While a single source of truth for engagement data is elusive, acknowledging universal engagement principles can transform organizational efficiency and communication.

Companies face challenges when combining data from different sources due to varying definitions of terms like "lead" across departments, leading to misunderstandings and inefficiencies; a better approach than seeking a single source of truth is to focus on detailed, consistent definitions understood by all teams.

The most efficient way to provide AI with the detailed context it needs is by collecting information as it is created. While these changes impact everyone in an organization, enacting them is the biggest barrier. Three guidelines to minimize pain and maximize value include externalizing descriptions to manage change effectively, thinking slow and acting fast to gather insights and mitigate risks, and searching for small victories to build momentum and demonstrate benefits.

In this post, I outline a strategy for efficiently capturing engagement data by integrating data collection into the creation process, leveraging digital tools for automation. This approach ensures accurate, comprehensive data without additional manual effort, facilitating better downstream decisions and maintaining data quality.

The article discusses a data framework that breaks down customer engagements into six facets (Who, What, Where, When, Why, and How), each divided into categories and sub-categories, totaling fewer than 500 elements to describe any engagement comprehensively. This detailed approach leverages emergent properties to simplify the data ecosystem and enhance the effectiveness of automation tools like AI in managing customer relationships.

Building a shared engagement architecture involves capturing clean, consistent metadata to power AI and automation tools. This article delves into three key solutions: implementing a simple, shared data framework, using tools and services to capture and share context, and developing rollout plans that provide immediate and long-term value, particularly for customer-focused business disciplines like Sales, Marketing, Product Development, and Service.

Humans naturally communicate context in complex, nuanced ways that AI cannot easily interpret. To ensure AI systems receive the specific and unambiguous context they need, companies must adopt a simple, consistent data framework and tools designed to capture and share detailed context efficiently.