How the right context can reshape agentic AI's productivity output

Imagine a scenario where an AI-driven sales agent authorizes a discount to retain an unhappy customer, while simultaneously, a finance AI flags accounts for potential credit risks. On another front, a supply chain AI reorders inventory based on projected profit margins. Although each of these actions is justified individually, they can lead to conflicting outcomes when combined. This illustrates the challenges of agentic AI, which is becoming increasingly prevalent as businesses transition from basic chatbots to more sophisticated autonomous systems that manage workflows across various sectors, including finance, supply chain, marketing, and customer service. The potential economic benefits of agentic AI are immense, with analysts predicting a productivity revolution worth trillions. However, the absence of context can lead to operational friction on a large scale. The core issue isn’t the intelligence of these systems but rather the context in which they operate. A recent survey by Harvard Business Review, sponsored by Reltio, revealed that while 94% of organizations are exploring AI initiatives, only 15% feel their data infrastructure is adequately prepared for agentic AI. This gap can help explain why some companies see substantial returns while others do not, with 60% reporting little to no impact from their investments. The disparity often arises from the fragmented ecosystems in which these AI agents function. Many agents only have access to partial data, as customer records, product information, operational histories, and financial systems frequently do not align in real-time. This lack of a complete picture can lead to inconsistent outcomes. Historically, enterprises have focused on improving data quality, such as eliminating duplicates and enhancing governance. While these efforts are important, they fail to address a more profound issue: the absence of context. Current systems of record document what has occurred but usually do not explain why. For example, when a manager grants a 20% discount following a service outage, the rationale for this decision may reside in an email or a Slack message, becoming a mere data point later without the surrounding narrative. This disconnect can lead to misinformed decisions by AI agents. To function effectively, agents require more than just accurate data; they need a dynamic context that maps relationships and decision-making processes, linking customers to products and actions to intents. Unfortunately, many organizations are burdened by data debt due to decades of system fragmentation. Marketing databases often fail to align with support systems, while supplier information is frequently duplicated across departments, and financial data can lag behind operational realities. The persistent issue of data silos stands as the primary obstacle to AI advancement, with 46% of leaders identifying it as a barrier. Simply introducing more agents into these fragmented systems exacerbates the problem, as each agent constructs its own incomplete version of reality. The organizations poised to lead in the coming decade will not be those with the highest number of agents but rather those that ensure their agents operate from a unified and trustworthy context. Achieving this shift necessitates infrastructure designed for integration, utilizing platforms that connect core data and metadata in real-time, establishing a comprehensive understanding of entities and relationships across the organization. As autonomous systems become more integrated into business operations, it will be up to leaders to determine whether their organizational intelligence will enhance or conflict.