As the world moves towards adopting and enabling new capabilities and efficiencies, there is a heightened emphasis on enterprises adopting artificial intelligence (AI) and expanding intelligent business capabilities by leveraging AI/machine learning (ML) algorithms and pre-trained models.

Companies across industries from agriculture, consumer, financial services, and pharmaceuticals to technology are exploring different AI and ML application areas, according to the Company Filings Analytics Database of research group GlobalData.

“Companies are looking to enhance work efficiencies and personalise customer experience through a combination of proprietary and non-proprietary ML algorithms, large language models (LLM), and neural networks, as well as human curation,” says Misa Singh, business fundamentals analyst at GlobalData.”

For instance, pharmaceutical companies are leveraging advanced ML algorithms and AI-powered tools to streamline drug discovery, create more affordable drugs and therapies, and reduce operational costs.”

Fusion Antibodies, an Ireland-based antibody development company, through strategic alliances with AI/ML companies and with the development of human antibody library “OptiMAL”, is looking at ways to shorten antibody drug discovery timescale.

Lantern Pharma discussed its proprietary AI and ML platform, RADR, which leverages over 34-billion oncology-focused data points and a library of 200+ advanced ML algorithms to help solve billion-dollar, real-world problems in oncology drug development. Apollo Hospitals Enterprise is looking at datasets of Covid-19 patients, integrated and analysed by ML algorithms, to improve diagnostic speed and accuracy.

Financial institutions such as Canara Bank have established an analytics department with state-of-the-art AI/ML algorithms and techniques to develop ML models to perform tasks ranging from prediction to near realtime decisions.

Some companies, as part of their strategy, are upgrading to ML algorithms for higher engagement and providing better customer services. For example, AU Small Finance Bank mentioned the application of AI and ML algorithms to gain a deeper understanding of customer behaviour to provide personalised recommendations.

Insecticides (India) discussed AI and ML algorithms in agriculture to enable the analysis of large datasets, prediction of crop yields, optimisation of resource allocation, and early detection of plant diseases.

Spire Global leverages proprietary AI and ML algorithms to analyse data from the company’s proprietary sensor network and third-party space and terrestrial sources to provide hard-to-get data, insights, and analytics for customers.

“As companies delve deeper into the vast potential of AI and machine learning, we witness not just a revolution, but an evolution in how they operate and interact with customers,” Singh says. “This transformation underscores a commitment to continuous improvement and a relentless pursuit of innovation, propelling us towards a future where the convergence of efficiency, innovation, and limitless possibilities reign supreme.”