Organizations today have more data than ever before, which should lead to smarter AI, more reliable decisions, and clearer accountability. But McKinsey's State of Organizations 2026 survey tells a different story: 88% of companies are implementing AI-driven tools, yet 81% report no meaningful bottom-line gains. There’s a growing gap between the investment in AI and the ROI.  

At the root of that gap sits a data problem.  

As the age-old saying goes: ‘garbage in, garbage out’. Without high-quality and perhaps more importantly, trusted data, even the most sophisticated AI models produce outcomes that can't be fully explained or acted on with confidence.  

The risks of a poor data foundation 

Every AI-driven decision an organization makes is shaped by the data behind it. When that data is fragmented, incomplete, or opaque, no decision built on it can be fully explained or defended, regardless of how sophisticated the AI behind it. 

Craig McKeown, Partner at PwC explains: “The capability of AI is remarkable, but if you’re loading poor quality data into AI models or tools, then the outcome that you get won’t be great.” 

Many enterprises are still operating with legacy data architectures: customer records in one system, transactions in another, while communications and external data are siloed elsewhere. These data siloes may be adequate for reporting, storage, and compliance, but as AI acts faster and with greater autonomy, the risks from fragmented or inaccurate data become far more pronounced, and errors scale across the organization before anyone can intervene. 

The consequences are material across sectors. In insurance, for example, fragmented data across claims, fraud, and underwriting can prevent accurate risk pricing, leading to lost customers or underpriced risk that damages loss ratios. In the public sector, disconnected data across departments can lead to individuals being assessed on incomplete or outdated information, with real consequences for service delivery, compliance, and public trust. 

The solution enabling successful AI-driven decisions is to start by creating a unified, trusted, contextual data foundation. 

How a trusted, contextual data foundation enables decision velocity 

A contextual data foundation unifies fragmented data across your internal and external sources, makes lineage and ownership explicit, applies consistent governance across structured and unstructured data, and adds context so that data reflects real-world relationships rather than isolated records. In short, it makes your data trustworthy and AI-ready. And, it ensures AI-driven operations are accurate, explainable, and capable of supporting confident decisions. 

The ‘contextual’ part is really important. Context is what evolves a simple static data repository into a governed data foundation that reflects real-world information. It's what enables you to understand how people, businesses, accounts, transactions, and events connect over time. That's the essential ingredient for moving from raw data to meaningful insight, and from insight to confident, defensible action. You can find out more about how to build context here.  

Decision velocity is the speed and confidence with which decisions can be made, acted on, and scaled across an organization. When models are trained on accurate, governed data, outcomes are more explainable, and decisions are easier to account for and to unwind when needed. A unified contextual data foundation reduces friction at every stage: less time reconciling records, validating outputs, or manually reviewing AI-generated recommendations. The result is better decisions, made faster, with the confidence to act on them at scale. 

How Quantexa enables a trusted, contextual data foundation 

At Quantexa, we help organizations move from fragmented data to decision-ready insight powered by two interconnected core capabilities, dynamic Entity Resolution and Knowledge Graphs. 

Entity Resolution connects records that refer to the same real-world entity across different systems and data types, creating a single, reliable view of people, organizations, and assets. This resolves the duplication and ambiguity that undermine data quality and the AI built on top of it. Knowledge Graphs add context by modeling the relationships between those entities: how customers connect to accounts, how transactions relate to behavior, and how risks propagate across networks. 

Together, these capabilities create an accurate picture of data as it exists in the real world, giving AI and analytics the foundation they need to scale with confidence. 

“With the Quantexa platform, we [help our clients] extract key information from documents and unstructured data that provides rich information. By combining disjointed, fragmented data sets together and understanding the context and relationships, we’re helping clients solve a number of problems that were previously very difficult or expensive.”  

Craig McKeown, Partner at PwC 

A look ahead 

With a unified, governed, contextual data foundation in place, your organization can move faster and more accurately, surfacing unseen risks and hidden opportunities, reducing time spent on manual review, and shortening the distance between data, insight, and action. 

A high-quality, trusted data foundation is only the first step to effective decision-making. The next question, increasingly urgent as AI models are embedded in consequential decisions, is whether organizations can trust and explain why a decision was reached. Even with accurate data, AI-driven decisions can fail under scrutiny when the reasoning behind them can’t be traced or defended. Real-world context is what makes that reasoning visible, and it’s the subject we’ll explore in the next post in this series.  

Watch QuanCon On Demand to explore how trusted data foundations support confident, explainable decisions in the age of AI.