Context has become the new ‘big data’

For much of the last two decades, organizations operated under the assumption that collecting more data would naturally produce better decisions. Massive investments were made in data warehouses, reporting platforms, analytics systems and business intelligence tools. Those investments created tremendous value, but they also exposed an important reality: Collecting information and creating clarity are not the same thing. Today, AI is heading down a similar path. Many enterprise AI projects measure progress by counting how much information a model can access.

More documents become better than fewer documents. More systems become better than fewer systems. Larger context windows become better than smaller ones. The conversation often assumes that quantity and quality move together. According to Salesforce research, only 35% of business leaders say they are completely satisfied with their organization’s ability to use data effectively despite years of investment in data infrastructure and analytics.

Enterprises learned long ago that information alone does not create understanding. The same lesson applies to AI. When a model gains access to five versions of the same metric, conflicting definitions of a business process or documentation that has not been updated in years, it does not magically resolve those inconsistencies. It consumes them. More context can just as easily increase ambiguity as reduce it.

Simply exposing more information to a model does not guarantee better outcomes. What matters is whether the information available to the system helps it make the right decision at the right time.