Hello everyone. My name is Neha. Today I will be discussing a comprehensive framework for integrating artificial intelligence into in-licensing and e permitting support systems. This presentation examines how advanced natural language processing and machine learning techniques can create intelligent assistance. That argument. Human support agents rather than replace them by automating routine tasks such as information gathering, document verification, and case routing, AI significantly streamlines the licensing process while maintaining high standards of accuracy and compliance. Early implementation results demonstrate substantial, improves improvements in case resolution times staff productivity, and overall user satisfaction. Before diving into the AI solution, let us first set the stage by looking at the current landscape of digital licensing services and why a transformation is needed. Digital government initiatives have laid the groundwork for AI by modernizing licensing processes. Many governments now have dedicated digital transformation offices and digital first policies for regulatory services. According to OECD data, 51% of member countries have established. Central digital transformation offices and roughly 68% have specific digital first strategies for licensing and permitting. However, the maturity of e licensing still varies greatly. Leading nations boost around 72 person digital integration of services while other continues to rely on hybrid. Paper and digital systems. These disparities lead to inconsistent user experiences. Crucially, countries with fuel integrated digital licensing strategies see much higher citizen satisfaction. About 43% higher than average compared to those, who do patchwork systems. This underlines the value of complete digital modernization in government services. In summary, there is a broad recognition that embracing technology can improve licensing performance and user satisfaction. Yet, despite this progress, many operational challenges persist in current systems, and this is where. AI can make a meaningful difference. Let us examine those challenges. Next, despite digital progress, today's e licensing systems face significant operational hurdles. Many processes remain manual. In fact, how staff spend how to. 70% of their time on tedious document verification and compliance checks. Cross jurisdiction workflow are especially slow. International license application takes about 15 to 20 days longer to process on average than domestic ones. And even within US state to state verifications, for example, transferring A CPA license add around 12 to 15 days to the timeline. As a result, about 82% of licensing authorities report difficul maintaining consistent services levels during peak application periods. Backlogs are common, especially in high mobility professions. Where applicants move across states frequently. Furthermore, current systems struggle to adapt quickly when regulation changes. Implementing a new compliance requirements can take several months. These challenges, labor intensive processes, jurisdictional complexity and slow adaptability underscore the need for a smarter approach. This is where. AI comes in promising to elevate many of these issues. Through intelligent automation and decision support, artificial intelligence is introducing a paradigm shift in how licensing cases are handled. Early implementation of AI assisted in-licensing systems have demonstrated dramatic improvements. Across key metrics, studies report a 76% reduction in processing time and a 89% increase in accuracy in application handling, along with a 92% drop in compliance. Errors meaning process become much faster and far less error prone. AI is especially valuable in complex scenarios. For example, in multi-jurisdictional license verifications where rules differ by region AI solutions have shown roughly 83% better performance compared to traditional methods, machine learning models. Can even predict application outcomes with around 91% accuracy, enabling agencies to resolve potential issues proactively and allocate resources more effectively. It is worth emphasizing that these AI tools are designed to argument, not replace human experts. The AI handles. Routine tasks and provides data-driven insights while human officers continue to oversee the process and make judgment calls on exceptional cases. The success indicator so far, make a strong case that AI can address the challenges we identified. Next, let's discuss what is required to implement such AI capabilities. Starting with a technical architecture that underpins the system. Implementing AI in-licensing requires a robust architecture that blends into current government IT systems, and remains scalable. Our solutions use a modern microservices based architecture infused with AI components. Research shows this approach can boost performance significantly. AI optimized services handled complex licensing workflows 78% faster than traditional system designs. Machine learning helps manage the service network efficiently. AI driven routing and load balancing. Cut. Interservice communication delays by 63%, and predictive scaling automatically adds capacity during peak demand. The architecture has three layers. The inter, if it is, sorry, intelligent data acquisition for automated data gathering and validation, cognitive processing, middleware for real time decision. Support and smart workflow routing, adaptive service delivery for dynamic resource allocation. This layer design allows the system to quickly adapt to different tasks and volumes while remaining compatibility with legacy systems. In short, this technical foundation enables AI driven intelligence throughout the licensing process. Leading us to how data is processed within the system. The AI system uses a multi-stage data processing pipeline to analyze each application rapidly and thoroughly. It performs an initial AI screening to classify submission and extract key information achieving up to 98. Percent accuracy. It then applies cognitive validation with deeper machine learning checks, which raise accuracy to roughly 92%, sorry, 96%. And finally, a contextual validation using advanced