Artificial intelligence (AI) is only just getting started and is set to ramp up innovation across a range of industries. As internal and industry-specific AI tools become increasingly available, a new challenge emerges: can you trust them?
The effectiveness of any AI application hinges entirely on the quality and reliability of the data it is trained on. The more AI is used to automate, enhance and augment, the more important the conversation around data visibility and veracity becomes.
Recently, the Data & Trust Alliance introduced data provenance standards aimed at enhancing the trustworthiness of AI and AI-enhanced products. These standards emphasize the importance of understanding the origin, age, sensitivity, and reliability of data—offering the visibility required to support a growing number of AI applications.
At the core of this visibility is data provenance: the ability to trace the journey of data within an organization, offering insights into its quality, security, and validity. By capturing rich information about data’s origins and context, organizations can identify and resolve potential bottlenecks, inconsistencies or inaccuracies in the data pipeline—enhancing the accuracy and effectiveness of their AI systems. Without this, trust in AI models falters, impeding progress.
At IndyKite, we are deeply focused on data visibility, how data flows across systems, and how it can be used to drive value. We believe enterprises need new ways to approach data trust—ways that don’t require overhauling their entire tech stack.
With the right tooling, organizations can work with data as it is, where it is—without huge investment or complex transformations. The shift lies in how you approach the challenge and how you think about your data.
IndyKite enables a flexible data model that allows you to start small and evolve as you scale. You can build a unified data layer enriched with provenance metadata and relationship context. From there, this trusted data layer can power intelligent application logic, fuel analytics, and support AI-enhanced experiences with confidence.
We’re just beginning to uncover what AI can do—and we’re also just starting to realize the critical role of data visibility and veracity in enabling that future. The next wave of AI isn’t just about what AI can achieve—it’s about how we enable it, responsibly and intelligently.
Learn more in the Whitepaper: Data Trust
