Cognizant unveils major upgrades to Neuro AI platform

The upgraded platform is currently being implemented within local Australian companies and provides a tool for industry leaders to deploy advanced AI solutions to tackle business challenges. With an aim to empower enterprises, Cognizant Neuro AI helps in identifying, prototyping, and scaling AI decisioning use cases that can convert AI initiatives into revenue-generating opportunities.

The enhanced platform includes a new multi-agent powered discovery tool called Opportunity Finder to help businesses identify and prioritise AI use cases across various sectors. It enables users to construct AI models swiftly using synthetic or anonymised data, all supported by a user-friendly drag-and-drop interface and a suite of AI decisioning agents. applications for industries such as healthcare, finance, and agriculture are readily available.

Babak Hodjat, Chief Technology Officer of AI at Cognizant and inventor of Siri, elaborated on the potential of AI systems to support business processes more seamlessly. "This level of automation (specialised agents) has the potential to eliminate many redundancies and bureaucracies. AI agents will break down barriers, speeding up processes and solving problems you may not even have anticipated. The productivity gains could be unlike anything we've seen since the invention of the computer," he said.

Babak Hodjat also highlighted the shift in how AI is being approached in business: "We should view AI as a knowledge worker in a box. Like human workers, AI agents can sometimes be unpredictable and opaque in their reasoning. However, we can measure their performance and deploy them in ways that software alone could never handle. This transition represents a significant shift. In the past, we've incrementally added tools to support human work, but now we're transitioning to treating AI almost like employees. The biggest challenges won't be technological - they'll be cultural."

Cognizant's announcement coincides with findings from a study conducted with Oxford Economics, indicating a desire among most enterprises (76%) to leverage AI for new revenue streams, though many face challenges in scaling such initiatives.

The platform's enhancements include features developed from research conducted at the Cognizant AI Research Lab, which was launched earlier in the year. Clients such as Gilead Sciences and Bayer Crop Science have already utilised the updated platform to address industry-specific challenges.

Murali Vridhachalam, Head of Cloud, Data, and Analytics at Gilead Sciences, remarked on the platform's capabilities: "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. 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."

Patricio Salvatore La Rosa from Bayer Crop Science echoed similar sentiments, highlighting the platform's utility in agriculture: "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. We have directly tested several foundational components of Neuro AI, especially LEAF, which has empowered us to navigate complex scenarios effectively. By harnessing the collaborative capabilities of specialised Gen AI agents, we look forward to address intricate decision-making challenges in a reliable, transparent, and trustworthy manner."

In conclusion, Babak Hodjat emphasised the transformative power of multi-agent AI systems: "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. Multi-agent AI systems hold the key to solving these problems, which is why 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."