Hi, today we are going to discuss about, enhancing utility operations through human AI collaboration. And, this is my recording for, Conf42, JavaScript session. And, let's dive into, how utilities can enhance, AI and human collaboration, to enhance their operations. and JavaScript is one of the, key factor within the CI systems. which enables users to have, predominant access, having, user experience more better, compared to the previous versions of the CI systems, and, many other use cases of, JavaScript. But, this is also more, focusing on, How, AI is used, in the utility sector for better usability and experience and enhancing the overall operational experience of the users and, serving the better customer base. And, yeah, so let's dive into it, all So these are the table of contents, and, I'm going to discuss about today. especially the predictive analytics in utilities enhanced, customer service, A and MA data, ma management in utilities, the role of, human enterprise in AI operations. And, we'll take a case study of, the Catapult project, and damaging trench in AI utilities, the evolving, a relationship and conclusion. so most often the utility sector shifting. the shift in the technology comes later after a lot of technological innovation, innovations. But right now, it's not that it's not the case because there is a lot of expectation among the customer base, within the utility sector, having to use mobile, more mobile, more of mobile phones, able to view their bills, their consumption in real time and, there are a lot of, programs that are rebate programs and, energy consumption programs that are going on. the utility sector is, facing demands for reliability, sustainability, and customer satisfaction. And that's where, Human AI collaboration provides solution, combining AI's data processing capabilities with, human decision making. And that's the introduction. how, we're talking about, how predictive analytics in utilities, can be used. to help better customer service and, JavaScript is one of the, key factor in, different, business analytics, programs to show the, user experience, how, they can view different dashboards and, for example, there is a tool called WebFocus, where in JavaScript is predominantly used to enhance the user experience. So the AI driven predictive analytics helps forecast equipment failures and optimize maintenance schedules. For example, machine learning models predict transformer failures, preventing costly downtime. So managing the downtime, for the utilities is the major thing which, every utility, company has to take care because, no one wants an outage for the sake of, some mechanical failure, right? the, predictive analytics can be used to find out, where the, beforehand, before the, failure of a transformer or whether you're not getting a leads to a gatekeeper or The hidden system has failed to send a reads and where user not able to see the real time data of the Usage or consumption or the rate details, etc So So let's go to the next chapter the enhanced a enhanced customer service like I said before there's a lot of expectation among the customer base to, to use the latest and greatest technologies that are coming into their hand to, view the details, the earlier, you just have to, print out the usage and other details and build to the customer. That's when they'll be able to see, but now the millennial and, Gen Z generation, they should be able to see. their details, whenever they can and, it doesn't matter. It's midnight till ten o'clock or, and, there are also a lot of, programs going on time of use, net metering, because a lot of. People now want to shift to clean energy, so there is, there's a lot of, focus on, clean energy nowadays, so all these programs can be integrated with the help of, enhancing the great customer care service. there are a couple of, tools that we can discuss about in the chat boards that provide 24 by 7 customer service, like I said. They want to see in midnight, 12 o'clock, the chatbot should be able to guide them to how to retrieve their bills and how to retrieve their usage details. AA offers, yeah, so based on the, to and fro interaction with the chatbot, the AA should be able to, provide them energy saving tips and improving customer satisfaction. So the, let's say a customer was, recommended a clean energy program or a net meter energy program. or a time of use program or, various other programs. And, if he could save money on that program being used, then there is a lot of satisfaction that he's getting. And in turn, there is a great review for the utility company, for serving them better and, clean energy and many other things. And AI complements human agents by, handling routine inquiries well. Human addresses and complex issues and AI also allows utilities to participate, anticipate customer needs and provide proactive solutions. So it's all about. replacing the human in this aspect of the customer service. so you don't have to run a customer service team, which would, really have a lot of customer services team responding to the phone calls to the customers rather than, a chat bot would really, be a replacement for a customer service. using the latest LLM models, it can determine, the customer needs and, a predict like in human and, the accuracy is almost 99. 9 percent helpful for the customers in providing a better feedback because there is no latency, there is no, difference in the communication language, either you are Spanish or any other community, which is predominant within the, area that Utility is serving. So area and data management in Utilities, next slide. AA processes large volumes of data from smart meters, sensors in real time allowing for, quicker insights and, operational adjustments. Utilities are able to optimize power distribution and manage supply more effectively, improving the grid stability and efficiency. Real time analytics provides data that helps in decision making, such as when to shift lower power loads or how to integrate renewable energy resources effectively. this is all about, getting the real time data. so for the period of time, for example, there is a MO law where every year the computing power increases to access, by every two years. So, so earlier the cost of computing different data sources, data, large chunks of data has been huge cost for, utilities. Now they can put, I will, cloud and, AI technologies, which, can provide scaling to get the data and store them, storing them, and then processing compute can be available based on the, demand that the information they are getting. So they don't have to really scale up the original infrastructure on their on premise. and, they can really focus on their operations rather than the. technical infrastructure. And once the data is in the system, so the AI can be fed all the system and then, predict with using predictive analytics and, using the large data that they are getting, they can easily identify anomalies within the usage of the, electricity by the customers, usage of water or anything. Else, beforehand, really, the customer will be finding out. So there are, for