As artificial intelligence (AI) continues to revolutionize business operations, two recent reports have underscored the critical need for consistent, high-quality data in building reliable AI systems. Dave Curtis, the chief technology officer at global fintech company RobobAI, has identified four essential elements that organizations should consider when evaluating AI vendors and implementing AI solutions.
RobobAI, a pioneer in utilizing AI for managing spend visibility, optimizing B2B payments, and reducing supplier risks, recognizes that while organizations with high volumes of data stand to gain the most from AI adoption, the quality of that data is paramount. Curtis emphasizes, ‘AI can deliver tremendous benefits but requires a solid data foundation to do so.’ 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:
1. The size of the AI engine: The volume of data it holds determines the number of possible permutations or relationships between data points, which directly impacts the quantity and quality of insights generated.
2. The type of data: It’s crucial to ensure that the AI engine is built on data relevant to your company’s needs, whether that’s images, web references, or financial data.
3. The maturity of the AI engine: The duration of model training and testing is significant. Over time, AI improves data accuracy and increases both the volume and quality of relationships built between data points.
4. The AI team: With over 80% of companies encountering data-related barriers when implementing AI, it’s essential to look for a team with experience in data, AI, and your specific industry.
Curtis highlights the benefits for large organizations that leverage AI to classify spend data, stating that it enables them to manage supplier costs and risks more effectively while optimizing relationships with valuable suppliers. This capability, he argues, helps ensure long-term resilience for businesses.
RobobAI’s approach to AI development is noteworthy. ‘We’ve been rigorously building and testing our AI models for over seven years,’ Curtis reveals. The company offers direct access to these mature AI models, providing proactive organizations with a head start in quickly uncovering opportunities from their finance and procurement data.
The importance of AI in business operations continues to grow, with its potential to transform supply chain management, financial operations, and risk assessment. However, as Curtis’s insights reveal, the success of AI implementation hinges on the quality and relevance of data, the sophistication of AI models, and the expertise of the teams behind them.
As businesses increasingly turn to AI solutions to gain competitive advantages and operational efficiencies, understanding these key elements becomes crucial. The insights provided by Curtis and RobobAI offer valuable guidance for organizations navigating the complex landscape of AI adoption, emphasizing the need for a strategic approach that prioritizes data quality and relevance.
For more information about RobobAI and their AI solutions, interested parties can visit robobai.com.
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