Enterprise AI Analysis: Translating Academic Models into Corporate Strategy

Based on the research paper: "Encouraging Students' Responsible Use of GenAI in Software Engineering Education: A Causal Model and Two Institutional Applications" by Vahid Garousi, Zafar Jafarov, Aytan Mövsümova, Atif Namazov, and Hüseyn Mirzyev.

Executive Summary: From Classroom to Boardroom

The proliferation of Generative AI (GenAI) tools like ChatGPT presents a dual-edged sword in the enterprise. While offering unprecedented potential for efficiency, there's a significant risk of employees using these tools as a crutch, leading to a decline in critical thinking, problem-solving skills, and overall work quality. The research by Garousi et al. provides a powerful academic framework for tackling this exact challenge within software engineering education. At OwnYourAI.com, we recognize that the paper's core findings and its "Causal Model" are not just for students; they are a direct blueprint for any organization seeking to foster a culture of responsible, effective, and innovative AI adoption.

This analysis translates the paper's educational model into an actionable enterprise strategy. We'll deconstruct their causal model to show how managerial actions, employee behaviors, and AI tool characteristics interact to drive business outcomes. We'll then provide practical blueprints for implementing these principles to revamp existing workflows and design new corporate upskilling programs. By moving beyond simple "dos and don'ts" and adopting a structured, theory-informed approach, your organization can empower its workforce to use GenAI not just to complete tasks, but to innovate, improve quality, and build sustainable competitive advantages.

Deconstructing the Causal Model for Enterprise Use

The paper's most significant contribution is a causal model illustrating the factors that influence responsible GenAI use. We've adapted this model for an enterprise context, replacing academic terms with their corporate equivalents. This framework helps leaders understand the levers they can pull to shape AI adoption within their teams.

Enterprise Application Blueprints: From Theory to Practice

The paper's authors applied their model in two distinct educational settings. We've translated these applications into two enterprise blueprints that you can adapt for your own organization. These blueprints demonstrate how to move from a high-level model to concrete, on-the-ground interventions.

Interactive ROI & Value Analysis

Adopting a structured GenAI policy isn't just about risk mitigation; it's about unlocking tangible value. While precise ROI depends on your specific context, this calculator, inspired by the paper's focus on skill enhancement and quality, helps estimate the potential gains. The model suggests that moving employees from passive AI users to active validators reduces errors and improves the quality of work, leading to less rework and higher efficiency.

A Governance Framework for Responsible AI Adoption

Based on the interventions detailed in the research, we've created a starter governance framework. This isn't a restrictive policy document, but a set of guiding principles to build a culture of critical engagement with AI.

Interactive Knowledge Check

Test your understanding of the core principles for enterprise GenAI governance derived from the study.

Conclusion: Build Your AI-Powered Future, Responsibly

The research by Garousi et al. provides a clear, evidence-based roadmap for navigating the complexities of GenAI in a professional setting. By translating their academic model into an enterprise framework, we can see that the path to successful AI integration is not about banning tools, but about building skills, refining processes, and fostering a culture of critical thinking. The key is to treat GenAI as a powerful collaborator that requires human oversight, validation, and direction.