General Endeavor Management (GEM)

I am still contemplating the enterprise, building an enterprise knowledge graph, contemplating a theory for the enterprise, and connecting the enterprise to what I see as the "skeleton of the enterprise knowledge graph" which is the accounting information systems and compliance reporting.

It seems to me that the notion of general endeavor management (GEM) is part of this. Roy Roebuck has created what he refers to as Roebuck General Endeavor Management (RGEM). Here is a primer which is available on Amazon.com. Here is a book that explains Roebuck's framework for GEM.

General Endeavor Management (GEM) is a theoretical and operational framework designed to model, organize, and execute complex endeavors using unified systems architecture and knowledge representation.  An enterprise is effectively an endeavor.

Unlike traditional management models like the Zachman Framework, TOGAF, ArchiMate, EACOE that separate distinct business functions; such as operations, finance, strategy, and IT; GEM approaches any project, organization, or initiative as an interconnected system of entities, relationships, dependencies, and objectives. The following are the core concepts of GEM:

  • Holistic Endeavor Modeling: GEM treats every endeavor (from a startup project to enterprise architecture or space exploration) as a single, dynamic knowledge model.
  • Knowledge Representation: Instead of relying solely on unstructured narrative descriptions or isolated departmental spreadsheets, GEM relies on structured knowledge graphs to represent relationships between roles, resources, constraints, and outcomes.
  • Domain-Agnostic Abstraction: The underlying logic of GEM applies universally whether managing a software build, a supply chain, or organizational transformation.
In systems architecture and knowledge modeling, Roy Roebuck expanded on GEM concepts by developing RGEM (Roebuck’s General Endeavor Management framework). Roebuck’s work; spanning decades at the intersection of physics, systems engineering, and management; focuses on transforming endeavor management into a computable graph model.

My objective is to figure out how to maximize the potential of artificial intelligence, use global open industry standards where possible in systems where there is zero tolerance for error.

The fundamental difference between GEM and TOGAF/ArchiMate/Zachman/EACOE is that GEM is a universal, cross‑domain management discipline for orchestrating and managing endeavors whereas TOGAF/ArchiMate/Zachman/EACOE are enterprise architecture frameworks for describing and governing enterprise structure. 

GEM is about how to manage, whereas TOGAF/ArchiMate/Zachman/EACOE are about how to architect. GEM relates to helping management answer strategic, operational, tactical, mission, program, project questions.  TOGAF/ArchiMate/Zachman/EACOE helps an organization describe enterprise structures and relationships.

It seems to me that GEM has the notion of an "engine" that actually helps leadership, management, administrators, and operations perform work and achieve their enterprise mission/mandate. GEM also helps endeavors define and evaluate based on intelligence gathered.

Content (Current Disorganized Chaos)  >>  Framework  >>  Model  >>  Engine

What I am trying to figure out is the important standards and frameworks that can be used to interconnect the enterprise "stack". That might include interconnecting with other endeavors outside one's own endeavor/enterprise.  I need to do this in an environment that has zero tolerance for error because the core system, the accounting information systems used for compliance reporting, tax reporting, and management/cost accounting have zero tolerance for error.

I am looking to do this with an AI-First, Digital-First, Graph-First mindset. I acknowledge that there needs to be an effective path to get from the current legacy systems paradigm to the new paradigm; but I don't want to sacrifice the potential upside by making a fundamental or foundational mistake which cannot be fixed.  I want to avoid creating a new kludge to replace the current kludge.

I am looking for a practical approach to using knowledge graphs in practice in an enterprise environment where there is zero tolerance for error and maximizing to possibilities of artificial intelligence "done right".  This is what I currently understand. (Accumulated over 25 years.) Please email me if you think I am seeing something incorrectly.

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