RobobAI CTO Outlines Four Critical Elements for AI Success in Business

As artificial intelligence (AI) continues to revolutionize business operations, new reports underscore the critical need for consistent, high-quality data to build dependable AI systems. Dave Curtis, 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.

Curtis emphasizes that while organizations with large 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 explains. He notes that many companies face challenges due to multiple, siloed legacy systems containing disparate, duplicate, and incomplete data, making it crucial to establish an appropriate AI foundation for long-term success.

The first key element Curtis highlights is the size of the AI engine. The volume of data an AI system holds directly impacts the number of possible permutations or relationships between data points, which in turn affects the quantity and quality of insights that can be generated. This suggests that businesses should prioritize AI solutions with robust data repositories.

Secondly, Curtis stresses the importance of the type of data an AI engine is built on. Organizations should ensure that the AI solution they choose is trained on data relevant to their specific needs, whether that's images, web references, or financial data. This alignment is crucial for deriving meaningful insights and value from the AI system.

The third element is the maturity of the AI engine. Curtis points out that AI models improve over time, enhancing data accuracy and increasing the volume and quality of relationships built between data points. Companies should consider how long an AI model has been in training and testing phases when making their selection.

Lastly, Curtis emphasizes the significance of the AI team behind the technology. With over 80% of companies encountering data-related barriers when implementing AI, it's vital to work with a team that has experience not only in data and AI but also in the specific industry the business operates in.

The implications of these insights are significant for businesses across various sectors. Large organizations that successfully leverage AI to classify spend data can better manage supplier costs and risks, optimize their supplier relationships, and enhance their long-term resilience. This is particularly crucial in today's dynamic business environment, where supply chain management and cost optimization are key competitive factors.

Curtis reveals that RobobAI has been rigorously building and testing its AI models for over seven years, resulting in mature systems that are now being offered directly to organizations. This approach aims to give proactive businesses a head start in quickly uncovering opportunities within their finance and procurement data.

As AI continues to evolve and become more integral to business operations, understanding these four key elements will be crucial for companies looking to implement AI solutions successfully. By focusing on the size and type of data, the maturity of AI models, and the expertise of the AI team, businesses can position themselves to reap the full benefits of AI technology, potentially transforming their operations and gaining a significant competitive edge in their respective markets.