RobobAI CTO Outlines Four Key Elements for AI Success in Business
TL;DR
RobobAI's mature AI models offer direct access to proactive organizations, providing a head start in surfacing opportunities from their finance and procurement data quickly.
The AI engine's size, type of data, maturity, and the experience of the AI team are key elements when assessing AI vendors.
Large organizations leveraging AI to classify spend data gain the ability to manage supplier costs and risks, ultimately ensuring long-term resilience.
RobobAI utilizes AI to help businesses manage spend visibility, optimize B2B payments, and reduce supplier risks, revolutionizing how organizations manage their supply chains ethically and commercially.
Found this article helpful?
Share it with your network and spread the knowledge!
As artificial intelligence (AI) continues to reshape the business landscape, two new reports underscore the critical role of consistent, high-quality data in building reliable AI systems. Dave Curtis, chief technology officer at global fintech RobobAI, has identified four essential elements for AI success that businesses should consider when evaluating AI vendors.
Curtis emphasizes that while organizations with high volumes of data stand to gain the most from AI adoption, the quality of that data is paramount. "AI can deliver tremendous benefits but requires a solid data foundation to do so," Curtis states. He notes that the challenge often lies in dealing with multiple, siloed legacy systems containing disparate, duplicate, and incomplete data.
The four key elements Curtis outlines for assessing AI vendors are: the size of the AI engine, the type of data it uses, the maturity of the AI engine, and the expertise of the AI team. The size of the AI engine, determined by its data volume, affects the quality and quantity of insights generated. The type of data should align with the company's specific needs, whether that's image processing, web references, or financial data analysis.
Curtis also stresses the importance of AI engine maturity, explaining that models improve in accuracy and relationship-building capabilities over time. Lastly, he advises looking for a team with combined expertise in data, AI, and the specific industry, as over 80% of companies embarking on AI projects encounter data-related barriers.
For large organizations, leveraging AI to classify spend data can lead to better management of supplier costs and risks, as well as optimization of valuable supplier relationships. This capability is crucial for ensuring long-term resilience in today's dynamic business environment.
RobobAI, which has been developing and testing its AI models for over seven years, is now offering direct access to these mature models. This initiative aims to give proactive organizations a head start in quickly uncovering opportunities within their finance and procurement data.
As businesses increasingly turn to AI for competitive advantage, understanding these key elements for AI success becomes critical. Curtis's insights provide a valuable framework for companies looking to navigate the complex landscape of AI implementation and maximize the benefits of this transformative technology in their operations.
Curated from 24-7 Press Release
