Hi, this is Ika nda. I'm working as an SAP consultant from the last 14 years. Have experience working in SAPS four Brim CRM SD models. I have done a lot of implementations which include subscription management conversion charging, mass moduling integrating with SA systems like F-I-C-O-F-I-C-O-M. I have done a lot of implementations around sales, marketing, service management. Currently I'm working as a technical program manager at Discos. In this role I'm responsible for an SAP Brim implementation which includes my responsibilities as architecture, design, and integration efforts. Prior to this I'm working, I worked as an SAP as for Hana Brim Manager. As Spring and Consulting I lead a lot of SAB projects, which includes Om C, and ci. In this, video. I'm going to explain more about the ai, how it is impacting the revenue management how we can leverage predict analytics and AI for revenue optimization. In enterprise level. Currently in the present world we could see that a lot of things we can optimize using ai in this, in this video we'll examine how the real world results and the infrastructure requirements and ethical considerations are helping different organizations how AI integration can position your organization. This presentation explores more on AI technologies enabling gains in forecasting accuracy, price optimization, customer intelligence, and retention strategies. First, I would like to talk about the AI revolution in the enterprise revenue management area. I would like to talk how this AI is helping to shift from traditional business to to the AI cloud area. By building in inte systems from ground up companies are creating self optimization revenue engines. That continually, continuously adapting the market conditions, operation constraints, and customer behaviors. There's a lot of sustainable financial environments using ai, how AI can data can help you for building your new strategies and building your business complex solutions based on the data. Next, I would like to give you some forecasting the demand, how AI is helping for building revenue for the organizations. There's a 45% reduction cost in forecasting error when doing this AI power tools. There's a 92% accuracy on the data. How can the strategies can be built for organizations. And there is a 30% I rate turnover increase using this ai. And this is directly impacting the bottom line through improved cash flow and reduced carrying costs. Pricing is helping organizations to leverage the market conditions inventory levels and customer behaves retailers that are implementing this AI driven pricing report. Improvements, eight to 12% on average with an increase of up to 16% during the peak seasons of the market. Next, I would like to talk about the lead identification and how can you convert conversion automations, how AI is helping to pick the leads and implement their, convert their leads to business. By focusing resources on high potential leads, organs are achieving convers 30% higher conversion rate rates. Using this ai these systems continuously learns from successful conversions, becoming increasing accuracy day by day, and adapting to evolve based on the customer behaviors. And we have the next thing is about the data collection, how the AI is analyzing it. How these how the recommendations are made by the ai based on the data of the customers and the consumers, and how is it impacting the revenue? It is impacting almost a three, 300% increase in the revenue using all these tools by AI and by delivering relevant experience at precisely the right moment. These systems create stronger customer relationships while simultaneously maximizing the revenue opportunities, which is a big thing for our organization. These sophisticated systems content recommendations, and I interfaces to match each customer's unique interests and needs. And the next, I would like to talk about the customer retention and joint pretension. How how we are identifying the risks based on the behavior of the customer using the ai. Risk identification, we can reduce the risk to 35% using this ai, and the return of investment is increasing up to two 70% using these technologies. Next I would like to talk about the best best action intelligence, which is called NBI Next best action. Using this data, the AI engine has thousands of potential next steps and selects the optimal action for each individual customer balancing revenue opportunities with long-term relationship. It also, it is a proven that like 20% higher customer satisfaction and 28% improvement in sales with these AI tools. Next how the enterprise data infrastructure is helping. Is getting evolved using this ai when you use these robust infrastructures, which is which is a forms of foundation for the AI driven revenue optimizations, architecture involved. It is high standards and it is highly complaints and is very much real time. Enterprise grades implementations process a maximum of nine to 10 million records per hour. Which are, which have a response of 95% transactions. This performance is essentially for types and applications like dynamic pricing and personalization, which in which helps the organization to improve their revenues, revenue opportunities. So you have everything real time with using this AI driven tools and infrastructure. And it process a high volume in less time, which is a big thing for an organization. To build their strategies and the made up like how you can ethically do your business, like fairness, privacy, and data security and talent transparency while making decision making with the stakeholders the future of ai. It gonna impact, it, gonna do a lot of things in the in building a revenue and, it'll do a lot of changes to the integrated core AI revenue systems. Establishing robot frameworks for using, for ensuring AI systems operating fairly transparently in compliance with the regulatory and how these AI applications will help you scale and optimize continuously and it'll help you to develop the unified data architecture. Which can help you to improve your systems customer satisfaction and revenue. Thank you. Thanks for giving me this opportunity. Looking forward for the confidence. Have a nice day.