Cognizant Announces Multi-Agent Orchestration for its Neuro AI Platform
Enhanced platform integrates powerful multi-agent AI orchestration that can accelerate the enterprise AI productivity-to-growth journey
Dozens of businesses have already leveraged the platform to rapidly identify, build and pilot AI decisioning use cases that can deliver business value at scale
Cognizant announced significant enhancements to its Cognizant Neuro AI platform, aimed at enabling enterprises to rapidly discover, prototype, and develop AI use cases that can improve decision-making, leading to better company performance and new revenue opportunities.
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According to data from a Cognizant and Oxford Economics study, most enterprises (76%) are looking to leverage AI to create new revenue streams but struggle with implementing and scaling cross-enterprise use cases. Many (70%) also don’t think they’re moving fast enough. The enhancements to Cognizant Neuro AI address these problems — in just minutes, business leaders can identify what business problems to tackle, scope them, and generate synthetic data or import their own anonymized data to start creating AI models. The platform can then predict and provide guidance on meeting business outcomes while justifying those decisions, thereby enabling businesses to quickly assess the impact of a variety of use cases.
Now available to Cognizant clients, the enhanced Cognizant Neuro AI platform has been tested, piloted and used by dozens of clients already and is backed by multiple patents. It can be leveraged for almost any industry or business challenge involving data analysis, from inventory management and dynamic pricing to fraud reduction and efficient staff allocation. The enhancements to the Neuro AI platform began as research projects at the Cognizant AI Research Lab, which launched earlier this year. The lab focuses on researching and developing decision-based AI systems, and these enhancements are the first developments from the lab that have been integrated into a commercial offering.
Gilead Sciences, a leading biopharmaceutical company and Cognizant client, commented on the enhanced Neuro AI platform:
“Many enterprises struggle to apply AI beyond predicting outcomes, and that’s because solving real business problems usually involves thousands of different scenarios often with conflicting priorities,” said Murali Vridhachalam, Head of Cloud, Data, and Analytics at Gilead Sciences. “With these latest updates, Cognizant Neuro AI is the only platform I’ve seen that empowers businesses to quickly deploy end-to-end Gen AI use cases across various applications, and to uncover tangible, revenue-generating opportunities. Its innovative, multi-agent approach to managing decision workflows sets it apart in the industry.”
Another Cognizant client, Bayer Crop Science, also commented on their experience using the enhanced platform:
“Agriculture is one of the most challenging professions, requiring intricate decision-making amid environmental uncertainties and the need to balance social, economic, and environmental objectives,” said Patricio Salvatore La Rosa, Head of Decision Science at Bayer Crop Science. “We have directly tested several foundational components of Cognizant Neuro AI, especially LEAF, which has empowered us to navigate complex scenarios effectively. By harnessing the collaborative capabilities of specialized Gen AI agents, we look forward to addressing intricate decision-making challenges in a reliable, transparent, and trustworthy manner.”
Cognizant has integrated powerful new features as part of the enhancements to Cognizant Neuro AI, including a multi-agent powered discovery tool to identify use cases, called Opportunity Finder, as well as a suite of large language model (LLM) assistants that form a powerful AI decisioning engine. Clients first interact with the platform through Opportunity Finder, an LLM-assistant that helps identify potential AI decisioning use cases for their businesses. The Model Orchestrator, featuring a drag-and-drop interface, then enables users to clean up the data and apply a variety of machine learning models to it. Data preparation is streamlined through LLMs and then machine learning models are applied to predict outcomes while evolutionary AI models prescribe decisions. Once trained, the best models can be further interrogated through a web interface or queried via an LLM assistant.
The enhanced Cognizant Neuro AI platform is available with pre-built configurations that provide easy starting points for various use cases. These include healthcare (like drug discovery and treatment plans), finance (such as cybersecurity and fraud prevention), agriculture (like crop yield optimization and pesticide development), and general templates for supply chain, call centers, customer retention, and price optimization.
“Businesses are struggling with how and where to apply AI to solve business problems, and that’s why we’ve seen most AI use cases limited to prediction-based outcomes or single LLM chat-based solutions,” said Babak Hodjat, Chief Technology Officer of AI at Cognizant. “Multi-agent AI systems hold the key to solving these problems, which is why Cognizant Neuro AI is now built with one at its core. This platform puts business leaders – not just data scientists — in the driver’s seat, so they can tap into their own domain knowledge to quickly test and establish decision-making use cases for AI in minutes and then provide the resulting model code to iterate at scale.”
According to Gartner, “Multiple agents can work toward a common goal that goes beyond the ability of individual agents. The combined application of multiple agents can tackle complex tasks that individual agents cannot, while creating more adaptable, scalable and robust solutions.”1
Neil Ward-Dutton, VP Automation, AI and Analytics at IDC also commented: “As enterprises start to try to approach AI strategically, and move beyond experimentation, they are crying out to understand how to identify and prioritize use cases. Providers that can use technology to help accelerate the identification of use cases, and then use that technology to test and scale implementations, will be in a strong position.”
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