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GenAI Copilot as an “Innovation Operating System”: Controls, Learning Loops, and Integration Prerequisites

by Basil Obute, Kingsley C. Ugwu, Nzeribe A. Okeh
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 13 - Number 2
Year of Publication: 2026
Authors: Basil Obute, Kingsley C. Ugwu, Nzeribe A. Okeh
10.5120/ijaisfa260523eed8

Basil Obute, Kingsley C. Ugwu, Nzeribe A. Okeh . GenAI Copilot as an “Innovation Operating System”: Controls, Learning Loops, and Integration Prerequisites. International Journal of Applied Information Systems. 13, 2 ( May 2026), 79-94. DOI=10.5120/ijaisfa260523eed8

@article{ 10.5120/ijaisfa260523eed8,
author = { Basil Obute, Kingsley C. Ugwu, Nzeribe A. Okeh },
title = { GenAI Copilot as an “Innovation Operating System”: Controls, Learning Loops, and Integration Prerequisites },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2026 },
volume = { 13 },
number = { 2 },
month = { May },
year = { 2026 },
issn = { 2249-0868 },
pages = { 79-94 },
numpages = {9},
url = { https://www.ijais.org/archives/volume13/number2/genai-copilot-as-an-innovation-operating-system-controls-learning-loops-and-integration-prerequisites/ },
doi = { 10.5120/ijaisfa260523eed8 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-05-04T23:57:53.135908+05:30
%A Basil Obute
%A Kingsley C. Ugwu
%A Nzeribe A. Okeh
%T GenAI Copilot as an “Innovation Operating System”: Controls, Learning Loops, and Integration Prerequisites
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 13
%N 2
%P 79-94
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Enterprise GenAI copilot programs most commonly fail not because of poor model capabilities, but because businesses lack the necessary operating system for integrating and managing the key elements of a GenAI copilot, including: (i) data lineage and data retrieval provenance, (ii) tool integration and access control, (iii) governance-as-code (i.e. the ability to define and manage business rules through code), (iv) end-to-end traceability and approval processes, (v) learning loops (the ability to utilize and measure user activity and incidents as a means of improving the overall capability of GenAI). Drawing on sociotechnical systems, innovation systems, and Responsible AI research, we synthesize these into a 5-layer Innovation Operating System (IOS) and propose five falsifiable propositions (P1–P5) examining how IOS maturity, governance density, and learning loop maturity affect enterprise GenAI copilot performance. The study provides a reference implementation measured by: (a) IOS layer maturity, (b) a task-class governance density index, and (c) three performance proxies - Innovation Adoption Rate, Control Incident Frequency, and Retrieval Robustness Score. A replication package for this study includes a blueprint for all elements (schemas, queries, rubrics, notebooks, and a synthetic log generator).

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Index Terms

Computer Science
Information Sciences

Keywords

Generative AI Copilot Information Systems Governance Data Lineage Traceability Evaluation Design Science Responsible AI

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