Unheralded Asset in Trustworthy AI: Data Fluidity
In the rapidly evolving world of artificial intelligence (AI), one factor stands out as crucial for building trust and driving success: data liquidity.
Ed Macosky, the Chief Product and Technology Officer at Boomi, a leading cloud-based integration platform as a service (iPaaS) provider, underscores this point. AI, he explains, thrives on well-organized data that is dynamic, clean, and contextual, flowing effortlessly across systems and teams.
Data liquidity, the ability to bring the right data to the right place at the right time, is a key aspect of this. In dynamic environments, delays in data can lead to missed opportunities or misinformed decisions. On the other hand, real-time responsiveness is becoming a baseline requirement for data, ensuring it flows where and when it's needed to power AI that can adapt on the fly.
The quality of data behind AI will become increasingly important, especially in customer-facing experiences. High-quality data for AI should be accurate, context-rich, policy-compliant, and continuously updated. Trusted and usable data requires governance, which includes having a clear view of where data lives, who can access it, and how it's being used, supported by policies and controls that keep information secure, consistent, and compliant.
Modernizing integration across the enterprise is crucial for achieving data liquidity. Cloud-native iPaaS solutions offer organizations the ability to connect fragmented systems and automate data flows, facilitating the seamless movement of data.
Tracking progress towards data liquidity requires asking questions about data accessibility, system compatibility, speed of new data source connections, and data consistency, completeness, and updates. Achieving data liquidity is not just about operational hygiene; it's an early indicator of an organization's readiness to support AI at scale.
The critical shift in AI adoption is the focus on data agility as the new battleground for competitive advantage. In a world where AI agents must act autonomously, data liquidity is considered a core competitive advantage, not just intelligence.
For instance, an AI-powered investment assistant with high-quality, real-time data can offer trustworthy guidance and decisions. This is especially important as AI agents become more prevalent, acting confidently, in context, and at speed with well-organized data.
However, the journey towards data liquidity is not without challenges. Gartner analysts predict that more than 40% of agentic AI projects will be canceled by the end of 2027 due to factors like unclear ROI, technical complexity, and insufficient risk controls.
Despite these challenges, the benefits of data liquidity are clear. It forms the foundation for automation that's not only intelligent but also trustworthy, setting the stage for AI to transform businesses and society as we know it.
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