Generative AI has taken the world by storm, but few are familiar with specialised AI. Simply put, specialised AI refers to artificial intelligence systems that are designed and trained for a specific task or set of tasks. These AI systems are tailored to excel in one domain and have limited capabilities beyond that domain. Examples of specialised AI include language translation systems, image recognition algorithms, and recommendation engines.
Here’s the interesting part. While generative AI is booming, it can be very costly. This is because there is significant expense involved in training, and executing large language models sets it apart as a structural cost, distinct from previous computing booms.
“Generative AI and specialised AI represent two distinct branches of artificial intelligence, each fulfilling unique roles. In the realm of automation, GenAI facilitates rapid automation creation, whereas specialised AI, honing in on specific tasks and trained on task-specific data, presents notable advantages within enterprise AI over broad foundational models like Chat-GPT,” says Arun Balasubramanian, VP & MD, India, and South Asia, UiPath, a NYSE-listed enterprise automation software company, with more than 10,000 customers globally, including Federal Bank, JSW, Max Healthcare, etc., in India. “In simple words, specialised AI yields swift, precise, and cost-effective tailored solutions, generating high-value outcomes.”
UiPath has been working on AI for many years. Its specialised AI solutions include over 70 models that enable customers to understand screens, mine tasks, process documents, and utilise unique and proprietary data sets within enterprise workflows. According to Balasubramanian, examples of early adopters of specialised AI solutions are companies in retail, healthcare, financial services and banking, insurance, and manufacturing. As the potential of specialised AI continues to unfold, its applicability holds promise for various industries, enabling them to navigate complex challenges and unlock new areas of growth.
Sharing examples on how UiPath customers have leveraged specialised AI, Balasubramanian said ArcelorMittal Nippon Steel India optimises vendor invoice processing using UiPath Document Understanding. With 300,000 invoices annually from 10,000 vendors, it achieves 90%+ accuracy, saving 6-9 days on average.
“Again, Thermo Fisher Scientific, a global Fortune 500 company, employs UiPath Document Understanding for efficient document processing. Processing over 800,000 invoices yearly, it achieves 70% processing time reduction and 85% automated data accuracy. Hiscox, a top insurer, deploys UiPath Communications Mining, reducing broker response time from days to hours,” he says.
