Ali Behnam, founder at Tealium.
For much of the AI boom, the industry has focused on scale: the latest frontier models, more data, bigger and more sophisticated computing infrastructure.
Those investments have produced remarkable advances. But as AI capabilities become increasingly accessible, organizations face a different question: If everyone has access to the same models and capabilities, where will competitive advantage come from?
The answer may not be the model or the infrastructure. It will be the data and the context surrounding it.
An AI model doesn’t inherently understand your customers, your business priorities, your operating environment or what happened five minutes ago. That context is unique to every organization, and increasingly, it will be what separates generic AI from AI that creates real business value.
Data Without Context Has Limits
Over the years, businesses have invested heavily in accumulating customer and operational data. These span from purchase history, behavioral data, customer conversations and interactions, inventory, pricing and more. Whether these data points sit in different systems or are stored in a modern customer data warehouse, their value won’t come to fruition until there is situational awareness.
Imagine two customers searching for the same product on a retailer’s website. The first is a new visitor who arrived through an advertisement and is researching the product for the first time. The second is a longtime customer who purchased that exact product three days ago and is now searching for help installing it. The action is identical. The intent is completely different. Situational awareness fuels the right customer experience.
The goal isn’t simply to give AI more information. It’s to give it the right information for the situation at hand. It’s to give AI the right context.
Context Has A Shelf Life
One aspect that organizations often overlook is that context is not static. It changes over time, and some context loses its relevance faster than others.
A customer’s purchase history may be useful, but what they’re doing right now outweighs all the historical records. Netflix provides content recommendations based on what the subscriber has watched over the last couple of weeks or months, not what they watched 10 years ago.
Consider a customer who spends several days researching a new dishwasher. Their searches, product comparisons and visits to review pages provide valuable signals about what they want to buy. But the moment they purchase a dishwasher, the value of the data declines. Continuing to recommend dishwashers based on previous week’s activity is no longer personalization. It’s wasted messaging.
This becomes increasingly important as AI moves from answering questions to taking actions. An AI assistant making recommendations will need to know what happened minutes or even seconds ago. Organizations need a context layer that continuously evolves as new interactions, transactions and signals change the situation AI is being asked to understand.
Context Reduces AI Costs
Giving AI better context doesn’t simply improve the quality of its answers or recommendations. Done properly, it reduces the amount of information AI agents need to process. It improves response times, reduces the unnecessary model calls and lowers the overall cost of running AI systems.
One common assumption in today’s AI deployments is that feeding it more information will provide better results. This is both wrong and costly.
Large language models (LLMs) process the information provided to them as tokens. The more information is included in the prompt or is retrieved in the model’s context window, the more information the system needs to process. If any of that information isn’t relevant to the decision, you’re effectively paying AI to find the signal inside the noise. Context reduces the noise and lets AI systems focus on the right signal.
And this benefit is only amplified when it comes to agentic AI.
Traditional AI applications may make one model call to do an analysis. Agents can make many. An agent may interpret a request, decide which tools to use, query multiple systems, evaluate the results, determine its next action, call another model and repeat the process until the task is complete. Every step adds token consumption. With the right context on the other hand, the agent can focus on the actual task instead of reconstructing the customer’s situation.
Context Is Your Competitive Advantage
As AI models become more capable and widely adopted, the capabilities themselves will become less differentiating. Competitors will have access to many of the same models, infrastructure and AI tools. They can deploy similar agents and build similar applications.
What they can’t duplicate is the context surrounding your business and customers.
They don’t have the history of interactions with your customers. They don’t know how your customers interact with your marketing offers and what their shopping preferences are. That context is proprietary to your organization.
This changes how businesses should think about AI differentiation. The advantage will not come from having the most powerful models or feeding them the largest amount of data. It comes from how effectively an organization can turn its own data into relevant context, keep it current and make it available when AI needs it.
Models will continue to improve. Computing costs will continue to fall. AI capabilities that seem differentiated today will eventually become widely available.
Your business and customer context won’t.
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