Ed Macosky, Chief Product and Technology Officer at Boomi, a leading provider of cloud-based integration platform as a service (iPaaS).
Your AI might be cutting-edge. But is it trustworthy?
Gartner analysts expect that more than “40% of agentic AI projects will be canceled by the end of 2027,” citing factors like unclear ROI, technical complexity and insufficient risk controls. Often, that lack of control is rooted in disconnected, incomplete or ungoverned data, leaving AI agents to make decisions that are misleading at best and reputation-damaging at worst.
In a world where AI agents must act autonomously, the real competitive advantage isn’t just intelligence—it’s data liquidity.
Data Liquidity: Why Does It Matter Now?
As agentic AI matures, the expectations around data readiness are rising fast. When AI projects fall short, it’s often not the model that’s broken but the data ecosystem it depends on.
That’s why data liquidity, which refers to the ability to bring the right data to the right place at the right time, is emerging as a core differentiator. Data liquidity is what separates operational AI from transformative AI because it forms the foundation for automation that’s not only intelligent but also trustworthy.
What Does High-Quality Data Look Like For AI?
AI agents, especially those designed to operate autonomously, rely on high-quality data that’s not just accurate but context-rich, policy-compliant and continuously updated. When data is well-organized, AI agents don’t waste time resolving mismatches or reconciling incomplete inputs—they act confidently, in context and at speed. It’s this level of precision that turns data into a trustworthy asset, not a liability.
Consider this: If an AI-powered investment assistant recommends a high-risk portfolio to a risk-averse customer, the issue likely stems from incomplete or outdated user data. As AI becomes embedded in more customer-facing experiences, trust will depend less on how advanced the model is and more on whether the data behind it is accurate.
Now, imagine that same assistant powered by high-quality, real-time data: a continuously updated view of the customer’s complete financial picture, including risk tolerance, investment history and current portfolio. Instead of recommending a high-risk product, the assistant offers a diversified strategy aligned with the customer’s preferences. With the right data behind it, AI delivers trustworthy guidance and decisions.
How To Achieve Data Liquidity
Achieving data liquidity isn’t a switch you flip—it’s a strategic transformation that touches systems, people and processes. It starts with removing the barriers that prevent information from moving freely across the business.
One of the most impactful ways to begin is by modernizing integration across the enterprise. Cloud-native integration platform as a service (iPaaS) solutions can play a role here, offering organizations the ability to connect fragmented systems and automate data flows.
But connectivity alone isn’t enough. Trusted and usable data requires governance. That means 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.
Speed also matters. In dynamic environments, delays in data can lead to missed opportunities or misinformed decisions. That’s why real-time responsiveness is becoming a baseline requirement, ensuring data flows where and when it’s needed to power AI that can adapt on the fly.
Tracking progress toward data liquidity requires asking the right questions. How easily can teams access critical data? How well do different systems work together? How quickly can new data sources be connected? And is the information consistent, complete and updated?
These questions are more than operational hygiene—they’re early indicators of how ready your organization is to support AI at scale. If teams still rely on manual exports, workarounds or stale reports, chances are your AI efforts will lag too.
The Road Ahead
As organizations embrace agentic AI, a critical shift is underway: Model sophistication is no longer the sole focus. Data agility has become the new battleground for competitive advantage.
Trusted AI can’t be built on static infrastructure. It requires dynamic, clean and contextual data that flows effortlessly across systems and teams.
If your AI systems had to make a high-stakes decision in real time, could your data deliver? Could a fraud detection system recognize that a customer is on vacation before mistakenly denying their international transactions? Could a supply chain agent reroute inventory mid-shipment based on weather alerts or demand spikes?
Because in the end, AI doesn’t just need data. It needs liquid data. And that’s the difference between automation and transformation.
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