Industries
Chemicals
Creating tailored digital solutions to improve efficiency in chemical production and quality assurance using AI
Chemical operations are complex, data-heavy, and sensitive to inefficiencies R&D teams and manufacturers need better tools to accelerate discovery, reduce waste, and maintain consistent quality
We provide AI models, predictive analytics, and custom software to support smarter decision-making in chemical processes
Future trends
annual hours saved
Cognite50-site AI programs free 100,000 hours annually
Over two years, chemical operators will standardize industrial data platforms to scale maintenance and optimization models across sites. That capability releases engineering capacity for faster production and reliability decisions.
A global chemical and specialty-materials company saved more than 100,000 hours annually after deploying over 50 AI use cases across 50 sites.
Cognite
projected predictive-maintenance adoption
IBMPredictive maintenance nears universal deployment by 2028
By 2028, predictive maintenance will shift from a selective reliability program to a standard digital layer across chemical plants. Asset strategies without connected data risk a structurally weaker cost base.
Predictive-maintenance adoption in chemicals is projected to increase from 39% today to 98% by 2028.
IBM
opex savings
Boston Consulting Group (BCG)AI-first operating models target 10% opex savings
Over the next two years, chemical producers will redesign engineering and production workflows around AI rather than isolated use cases. A lower cost base can reset margins through the next cycle.
AI-first chemical companies can achieve opex savings of 5% to 10% through AI-enabled engineering and production decisions.
Boston Consulting Group (BCG)
projected predictive maintenance adoption
IBMPredictive maintenance races toward 98% adoption by 2028
By 2028, AI predictive maintenance will shift from selective deployments to an expected operating capability across chemical production. Plant leaders should fund connected maintenance data before reliability gaps harden.
Predictive maintenance adoption in chemicals is projected to rise from 39% today to 98% by 2028.
IBM
Our use cases
We can build platforms that analyze lab results, simulations, and chemical data to identify promising compounds faster
We provide real-time monitoring and predictive models that optimize production settings and minimize downtime
We can automate analysis of lab reports and test data to flag anomalies and maintain compliance
We offer tools to track environmental metrics and ensure operations meet evolving regulatory standards
We deliver AI solutions that align production planning with predicted demand to avoid stockouts or overproduction
We create tailored tools to manage experiments, standardize reporting, and centralize research knowledge
