Since GenAI upended traditional workflows, the use of these solutions has now become a standard operating procedure. Nearly 9 out of 10 respondents in McKinsey’s “The State of AI 2025” survey reported implementing AI solutions for at least one business function, while almost 80% are implementing GenAI specifically. Increasingly, autonomous agents that can plan and execute workflows are gaining traction, with nearly 4 in 10 respondents reporting having “experimented” with these solutions and 23% reporting that an agentic AI system is being scaled within their enterprises.  

Yet, scaling successful enterprise adoption of AI is not without its hurdles, and chief among those is data readiness for AI. If your organization is like most, data lives in fragmented, siloed environments that have evolved over years, sometimes decades. Critical information is stored in inconsistent data formats or is spread across disconnected systems that do not communicate with each other. Even the most sophisticated AI systems will falter when poor-quality, fragmented data undermines the performance and reliability of AI models, calling into question the decisions they produce. 

With the rise of decision-centric organizations, decision explainability is imperative for Data and IT leaders. ​​According to Gartner’s CDAO Agenda Survey, 70% of respondents now see themselves as responsible for their organization’s AI strategy and operating model, with three-quarters reporting that their positions would be contingent upon the success of that strategy. This puts them in a tight spot, as AI solutions implemented without data readiness may yield organizational failures, forcing accountability for decisions they may not be fully able to explain or (if necessary) unwind. 

The solution here isn’t to implement “better” AI models, and it certainly isn’t more data. The first step is to create a trusted, governed data foundation, then the only way to implement AI solutions that produce explainable, transparent decisions is by adding context. 

"Context provides organizations with the ability to make better, trusted decisions by grounding AI in data." 

Vishal Marria, CEO and founder, Quantexa 

Understanding the role of context 

Context is one of the most overused words in AI conversations right now. When you hear ‘AI needs context,’ it typically means one of four things:  

1. Prompt context - The instructions and examples you give to a model that shape how it responds. It’s useful, but it only shapes how a model reasons and doesn’t change what it knows.
   

2. Window context - How much text the model holds in memory during a single session. A longer window means the model carries more of the conversation, but it can often feel quite mechanical.  
   

3. Record context - A consolidation of information that AI retrieves when needed, such as customer situation and history. It’s good, but too light for complex decision-making due to its flat structure.  
   

4. Semantic context - Meaning-based similarity across data. This is useful for finding related content, but not for establishing real-world identity or relationships. 
   

5. Real-world context - Entities and relationships, showing who someone really is, how they’re connected, and events that shape their connections.  

At Quantexa, we focus on going beyond record context into real-world context. This is the hardest one to achieve. Real-world context closes the gaps between raw data, insights and impact. It surfaces the real-world connections between individual data points, such as people, places, accounts, and events, to provide the full picture of how entities are related to each other. Context makes isolated facts meaningful by illuminating the real-world situations they represent when viewed together. It provides clarity and transparency.  

Without real-world context, your organization sees an incomplete picture of individuals’ relationships and histories, increasing the risk that AI-driven decisions are based on incomplete or misleading information or are misinterpreted, particularly when multiple entities are involved. Organizations that use data without context see an incomplete picture of the relationships and histories of individuals, organizations, and transactions. This is what makes adding a context layer to a data platform so important. 

"Trusting that the context behind your customer is the right context for you to make decisions on is one of the biggest missing links today."

Shailendra Jain, MD Strategy and Marketing, Moody's 

Why data quality isn’t enough 

For many organizations, data fragmentation and poor quality data are already challenges, preventing users from seeing the full reality behind decisions and hindering AI implementation due to untrustworthy, unexplainable outcomes that may be based on inaccuracies. 

High-quality, accurate data resolves one visibility problem. But even with good data, decisions made without context can easily fail under scrutiny. Producing trustworthy decisions from data is a separate challenge, and context is what makes this possible.  

"Many of our customers are experiencing data fragmentation, lack of quality, and they're struggling to make sense of it. Context glues it together, and ultimately, that context is what allows our customers to make those better decisions." 

Jamie Hutton, Chief Technology Officer, Quantexa 

Consider a credit card fraud detection service. One user shows a sudden spike in purchases: an expensive dinner in a foreign city, a luxury-goods buying spree. These purchases are unusual for this customer, and the fraud detection alerts go off.  

The data is good - these transactions took place, and are indeed unusual for this user. However, context is necessary to determine whether this alert is valid. Has this user’s mobile banking app pinged in a nearby airport? Does this user have a history of travel to this city, or at this time of year? Has this user’s credit card been used to purchase an airline ticket recently?  

In the absence of context, even good data is impossible to interpret, and a course of action is unclear. But real-world context solves the problem; it enables the credit card company’s system to decide on the spot whether to freeze the user’s card or continue approving transactions. 

Context supports accurate risk assessments and situational understanding by connecting entities, events, history and relationships; it provides a clear picture of reality on the ground rather than an isolated snapshot. When that connective layer is present, organizations can move from data that’s technically accurate to decisions that are auditable, transparent and defensible. 

Agentic AI raises the stakes 

The concept “Garbage in; garbage out” may elicit groans from CTOs and CDAOs who have heard it stated one too many times. But it nonetheless remains true. And as agentic AI begins to overtake more processes and workflows across every industry and organization, the risks of utilizing “garbage” data are ever more pronounced.  

Because agentic AI systems act autonomously, replicating decisions made on data (often isolated, without shared context or control frameworks) at greater speed and scale than their predecessors, every gap in decision logic is amplified. Faulty, incomplete or siloed data, the “garbage” in the adage, result in models making opaque decisions that cannot be explained, defended or unwound. Agentic AI compounds an already existing accountability gap when context is absent. 

"The shared context framework is going to be the key to success for agentic AI. Context can enable better decision-making across the board." 

Sam Abadir, Research Director Risk and FCC, IDC 

A contextual data foundation bridges the gap between isolated points of information and real-world understanding. It underpins successful AI use, enabling better, more trusted and more impactful decisions. 

Explainability at the forefront 

Maintaining explainability is of paramount importance as agentic tools take over important business processes. Yet, too many AI models rely on opaque decision logic, leading to CDAO’s who may know what decision was made but not why. For data leaders held accountable for AI outcomes, that’s an unsustainable position. 

"If you can't reason with data, you're making decisions in isolation instead of having a holistic view." 

Vishal Marria, CEO and founder, Quantexa 

When AI is grounded in contextual data, decision logic becomes transparent, auditable and defensible. Organizational leaders can trace how specific entities, events, and relationships contributed to a specific result, and the decision path is clear and trustworthy. 

Concerns about explainability inhibit some organizational leaders from implementing AI. In a 2024 McKinsey survey, 40% of business leaders cited explainability as a key risk in adopting AI. This makes sense; leaders are understandably wary of rolling out and scaling systems that may make decisions more opaque or harder to account for. 

Context is the answer. The capacity to concretely show how specific inputs led to specific outputs at every stage of the decision cycle requires a contextual, real-world view of the baseline data. Trust in AI grows when people can understand every step of the decision-making path, and context is what makes explainability possible. 

A look ahead 

For decisions to be grounded, defensible and trusted, even the best data and the most sophisticated agentic AI aren’t enough. Context delivers the missing piece of the puzzle, bringing disparate data sources together into a real-world picture that AI models can use to produce accurate, explainable and strategically sound outputs.  

The next challenge is operationalizing AI ​​at an enterprise scale, where decision intelligence runs consistently across workflows and jurisdictions. That’s the objective we’ll be exploring in the next article in this series. 

Watch QuanCon On Demand to explore how context-driven decision-making is reshaping AI strategy for data leaders.