At a Glance

  • Innovation is the name of the game for chemical companies to optimize operations and enhance profits. To fast-track innovations, use of AI and Machine Learning in the chemical industry is crucial.
  • Many in the chemical industry are touting benefits of leveraging AI and machine learning to analyze data, systemize processes and mitigate errors by minimizing manual interventions.
  • Human-machine combination is proving to be a transformational phenomenon that can change the way the chemical industry is functioning in the USA and all over the world.


The world is growing and seeing changes at an unprecedented rate. The year 2020 has shown us that advancement in science, especially the field of chemicals (and related fields such as biotechnology that deal with the vast majority of chemicals), cannot come fast enough! We need faster, error-free methods to test chemical molecules, computer-generated models for process optimizations, and digitally-adept sensors for quality controls. AI and machine learning play an integral role in efficiently taking the chemical industry towards a trend of modernization and innovation.

The path to digital transformation involves infusing AI into crucial processes and milestones. The journey includes digitizing assets, automating processes based on those assets (known as digitalization), and then creating new ways of doing business.

Figure 1 AI is the greatest commercial opportunity in today’s economy

AI is the greatest commercial opportunity in todays economy

When organizations invest in AI, the goal is to create and enhance digital experiences. AI apps can employ skills to mimic human cognitive functions such as vision, speech, and natural language understanding. By including these human-like capabilities, AI can enable organizations to construct digital experiences that are smart, fast, and helpful to end-users.

A recent study by Accenture shows some promising figures when it comes to use of artificial Intelligence in the chemical industry. According to research, companies that have implemented Industrial AI in the chemical sector are exhibiting significant benefits as follows:

72%report a minimum 2x improvement in some process KPIs

37%report a 5x improvement in KPIs

4 Ways of Applying AI and Machine Learning in the Chemical Industry

While every enterprise has its blueprint to follow when it comes to the application of tools and techniques on AI and Machine Learning, certain broader areas can be common for the chemical industry. Let us have a look at 4 such facets:

01.Data Crunching and Analysis for Molecular Activity Studies

Understanding chemical products and utilizing them for desired effects usually take years of analytical studies, laboratory experiments, clinical trials, farm trials, etc. However, these days, faster results that can mitigate manual errors and minimize efforts are required. Machine learning and AI tools are effectively used to feed and churn a humongous amount of data, and can systematically analyze the said data. This data churning and analysis will help separate chemicals that are effective yet have fewer or no side-effects from the ones that do not have desired effects or are toxic. Apart from the use of AI and machine learning in the pharmaceutical and pesticide industry, possibilities can also be explored in the manufacturing industry, which is heavily dependent on chemicals. Applying AI and machine learning in the chemical industry can also help expedite the efforts in fighting climate change by estimating the damage done by harmful pollutants. This will aid companies in making essential changes in their machinery and processes to minimize the pollutants being released into the water bodies and air.

02.Result-oriented Innovation and Boost in the Chemical Industry R&D Sector

R&D forms the backbone of any industry, and the chemical industry is no exception to this. Today, major players in this sector are looking for focused R&D and innovation that can yield faster and more accurate results following the use of artificial intelligence in the chemical industry. Machine learning tools can help in exercising this type of quick research with the help of computerized permutations and combinations. It can also help in recognizing the right molecules, generate formulas, and aid in knowing the precise quantities of different chemicals required. AI, on the other hand, can help in predicting chemical combinations that can be a breakthrough in terms of innovation. These innovations can boost the ongoing and upcoming efforts in the pharmaceutical, food, biotechnology, oil and gas, petrochemical, manufacturing, and many such industries.

03.Seamless Juxtaposition of Human and Machine Efforts

Since the introduction of computers in the workplace, humans and machines have proven to be the best combination for effective results. However, digital transformation and the introduction of layers and layers of machine-generated intelligence can make this combination even more successful than ever before. Imagine an idea that has come out of the human mind, but it can be analyzed, tested, and streamlined to mitigate risks or errors even before it has physically been put to the test? This can lead to unprecedented time and efforts being saved. Such amalgamation of human and machine efforts can influence the chemical industry to fast-paced innovations, productions, operations optimization, and other concurrent developments.

04.Preventative Measures and Predictive Forecasting

In today’s industrial world, competition is steep and it is essential for enterprises in the chemical industry to be prepared and to be proactive. AI and machine learning tools can help a great deal in this by predicting snafus or future maintenance requirements. Advanced analogs and mathematical models can also be used to estimate oil, gas, and other raw material demands so that the companies can streamline their supply chain in a manner that avoids potential delays and last-minute cost hikes.

While these applications of AI and machine learning cover the broad spectrum when it comes to the chemical industry, there is still a lot of research and development going on in this field. As the world embarks upon a new journey of Industry 4.0, it would be interesting to see more transformations brought in by the use of artificial intelligence in the chemical industry.

Where is the Challenge?

AI-enabled systems are helping companies achieve objectives, goals, and enhance user experience. However, each time a system is not maintained correctly, the process begins to decay by behaving unpredictably. Organizational leadership can thus lose confidence in AI as a process enhancement system. Moreover, organizations should have the required maturity—which encompasses strategy, culture, organizational structure, and core capabilities—to own an AI-based system responsibly. In some cases, this may mean a withdrawal or rejection of AI technologies based on unsuccessful attempts to adopt AI. Organizations or their customers may lose trust in AI as a technology, branding it as unpredictable or too hard to operate. This outcome will push real digital transformation further away and cause the organization to miss out on the powerful impact that AI can have on their business.

Microsoft has worked on defining an operational model that helps organizations assess their attributes that contribute to the adoption of AI technologies. A well designed AI Maturity Model assists organizations in gathering information related to the core characteristics required for teams. Forward-thinking organizations that have embraced AI are witnessing improvement in user behavior. The transformation meets all prime objectives of a Chemical company, mainly driving productivity, sales, Environment Health and Safety, appropriate management of hazardous chemicals in the warehouse.

Figure 2AI Maturity Model

AI Maturity Model

Additionally, Microsoft has compiled prescriptive guidance associated with adopting the right AI technologies for an organization’s current maturity level, while advising on how to increase maturity to embrace more advanced AI capabilities.

Key Takeaways

  • AI and machine learning in the chemical industry can be critical players in clinical trials, farm trials and overall study of chemical molecules.
  • With the cutting-edge application of AI and machine learning tools, the chemical industry can streamline its processes and supply chain for better operations and enhanced profitability.

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