develop AI-based solutions. Some organizations will spare no expense to get an in-house team; others will simply use an AI guru to collaborate with internal experts. In this era of rapid progress, agility is crucial: An external expert who specializes in the latest AI developments may be the best way forward.
- Collaboration, communication, coordination. Cross-functional developments can be extremely valuable for an organization; these project “gems” can help all (or most of an) organization. In order to target these high-value projects and unveil synergies, an organizationally diverse leadership team with members from different parts of the business should be involved in reviewing the portfolio and performing an ongoing assessment of the projects and their applicability. As such, transparency is essential for the governance team.
- Policy and legal awareness. Organizations need to learn about required cybersecurity standards and data transfer laws that must be followed when planning AI projects. There are many potential landmines that organizations need to be aware of and that need to be considered early on in projects. They will need to be reassessed over time, to prevent major setbacks.
- Be curious and creative. In order to stand out, organizations need to drive change, not follow it. AI projects tailored to a specific business are the most effective. Rather than implementing what others have done, companies need to identify opportunities unique to their own organizations. Blaze a new trail.
- Assess the projects as part of a portfolio, and ensure only key, strategic projects are selected. When companies take on too many projects, these tend to fall into “pilot purgatory”—and there are material costs to delaying a project. To avoid this, experts advise companies to focus on a few, high-impact projects at a time. Eliminate any project that is not “right, right now.” The best way to make the most of AI is to prioritize which data is required for AI to accelerate business performance and not fall into the trap of applying it to all parts of the business. It’s important to narrow down which units will benefit most, and concentrate efforts there.
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AI and competitiveness go hand in hand.
AI is not coming. It is here—and it is allowing companies to make major progress. It provides automation tools for improving efficiency and operations, but it also empowers decision-makers to make better data-driven choices. In order to lead in their industry, businesses need to take a hard look at their organization, identify key untapped synergies and major pressure points, and be prepared for agile AI adoption.