AI and data driven product management for sustainable energy innovations

Imminent innovations are predicated to become more digitalization empowered and artificial intelligence-driven. The concept of data-driven innovation is also a recently emerging technology. This paper investigates the combined effects of AI and data analytics on energy product innovation, analyzing how AI tools including machine learning (ML), natural language processing (NLP), visualization and reporting tools, and etcetera enhance decision-making, optimize workflow and align product development strategies with consumer preferences. It analyzes how AI and data analytics can assist product managers in making informed decisions about energy product development. Energy corporations have widely adopted AI for enhanced analysis in resource optimization for sustainability. For instance, energy product managers can easily predict changes in demand and follow up on product usage using machine learning algorithms, Internet of Things (IoT) devices and smart meters. When integrated, AI and data analytics tools can aid product managers across energy corporations to gain actionable insights into market trends, consumer sentiments, and conduct predictive, descriptive or prescriptive analytics. Furthermore, the adoption of renewable energy sources by the industry intended for a smoother evolution towards a new energy mix can also be efficiently aided by AI. AI in energy systems will introduce sustainable and cost effective measures for product operations across energy industries. This study reveals that AI’s ability to analyze vast quantities of data for predictive modelling and its segmentation and sentiment analysis can assist product managers in making prompt data-driven decisions concerning energy products.