Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams

• Author(s): Favour Uche Ojika ; Wilfred Oseremen Owobu ; Olumese Anthony Abieba ; Oluwafunmilayo Janet Esan ; Bright Chibunna Ubamadu; Andrew Ifesinachi Daraojimba
• Paper ID: 1702874
• Page: 439-453
• Published Date: 31-07-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 5 Issue 1 July-2021

Abstract

In today’s fast-paced digital landscape, Agile teams face increasing pressure to deliver high-quality products rapidly and efficiently. While Artificial Intelligence (AI) has been integrated into various software development processes, its optimization for enhancing cross-functional collaboration remains underexplored. This study presents a comprehensive framework for optimizing AI models to support cross-functional collaboration, ultimately improving product roadmap execution in Agile environments. The proposed framework leverages natural language processing (NLP), machine learning (ML), and reinforcement learning (RL) techniques to enhance communication, knowledge sharing, and decision-making among development, design, marketing, and operations teams. The research begins by identifying key collaboration bottlenecks that impede roadmap execution, including misaligned priorities, fragmented information flow, and delayed feedback loops. Based on these insights, the framework integrates AI-powered tools to automate backlog grooming, prioritize tasks dynamically, and provide context-aware recommendations aligned with evolving team objectives. It also includes AI-driven dashboards that visualize dependencies and predict delivery timelines, thereby facilitating transparency and accountability across teams. A mixed-method approach was employed to evaluate the framework, combining qualitative feedback from Agile practitioners with quantitative metrics such as cycle time reduction, sprint goal attainment, and stakeholder satisfaction. The results demonstrated significant improvements in coordination efficiency, faster decision-making, and better alignment between team outputs and strategic goals. This research contributes to the intersection of AI and Agile methodology by presenting a novel approach to AI model optimization tailored for collaborative, multi-disciplinary product development. By embedding intelligence into routine Agile rituals—such as sprint planning, retrospectives, and stand-ups—the framework ensures that insights are shared in real-time, actions are data-informed, and value delivery is continuous. The findings emphasize the importance of adaptive AI systems that evolve with team dynamics and product complexities. The proposed framework serves as a blueprint for organizations seeking to enhance Agile maturity and product innovation through intelligent automation and AI-enabled collaboration.

Keywords

Agile Teams, Artificial Intelligence, Cross-Functional Collaboration, Product Roadmap, Machine Learning, Natural Language Processing, AI Optimization, Team Coordination, Agile Maturity, Product Development.

Citations

IRE Journals:
Favour Uche Ojika , Wilfred Oseremen Owobu , Olumese Anthony Abieba , Oluwafunmilayo Janet Esan , Bright Chibunna Ubamadu; Andrew Ifesinachi Daraojimba "Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams" Iconic Research And Engineering Journals Volume 5 Issue 1 2021 Page 439-453

IEEE:
Favour Uche Ojika , Wilfred Oseremen Owobu , Olumese Anthony Abieba , Oluwafunmilayo Janet Esan , Bright Chibunna Ubamadu; Andrew Ifesinachi Daraojimba "Optimizing AI Models for Cross-Functional Collaboration: A Framework for Improving Product Roadmap Execution in Agile Teams" Iconic Research And Engineering Journals, 5(1)