transformation transformer based models compared to application. Against relevant regulations bringing accuracy to approximately 98%. These three automated checks occur within seconds, flagging errors or compliance issues early, and allowing valid applications to move forward without delay. Moreover. The pipeline adapts to different document formats and jurisdiction specific rules. By learning from each case, this intelligent framework dramatically cuts down manual work, and it continues to improve itself over time as more data is processed in essence. The system handles licensing data swiftly and with high precision, ensuring accuracy and compliance from the start with processing streamlined. And the next consideration is how we safeguard the system security and integrity. Security is a key and a critical pillar of this AI driven system. The platform employs intelligent adaptive defenses that evolve with emerging threats while safeguarding data and privacy. There are three key components, AI enhanced authentication using behavior biometrics for continuous user verification, which achieves 96% accuracy in detecting unauthorized access attempt. Cognitive authorization, an AI that dynamically adjusts user permissions based on context and risk preemptively blocking 89% of potential access violations and intelligent data protection machine learning algorithms that monitor data usage and flag anomalies, identifying suspicious access events. 47 times faster than traditional methods. Together these measures greatly strengthen the system's defense. They catch threats in real time, automatically adapt to new attack patterns, and even adjust security controls as regulation change. In essence, that AI makes the licensing process not only more efficient. But also highly secure and compliant by design. Now, having covered the architecture and security, let's examine the core functionalities and how we benefit that this AI enabled system delivers. Integrating AI into the licensing platform introduces several key capabilities. Intelligent information processing. Automated data extraction and validation significantly improves accuracy by about 64% and speeding up to processing time, for example, turning a task that took 45 minutes into one, done in 12 minutes. Workflow optimization, AI driven workflow management. Dynamically prioritizes en routes cases, cutting average case resolution time by 53%, and improving decision consistency by 76%. Advance the analytics Daphne Framework and AI analytics engine analyzes patents and provides decision support. It has improved the accuracy of decisions in complex cases by 59% and can predict potential compliance issues or workload surges. So they are, and they can be addressed proactively. Overall, these capabilities allow the licensing process to run faster, more accurately and more consistently. The AI handles. Routine work and flag issues. While human officials can focus on complex cases, having seen what the system can do, let us consider how to implement it effectively to realize this AI solution. We follow a four part implementation strategy. Infrastructure preparation and governance, establishing the necessary infrastructure and AI governance early, which results in 63% better compliance, alignment system integration, connecting the AI platform seamlessly with legacy systems, 71% faster interstate license verification processing observed. Capability development, comprehensive training and change management, which allocates 25% of the project budget to training led to 77% higher user adoption and continuous improvement. Ongoing performance monitoring and iterative enhancements improve system reliability by 76% and resolving issues 58% faster With Agile updates, this structured approach mitigate risks and ensure a smooth transition for the organization. Now that the AI system is in operation, we can examine the key performance outcomes achieved. The impact of the AI driven system can be seen in several metrics, operational efficiency, end to end processing time dropped by about 43% and roughly. 76% of routine cases are now handled without human intervention. User experience. User satisfaction score rose significantly, and self-service usage increased from 35% to 82% of applicants economic impact, operational cost. Fell by around 48% in the first year, and the investment in AI paid off quickly. Most agencies reached a full return on investment in roughly 14 to 15 months. Finally, let us look ahead and to how we will build on the success and ensure long-term scalability. Looking ahead. Several developments will further enhance the system advanced AI capabilities. The next generation AI is projected to automate up to 85% of routine licensing tasks and reduce processing time by an additional 67%. These smarter systems will handle the bulk of applications. Dramatically speeding up the throughput. Sustainable evolution, the agile development and continuous learning will let the system adapt to regulatory changes much faster. Future platforms could implement new rules 62% more quickly while remaining approximately 94% compliance accuracy. So when a law change, the in-licensing system can update in weeks instead of months. Infrastructure modernization, cloud native modular architectures will vastly improve scalability and resilience. Studies indicate that cloud-based microservices can provide 73%. Better scalability and 89% higher resource utilization. This means the system will easily handle increasing volumes or new features without performance issues, and will be more robust against surges or downtime integrating emerging technologies. Combining AI with other innovations will amplify its benefits. Analyzing analyzers suggest a comprehensive digital transformation strategy could improve overall process automation efficiency by 71%, while also significantly reduces errors and operational costs To conclude. Embracing AI driven case management is a crucial step towards more efficient, accessible, and user-centric public service in licensing. By continuing to invest in advanced AI solutions and staying capable and adaptable, licensing authorities will be well equipped to meet. Future challenges. Thank you.