AI-Driven Product Roadmap for Strategic Business Alignment in SW
Elhenawy, Tamer Mabrouk Aly (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120833552
https://urn.fi/URN:NBN:fi:amk-2025120833552
Tiivistelmä
This thesis is trying to explore the capability of Artificial intelligent to foster building the roadmaps and hence the decision making in product management for software development within a highly competitive industry and mainly in the software section. Additionally, to explore the way to smooth the adoption of AI within the team. The thesis is commissioned by R&D team in a global leading Telecommunication and software vendor to increase the accuracy and the quality of the product roadmap planning by the introduction of AI. The main beneficiaries of this study are R&D implementation team, their leaders, line managers, product managers, release managers and the business strategy teams, accordingly Executives, Marketing and Sales.
The development task focuses on identifying the opportunities and challenges of introducing an AI tool to build high quality and accurate roadmaps, resulting in supporting the decision making in the early phases of software product development. Which shall optimize the usage of resources, maximize throughput, and result in customer satisfaction.
The methodology used a mix of quantitative and qualitative case study by combining survey results with in-depth interviews with stakeholders responsible for building the roadmaps. Reviewing and assessing the current roadmap and effort estimation practices resulted in finding out the gaps and inefficiencies, understanding the participants’ concerns and needs for building an efficient AI tool meeting their expectations.
The key findings results that AI can provide more accurate, faster and transparent predictive roadmaps through an AI tool providing higher accurate effort estimations, still the adoption of AI tool depends on the readiness of the team and the organization -on larger scale- in accepting the changes and adopting the AI tool, the adoption shall consider the AI tool as a supportive not a replacement tool to boost the Human-AI collaboration, the study is recommending the implementation of the AI tools in phases not in one shot to increase the acceptance from the users and enhancing the AI tool functionalities and performance through regular feedbacks.
The development task focuses on identifying the opportunities and challenges of introducing an AI tool to build high quality and accurate roadmaps, resulting in supporting the decision making in the early phases of software product development. Which shall optimize the usage of resources, maximize throughput, and result in customer satisfaction.
The methodology used a mix of quantitative and qualitative case study by combining survey results with in-depth interviews with stakeholders responsible for building the roadmaps. Reviewing and assessing the current roadmap and effort estimation practices resulted in finding out the gaps and inefficiencies, understanding the participants’ concerns and needs for building an efficient AI tool meeting their expectations.
The key findings results that AI can provide more accurate, faster and transparent predictive roadmaps through an AI tool providing higher accurate effort estimations, still the adoption of AI tool depends on the readiness of the team and the organization -on larger scale- in accepting the changes and adopting the AI tool, the adoption shall consider the AI tool as a supportive not a replacement tool to boost the Human-AI collaboration, the study is recommending the implementation of the AI tools in phases not in one shot to increase the acceptance from the users and enhancing the AI tool functionalities and performance through regular feedbacks.