Customer-based Context Engineering is crucial as AI amplifies alignment and misalignment between systems and human context, particularly for mid-market and growing SMB companies competing with more technically advanced players.
The approach involves mapping, indexing, and exposing customer context across all systems to create coherence without requiring system replacement.
By treating the customer as the primary organizing principle, systems can operate from a shared understanding of the customer's perspective.
This method enhances the performance of systems by linking customer information across different platforms, leading to more efficient operations.
Customer Data Platforms (CDPs) and CRMs are effective but work above business systems, while customer context engineering delves deeper to integrate information across all systems.
A case study involving Spectrum Enterprise showed remarkable results after implementing customer-based context engineering, leading to increased adoption, decreased support calls, and unified customer data.
The five core disciplines identified for finding and codifying customer context include service blueprinting with data anchors, customer data modeling, data interrogation and cleanup, indexed data layer, and API & AI agent interface enablement.
This approach ensures that systems can reflect how customers think and behave, enhancing efficiency in support, sales, and overall business operations.
Customer-based context engineering mitigates the risk of AI failures due to incomplete or misaligned data, offering strategic benefits such as improved customer value measurement.
Implementing customer context work requires cross-functional collaboration and executive support to ensure alignment across organizational boundaries for enhanced customer experience and operational efficiency.
Aligning systems with human context not only improves design but also enhances infrastructure, providing companies with structural advantages that create lasting value over time.