Strategic digital product management: Nine approaches - ScienceDirect The role of product management (PM) is key for building, implementing and managing software-intensive systems. Whereas engineering is concerned with how to build systems, PM is concerned with ‘what’ to build and ‘why’ we should build the product. The role of PM is recognized as critical for the success of any product. However, few studies explore how the role of PM is changing due to recent trends that come with digitalization and digital transformation. Although there is prominent research on PM, few studies explore how this role is changing due to the digital transformation of the software-intensive industry. In this paper, we study how trends such as DevOps and short feedback loops, data and artificial intelligence (AI), as well as the emergence of digital ecosystems, are changing current product management practices.
Methods:
This study employs a qualitative approach using multi-case study research as the method. For our research, we selected five case companies in the software-intensive systems domain. Through workshop sessions, frequent meetings and interviews, we explore how DevOps and short feedback loops, data and artificial intelligence (AI), and digital ecosystems challenge current PM practices.
Results:
Our study yielded an in-depth understanding of how digital transformation of the software-intensive systems industry is changing current PM practices. We present empirical results from workshops and from interviews in which case company representatives share their insights on how software, data and AI impact current PM practices. Based on these results, we present a framework organized along two dimensions, i.e. a certainty dimension and an approach dimension. The framework helps structure the approaches product managers can employ to select and prioritize development of new functionality.
Artificial intelligence is not impacting all markets in the same way:
Utilization of Artificial Intelligence in Project Management - Theseus
The potential of generative artificial intelligence in leading a scalable agile enterprise by objectives - LUTPub ? The potential of artificial intelligence (AI) in strategic management and operations remains unexplored mainly due to the novelty of the technology. This study examined how generative artificial intelligence (GAI) can enhance leading by objectives in scalable agile enterprises to improve productivity efficiency. Using a design science research (DSR) approach, the study developed a leading by objectives framework over four iterative cycles.
In the first cycle, the themes of high-level leading by objectives were identified and evaluated for technical suitability and potential benefits. The second cycle mapped the AI features of most used project management tools and refined the framework into more specific use cases. In the third cycle, the impacts of increasing context in prompts were analysed, and the framework’s use cases were tested with ChatGPT using simulated data to evaluate their feasibility. The fourth cycle observed Microsoft Copilot experiments conducted by a business team in the partner company to understand the real-life potential of the leading by objective use cases.
The impact of Artificial Intelligence on businesses. The case of Bluewind
an Italian company in the software industry that decided it was time to take the big jump and implement artificial intelligence in their processes: