Presentation Type
Article
Location
Kennesaw, Georgia
Start Date
1-4-2026 12:30 PM
End Date
1-4-2026 1:45 PM
Description
For over 40 years enterprise architecture has promised to align technology investment with organizational strategy. Just a portion of that promise has been fulfilled. Extensive documentation or artifact repositories that describe the enterprise in detail but hardly ever influence its direction in any discernible way has been the field's main output. This paper begins with that diagnosis which was most accurately expressed by Tamm et al. and in the context of the public sector by Dang and Pekkola and inquires as to whether artificial intelligence can solve the underlying structural issue instead of just speeding up the documentation process. We contend that it can but only if the integration is theoretically based as opposed to feature driven. Using necessity arguments rather than design preferences we construct the AI-Driven Enterprise Architecture and Value Realization Framework (AI-EA-VRF). Based on Teece's Dynamic Capabilities theory, Henderson and Venkatraman's Strategic Alignment Model, and Weill and Ross IT governance framework we demonstrate that precisely four components, a Strategic Alignment Engine, an AI Decision Intelligence Module, an Architecture Governance Layer, and a Value Realization Dashboard and that eliminating or collapsing any of them would violate at least one fundamental theoretical premise. Based on Cohen and Levinthal's absorptive capacity construct five-level Organizational Maturity Adoption Model outlines quantifiable entry requirements at each level. A specific empirical research agenda is defined by seven falsifiable hypotheses. Additionally, the paper offers a theoretical rebuttal that pinpoints the boundary conditions under which the competing explanation that well-resourced manual governance can achieve equivalent outcomes fails.
AI-Driven Enterprise Architecture and Value Realization Framework
Kennesaw, Georgia
For over 40 years enterprise architecture has promised to align technology investment with organizational strategy. Just a portion of that promise has been fulfilled. Extensive documentation or artifact repositories that describe the enterprise in detail but hardly ever influence its direction in any discernible way has been the field's main output. This paper begins with that diagnosis which was most accurately expressed by Tamm et al. and in the context of the public sector by Dang and Pekkola and inquires as to whether artificial intelligence can solve the underlying structural issue instead of just speeding up the documentation process. We contend that it can but only if the integration is theoretically based as opposed to feature driven. Using necessity arguments rather than design preferences we construct the AI-Driven Enterprise Architecture and Value Realization Framework (AI-EA-VRF). Based on Teece's Dynamic Capabilities theory, Henderson and Venkatraman's Strategic Alignment Model, and Weill and Ross IT governance framework we demonstrate that precisely four components, a Strategic Alignment Engine, an AI Decision Intelligence Module, an Architecture Governance Layer, and a Value Realization Dashboard and that eliminating or collapsing any of them would violate at least one fundamental theoretical premise. Based on Cohen and Levinthal's absorptive capacity construct five-level Organizational Maturity Adoption Model outlines quantifiable entry requirements at each level. A specific empirical research agenda is defined by seven falsifiable hypotheses. Additionally, the paper offers a theoretical rebuttal that pinpoints the boundary conditions under which the competing explanation that well-resourced manual governance can achieve equivalent outcomes fails.
