As artificial intelligence (AI) continues to revolutionize global industries, a new, specialized form of AI — vertical AI — has emerged as a game-changer for the chemical sector. Unlike general-purpose AI, vertical AI makes it possible to build software specifically for complex industry verticals, like chemicals and ingredients.
Most SaaS products have been built to address business processes and challenges in the broadest, most general way possible. Vertical AI enables chemical companies to access software specifically designed for their unique needs, considering the highly technical data and specialized business workflows.
In this article, we’ll introduce vertical AI and explore how it can transform chemical companies’ operations, from research and development (R&D) to supply chain optimization.
What is Vertical AI?
Vertical AI is a type of artificial intelligence created for specific industries. Unlike horizontal AI, which uses general algorithms across many areas, vertical AI focuses on deep knowledge and specialized data to solve the unique problems of a particular sector. In other words, horizontal AI leverages broad datasets to deliver broad results, while vertical AI leverages an organization’s own hyper-specialized datasets to deliver hyper-specialized results.
Example of vertical AI applications include:
Harvey, a generative AI platform tailored for law firms and legal professionals
Hippocratic AI, which develops an LLM and AI agents to improve health care
Sixfold, an AI application designed to help insurance underwriters
What is Horizontal AI?
Horizontal AI can be used by any company regardless of their industry. It uses general algorithms across many areas. Examples of horizontal AI applications include:
For the chemical industry, vertical AI means AI models that understand the intricacies of chemical processes, regulatory frameworks and material properties. Vertical AI doesn’t just process data — it interprets it within the context of your industry and your business, enabling precise, actionable insights.
Why Vertical AI Matters for the Chemical Industry
The chemical industry operates in a uniquely challenging environment requiring precision, safety and adherence to stringent regulations. Here are some ways vertical AI can address these demands:
Complex Supply Chains: Managing chemicals’ global sourcing, production, and distribution involves numerous variables. Vertical AI can optimize logistics, forecast demand and identify supply chain risks.
Stringent Regulatory Requirements: Compliance with environmental, safety and trade regulations is essential. Vertical AI helps streamline compliance processes and ensures accurate reporting.
Demand for Innovation: Accelerating R&D efforts is critical for maintaining a competitive edge. Vertical AI can predict successful chemical formulations, reducing time-to-market for new products.
“Smaller models, trained on domain-specific data deliver better performance on latency, accuracy, and cost than larger foundational models.” – Sangeet Paul Choudary, for Platform Revolution
In non-tech speak, that means vertical AI models trained on a data set from a single industry can work faster with greater accuracy at a lower cost than a generic tool like Microsoft CoPilot or Google Gemini.
Applications of Vertical AI in the Chemical Industry
Vertical AI applications can address core workflows. Supporting workflows may be able to be addressed with horizontal or generalized AI applications. Core workflows within a chemical company are any workflows necessary to manufacture and deliver a product to customers. Supporting workflows include back-office functions such as EH&S, marketing and procurement.
Let’s explore some of the most impactful core workflow applications:
1. Field Sales Enablement
Field sales teams in the chemical industry often face challenges managing large product portfolios and addressing highly technical customer needs. Vertical AI can transform field sales enablement by providing instant access to centralized product data, enabling sales reps to browse catalogs by any dimension or parameter. It simplifies searching, filtering, and finding the exact product required, while also allowing reps to quickly make tailored product recommendations and suggest effective alternatives or offsets.
Example: A field sales representative uses Knowde MDM platform during a meeting with a customer. If the customer asks “Do you have a translucent high-density plastic with a specific tensile modulus that is approved for food use,” the rep would search their company’s centralized product catalog by those parameters to find a product that fits the requirements.
2. Predictive Maintenance
Chemical manufacturing equipment often operates under extreme conditions, making unplanned downtime costly and potentially hazardous. Vertical AI uses real-time data from sensors to predict maintenance needs, helping companies avoid breakdowns and optimize equipment performance.
Example: Senseye Predictive Maintenance from Siemens uses AI along with machine learning and institutional data to provide predictive maintenance that is moving toward prescriptive maintenance. (Source: Seimens) It’s basically like adding an expert to the team who can help manufacturing staff make smarter decisions to increase efficiency and uptime.
3. Product Formulation
Developing new chemical formulations can be a time-consuming, trial-and-error process. Vertical AI accelerates this by analyzing historical data and simulating potential outcomes. This reduces the number of physical experiments required, enabling faster innovation.
