Generative Artificial Intelligence (AI) and data quality can coexist effectively, despite some differing opinions. High-quality data is crucial to the performance of AI systems, including generative AI. Just like good fuel is essential for a car’s performance, an AI system needs high-quality data for its efficient functioning. Thus, having a clear data management strategy can improve the functionality and reliability of generative AI, giving companies a competitive edge in various sectors.
Dr. Abrar Abdulnabi, Head of AI at Saal AI, drew an analogy between data and fuel for AI engines, emphasizing the key role of data quality in AI performance. On similar lines, Kiran, a data scientist at Saal AI, elucidated that high-quality data increased the accuracy and reliability of AI results, including results from generative AI.
However, the path of integrating quality data with generative AI is not without its challenges. Wessam AbuOrabi, Business Development Manager at Saal AI, voiced concerns regarding hasty decisions based on incomplete or inaccurate data analysis. Mishaps can lead to significant outcomes, potentially impacting strategic business decisions and planning. An illustrative example is of a company launching a new product prematurely, based on inaccurate or incomplete market data – the resultant financial losses and reputational damage could be immense.
Looking ahead, Vikram Poduval, CEO of Saal, believes companies will need to synergize their IT, risk, and data teams to manage and use data safely. With the stringent privacy rules in place, proper data management is critical to ensure safety while simultaneously utilizing it for AI training.
As generative AI becomes more advanced, expectations for personalized and ethically-grounded results will rise. The onus is on developers to ensure that high-quality, ethically sourced data is used in a manner that’s both legal and compliant with regulations like GDPR and AI EU Act.
In conclusion, generative AI and quality data are essential for the AI’s performance and its ethical standing. Therefore, proper data management, understanding, and utilization are paramount to realizing the full capabilities of AI technologies.
