Cross-Disciplinary Collaboration: Bridging Management and Computer Engineering for Innovation

Abstract

Background: The integration of management and computer engineering has become a crucial driver of AI innovation, allowing organizations to bridge technical expertise with strategic leadership for sustained technological advancements. Effective interdisciplinary collaboration enhances AI adoption by aligning engineering-driven developments with business objectives and regulatory frameworks.

Objectives: This study examines AI-driven interdisciplinary collaboration through three case studies: Google, Tesla, and Singapore’s Smart Nation Initiative. It investigates how corporate, industry-led, and policy-driven AI models influence technological scalability, governance, and economic impact.

Methods: A case study analysis was conducted to assess how Google, Tesla, and Singapore leverage AI in distinct organizational settings. Google’s AI research follows a corporate-led commercialization strategy, integrating deep learning and NLP to generate over $200 billion in annual advertising revenue. Tesla’s vision-based Full Self-Driving (FSD) technology, combined with a subscription-based model, exemplifies industry-led AI disruption in autonomous mobility. Singapore’s Smart Nation Initiative, a policy-driven AI governance model, integrates AI and IoT solutions, reducing traffic congestion by 20% and cutting waste management costs by 15%.

Results: Findings indicate that AI deployment success relies on effective cross-functional collaboration between technical developers, business strategists, and policymakers. Corporate and industry-driven AI programs focus on commercialization and market leadership, while policy-driven AI models emphasize regulatory oversight and public-sector cooperation. The results highlight how adaptive AI governance, responsible AI implementation, and dynamic collaboration frameworks are essential for overcoming AI adoption challenges across private and public sectors.

Conclusion: The insights from this research provide a strategic foundation for business leaders, policymakers, and researchers to develop AI systems that align technological innovation with governance, ethics, and societal impact. By fostering interdisciplinary collaboration, organizations can maximize AI potential while ensuring compliance, ethical standards, and long-term sustainability.