Example: IBM’s RXN for Chemistry utilizes AI to predict chemical reaction outcomes, reducing the need for extensive physical testing. This approach has accelerated research timelines by up to 30%, fostering faster innovation. (Source: Digital Defynd)
4. Quality Control
Maintaining consistent product quality is critical in the chemical industry. AI-driven quality control systems monitor production in real time, detecting anomalies before they lead to defective products. This ensures that every batch meets regulatory and customer standards.
Example: AI applications in image recognition and spectroscopy have improved quality control in chemical manufacturing by detecting defects and ensuring products meet specific standards, leading to enhanced product consistency. (Source: Allied Market Research)
5. Regulatory Compliance and Safety
Compliance with environmental and safety regulations is non-negotiable for chemical companies. Vertical AI automates monitoring and reporting processes, reducing the risk of human error and ensuring adherence to industry standards. For example, AI can track emissions in real-time, flagging potential violations before they occur.
Example: AI-driven predictive maintenance not only reduces downtime but also helps in meeting safety standards and environmental regulations by preventing equipment failures that could lead to safety incidents. (Source: Nanoprecise)
6. Supply Chain Optimization
From sourcing raw materials to managing transportation logistics, supply chains in the chemical industry are complex. Vertical AI enhances supply chain visibility, predicts demand fluctuations and optimizes inventory levels, helping companies adapt to changing market conditions.
Example: AI enables more efficient management of the chemical supply chain by analyzing historical data and market trends to generate accurate demand forecasts, optimizing inventory levels, and reducing costs. (Source: Allied Market Research)
Benefits of Vertical AI for Chemical Companies
The benefits of adopting vertical AI for chemical companies are substantial:
Enhanced Efficiency: Vertical AI reduces costs and minimizes waste by automating routine processes and optimizing operations.
Improved Decision-Making: Gain data-driven insights for strategic planning and operational adjustments.
Faster Innovation: Accelerate R&D efforts with predictive modeling, quickly bringing new products to market.
Sustainability: Optimize energy usage, reduce emissions and minimize resource consumption to align with sustainability goals.
Risk Mitigation: Predict and prevent issues, from equipment failures to compliance violations, before they occur.
Statistic: Over 80% of chemical industry executives believe AI will significantly impact their business within the next three years, with implementations in R&D (74%), manufacturing (61%), forecasting and planning (47%), and risk management (58%). (Source: Postindustria)
Challenges and Considerations in Adopting Vertical AI
While the potential benefits are immense, implementing vertical AI requires careful planning. Here are some challenges to consider:
Data Quality and Availability: AI models rely on accurate, well-structured data. Companies must invest in robust data collection and management systems.
Integration with Legacy Systems: Many chemical companies use older systems that may not be immediately compatible with AI technologies. Gradual integration and updates are often necessary.
Regulatory Compliance: AI solutions must align with industry regulations to ensure compliance in automated decision-making processes.
Interpretable AI Models: For executives and stakeholders to trust AI-driven insights, the models must be transparent and easy to understand.
Getting Started with Vertical AI
Implementing vertical AI doesn’t happen overnight. Chemical companies can start by identifying areas where AI can deliver the most value, such as predictive maintenance or quality control. Partnering with AI vendors specializing in solutions for the chemical industry can ease the transition and ensure solutions are tailored to their needs.
Employee training is another critical component. Empower your team to use AI tools effectively, fostering a culture of innovation and collaboration.
The Future of Vertical AI in the Chemical Industry
As vertical AI technologies continue to advance, their impact on the chemical industry will only grow. From driving sustainability initiatives to enabling smarter supply chain management, AI will play a vital role in shaping the sector’s future.
Early adopters of vertical AI are likely to gain a significant competitive advantage. AI works like compound interest, where models that start with a large chunk of data can learn quickly and continuously from prompts and human interaction. This means that companies developing AI-powered vertical software starting with rich industry data will have a greater advantage over other companies that are just starting.
A Transformative Opportunity
Vertical AI represents a transformative opportunity for the chemical industry. Addressing industry-specific challenges with tailored solutions offers a pathway to enhanced efficiency, accelerated innovation, and greater resilience. Exploring vertical AI can provide executives looking to future-proof their operations an edge that general AI can’t match.
