New Reports Emphasize Critical Need for Quality Data in AI System Development

Two new reports highlight the need for consistent, high-quality data to build reliable AI systems. Dave Curtis, chief technology officer at global fintech RobobAI, explains key elements for AI success. Organizations with high volumes of data can realize the greatest benefits from adopting AI; however, the quality of data is critical.

AI can deliver tremendous benefits but requires a solid data foundation to do so, Curtis says. Multiple, siloed, legacy systems bursting with disparate, duplicate, and incomplete data make this a challenge. Establishing an appropriate AI foundation is critical for long-term success.

According to Curtis, there are four key elements to look for when assessing AI vendors: The size of the AI engine determines the number of possible permutations, or relationships between data points, which impacts the number and quality of insights that can be generated. The type of data must be appropriate for your company's needs, whether images, web references, or financial data. The maturity of the AI engine indicates how long the model has been training and testing, as over time AI improves data accuracy and increases the volume and quality of relationships built between data points. The AI team should have experience in data, AI, and your specific industry, as over 80% of companies that embark on AI hit barriers relating to data.

Large organizations that leverage AI to classify spend data gain the ability to manage supplier costs and risks and optimize more valuable suppliers helping ensure their long-term resilience, Curtis says. We've been rigorously building and testing our AI models for over seven years, Curtis says. We have mature AI models and we're offering direct access to these models to give proactive organizations a head start in surfacing opportunities from their own finance and procurement data quickly. For more information, visit https://explorerobobai.com.