By Virendra Sharma, Practice Director, Business Applications, Advaiya
Continuity and continuous improvements in work management techniques are highly important factors that are missed by many stable businesses. In a dynamic and highly competitive industry, especially in energy and utilities businesses, which are transforming rapidly, this becomes more important and critical. Optimising operations and enhancing sustainability with AI-powered work management within the energy and utilities sector requires a strategic approach focusing on proficiency and resource management with environmental considerations.
Here are the core aspects of AI-enabled work management in the energy and utility sector:
● Team assignment: Efficiently allocating human, material, and cost resources across the projects and company is vital. Incorporating AI into team assignment processes can lead to more efficient resource utilization, improved team performance, and better project outcomes. AI-based algorithms can effectively work on skill matching, resource allocation, task prioritisation with workload balancing, and take care of underutilised resources.
● Fieldwork: In the case of onsite support or field services, AI-based components can play significant roles like predictive maintenance and remote diagnostics and help onsite workers with virtual assistants and chatbots.
● Incident response: AI-embedded peripheral automation helps in work management for time-critical response or rapid infrastructure creation projects where fast and efficient development of essential and technological systems are crucial for the functioning and safety of society, especially in urgent or emergencies. It becomes highly useful when we expect quick turnaround to immediate needs, such as natural disasters, public health emergencies, security threats, or other unforeseen events that require rapid intervention to protect lives.
● Maintenance and inspections: For regular maintenance, AI can analyze data from sensors and monitoring systems to predict equipment failures and maintenance needs. This helps utilities schedule maintenance proactively, reducing downtime and optimizing resource allocation. AI can identify patterns and trends to guide decisions on repairs, replacements, and upgrades to ensure the reliability and safety of utility assets.
● Equipment optimisation: AI-enabled algorithms for complex linear and non-linear optimum solutions (NLP) for available resources/equipment are helpful in such a manner that they produce the optimum result in given constraints or dynamic constraints.
● Sizing: AI can help utilities forecast energy demand and adjust supply accordingly. Sizing of demand response schedules where consumers are incentivized to reduce energy consumption during peak periods, thus stabilizing the grid and adjusting energy distribution accordingly.
● Asset management: AI-powered analytics can assist in managing and optimizing the lifecycle of assets such as power plants, schedule repairs, transformers, distribution infrastructure, and reduced waste.
● Process automation: With the help of AI-driven peripheral automation to streamline routine tasks, freeing up resources for higher-value activities, including track and trace.
AI is drastically changing work management in the energy and utility sector daily. These core aspects help utilities meet the growing demand for energy while addressing environmental and operational challenges by optimising operations, modernising various processes, decreasing waste, and contributing to a greener and more efficient energy landscape.