example, there will be a lot of issues like diversions and, for water and especially for water, customers because they, some of the commercial customers I have seen, there'll be a lot of, sometimes tickets created because of the high usage and, the actual bill is really not built for that. Thank you. all these kind of, things can be really discovered beforehand and really helpful for the, utilities and like the utility management, the, when they buy the, electricity from Southwest Power Pool and different other grids. They need to make sure their grid is stabilized and no one is really attacking them, in terms of cyber security and other means. So the endpoint security can be enabled using the AI and by checking the data regularly. let's dive into next chapter. So the role of human expertise in AI operations, AI can process and analyze data quickly. Human oversight ensures that AI insights are used ethically and accurately. Human professionals validate AI recommendations, ensuring that critical judgments and ethical concerns are addressed. Human experts provide context and adaptability to AI systems, particularly in unexpected or nuanced situations. the, like I said, like we were discussing, so the AA cannot 100 percent take over or replace a human being, but there is a role for human where it can really, enhance the human's ability and reduces the time of operations and how this can be achieved is, really, amazing. human professionals validate the AA recommendations, ensuring that critical judgment. And Rather than the whole processing and, com, all the steps that they go through to end anomalies a can do the suggestions and humans can really take, decisions quickly. So there's a lot of, cost reduction here where, there, where the, a customer service team or business analysts don't really have to spend a lot of time in analyzing the data or. finding out, anomalies by themselves, they can depend on AI to, do the recommendations and make the decisions quickly. So for example, let's take a catapult project is a real world example where, we did this project in one of the, local communities in Kansas Board of Public Utilities where, AI driven edge computing processes sync data from smart meters and sensors in real time allowing immediate response to anomalies. A enhanced customer service provided proactive problem solving and personalized interactions leading to higher customer satisfaction. So the customer satisfaction is being gradually improved nowadays because of using this latest technology so that, the, at the end of the day, every utility company's, response is to, Help the customers better, save money for them, and in turn save the energy cost for themselves so they can better serve the customers, right? using these latest and greatest LLM models and, customer services that they're, customer service models that they're using, they just want to enable the customers, Service provides, better services, personalized interactions, and, And in turn get the higher customer service satisfaction. So I, I have seen this, within the lot of utility companies using the latest technologies to, really enhance the customer satisfaction. And, like we mentioned. AI really has that capacity to solve a lot of, computing, the computing data, for, from smart meters and sensors in real time so that they can help better the, services. So what are the trends in ai? the fu the. Let's discuss about the future trends. edge computing moving computations closer to data sources, enabling faster real time decision making and grid management. Advanced machine learning, deep learning and reinforcement learning models will improve load forecasting and failure predictions with high accuracy. Autonomous systems, AI driven drones and self healing grids will reduce human intervention in real time. dangerous situations, increasing safety and efficiency. there is a lot of safety concerns for the employees who are really working during the outages and, during any, roadworks or alignments. whoever does that, we can use a lot of, autonomous systems to, really help them automate the tasks rather than, mundane things that they really do. during outages and other stuff. so the advanced machine learning, reinforcement models will improve the load forecasting and failure predictions with accuracy. when the grid is really, the, the commercial customers are really, they're charged high when the, there is a peak demand on the, they apply on this grid. if the, If there is change in the pattern, it's really helpful for A to analyze those patterns and really, forecast the usage based on the customer's, requirement changes so that, they don't really put sudden pressure on these grid and so in turn we'll really help, we'll be really helping the grid from, failure. for example, there is a grid failure in Texas in 2021, where. the requirement was huge because of, the, because of the, the snow that we had and, and the grid was, one of the grids failed, either should be either because of the usage or because of the, because of, the not able to produce the energy demands that customers were requesting. in that case, a lot of people, a lot of people suffer from outages and, really don't really have their basic needs because that is the main source for everything, for any of the tasks. so in that situations can be avoided, because of the grid, the, the grid failures can be really avoided using the, upfront knowing the situations using the edge, advanced machine learning techniques. The evolving human AI relationship, the future of human AI collaboration, AI will increasingly serve a lot of tools to augment human decision making, providing data driven insights while humans focus on strategic thinking. Continuous skill development will be required, particularly in areas like data literacy. AI collaborative innovation will raise, as AI frees up human professionals to focus on creativity and strategic initiatives. this is. We discussed this point earlier, the only major thing here is there should be a lot of, development and understanding, learning to the customer service team and, analysts, because of the A, A. with, if A is, A is like a real time processor, right? there has to be a lot of upgrade in their skills, to catch up the, A, advances so that they can help the, better the customers. So the integration of AI and human expertise is rapidly transforming, utility operations leading into enhanced efficiency, improved decision making, and greater, improved decision making, and, AI leverage. A for predictive maintenance, customer service, enhancements, and real time data management. While relying on human oversight for strategy direction, ethical consideration, and contextual decision making, by combining the strengths of both AI and human professionals, utility companies are becoming more responsive to growing demands for reliability, sustainability, and customer satisfaction. Looking to the future, emerging technologies such as edge computing, advanced machine learning models, and autonomous systems are set to revolutionize AI. I'm going to be talking about the, the new, the new, the new, the new