Artificial intelligence (AI) has proven to be an asset in reducing human intervention in predicting and decision-making for many applications. Electrification through the addition of both electric vehicles (EVs) and charging stations is resulting in several core challenges, including improving utilisation, increasing availability, and reducing charging times. This paper aims to develop concepts that AI can be trained on to enable applications such as predicting battery life, making accurate charging-time predictions, and identifying the cheapest available charging options for EV owners. Additionally, a model was proposed to predict with available variables to provide major stakeholders, such as customers with EV purchasing timeframes, businesses with suitable chargers, governments for policy updates, and other stakeholders for carbon footprint reduction measures. Although this concept limits data collection from both manufacturers and customers, relaxing government policies to allow data access may lead to improved AI models. Upgrading charging equipment to enable data collection on customer charger utilisation is a challenge from bothmanufacturers' and users' perspectives. Users become conscious of their data privacy while sharing information about their vehicles and charging frequencies. Manufacturers become more conscious of their data privacy when sharing typical battery and other equipment characteristics curves, which are more confidential, to ensure they are not readily available to competitors. The holistic conceptual model developed in this paper served as the basis for AI training. The model offers significant opportunities to learn from other published predictive techniques and data analysis methods for critical infrastructure, thereby increasing the safety, quality, and reliability of electrical power.