Enterprise AI development is shifting focus from solely improving models and data pipelines to incorporating the institutional knowledge held by non-technical employees. PromptQL, a startup, is creating AI analyst tools that integrate this human expertise into AI systems to address challenges faced by large organizations, especially in sectors like retail. This approach seeks to reduce errors caused by undocumented changes and knowledge silos that hinder accurate data analysis at scale.
By involving non-technical staff in the AI workflow, PromptQL hopes to prevent misunderstandings that arise when important context—such as changes in business definitions—remains unrecorded and only understood by a few individuals. This method aims to create a more reliable and comprehensive knowledge base that supports better decision-making across the enterprise.
**Why this matters**
As companies increasingly deploy AI beyond pilot projects, relying solely on improved models and data is proving insufficient. Capturing the nuanced understanding of business processes from employees who are not data specialists can help prevent costly analytic errors and improve the accuracy of AI-driven insights. Addressing knowledge silos is critical for enterprises seeking to scale AI applications effectively and maintain consistent, trustworthy analytics.
Source: NewsData