Good evening everyone. I'm Sohi Pan Book. I'm excited to walk you through how artificial intelligence, when combined with Microsoft ecosystem is fundamentally changing how we approach recruitment from talent sourcing to onboarding. AI is helping us become faster, smarter, and more in how we hire top talent. In this presentation, we'll explore real use cases. Performance, improve improvements and vision of what's next in this space. Let's see how it works. Imagine you are having a senior full stack developer with over 10 years of experience in Microsoft Technologies, someone proficient in T net Core, angular, or React, and databases like SQ Server or MongoDB. Searching manually through thousands of profile on job portals can be overwhelming and time consuming. So now picture how AI driven equipment system that transform this process. First, the profile filtering the AI scans thousands of resumes extracting not just keywords, but contextual insights. It identifies genuine candidates with proven experience in the required technologies, not just those who leased them on their resumes. It analyzes project histories, patterns of growth, real world application of skills. Second powered AI powered wise engagement. Once a potential match is found, the AI initiates a natural sounding wise call to the candidate. It gathers deeper insights, verifying their background, exploring recent project experience, and confirming their self expectations, ensuring a strong alignment before moving forward. Third. Automated skill assessment. If the AI determines a strong match, it automatically sends a personalized skill assessment to the candidate via email id, tailored to their experience and job requirements. Four realtime scoring and introduc. Upon completion of the assessment, they AI affiliates performance across different sections. Based on these results, it trigger another AI voice interaction to schedule interviews, coordinating seamlessly with availability of hiring managers. Fifth, multi round, inter coordination. Whether the process involves two rounds of phi, the AI handles all scheduling logistics, ensuring no time is lost, and communication is prompt and professional. Final selection and letter. This is the final step. Once all interviews are complete, the system sends an automated email informing the candidate of the outcome. If selected, the candidate receives the o. Along with the company's terms and condition, ensuring a smooth and timely onboarding process, understanding AI in recruitment, let's begin by understanding the ai. Technology is used in recruitment. Machine learning enables system to analyze historical hiring data and continuously learn from outcomes, helping predict which candidates are likely to succeed. Natural language processing, which is nothing but, and LP allows systems to understand. Resumes like human would, interpreting context. 11th and even detecting misleading claims, deep learning analyzers, video or audio from interviews to detect subtle traits like communication, style, tone, confidence, and culture alignment. These technologies, when combined delivered equipment process that's intelligent, adaptive, and scalable. Key performance metrics. AI impact is measurable. These aren't just concepts. They drive business outcomes. Like 45% reduction in time to hire means roles get filled faster, reducing productivity gaps. 37% improvement in quality hire leads better. Long-term performance, 32%. Lower hiring cost come from automation of task and 58% reduction in unconscious bias through object to consistent candidate evaluation. These numbers just demonstrate how AI turns hiring into high performance function, not just support process. NLP, which is nothing but natural language processing. Resume screening is one of the most time consuming steps in recruitment. Traditional keyword searches are often rigid and missed quality hand yet with 10 lp. The system understands the matics DevOps. Suppose DevOps engineer or the cloud automation specialist might mean the same thing contextually. It identifies core skills, certifications, even career progression. Parsing is format agnostic with a resumes coming PDF or document or even scanned formats. The result is 43% improvement in resume matching accuracy, helping recruiters focus on real fix. AI driven candidate assessment. Once a candidate passes the resume stage modern app platform take out to avoid technical, cognitive and behavioral traits. Cognitive testing adapts in real time to assess logical thinking and learning potential natural language analysis of evaluates how well candidates communicate and fit with the company culture. Technical simulation validate practical skills like coding, debugging, or architectural design. The AI model then predict on the job performance con, constantly learning from actual post higher outcomes. Predictive ans in hiring predictive analytics helps sift equipment from being reactive to proactive by mining historical data across thousands of. AI can detect what combination of skills, experiences, and personality traits lead to successful outcome in your specific context. This results in reduced turnover, higher job satisfaction, more consistent hiring Sensor companies using predictive hiring models have reduced mishires by 30% enhancing candidate experience. The candidate experience is just as important as recruiter efficiency. AI check bots provide instant answers 24 7, giving candidates timely responses. Smart adaptive forms eliminate repetitive inputs and reduce drop off automated status updates, eliminating and frustrating black hole feeding. These innovations have improved a candidate satisfaction by 41 percentage. And 89 percentage of applicants report, more engaging and respectful process workflow automation bureaus. Let's talk about internal gains. Recruitment involves a lot of repetitive tasks, emailing, scheduling, coordination, concerning all this. AI based work for automation helps head chartings to save up to 25 hours per recruiter per week, manage more candidates without additional headcount. Free up time to focus on strategy, not administrative work. This is especially useful during high volume hiring phases, like campus drives or seasonal expansions. Microsoft AI recruitment ecosystem. Microsoft ecosystem are for seamless AI powered equipment Experience like integrating with LinkedIn talent Solution gives access to passive candidates and predictive batching. Integrating with Microsoft Dynamics 365 HR integrate applicant tracking, onboarding and performance monitoring into one system, integrating with Microsoft Azure AI services. Hello, custom. Machine learning models for skills matching, resume passing and chat bot integrations. The synergy between these platform and shows end-to-end intelligence from the first job. Implementation roadmap. So how do you implement all this? Assess your current recruitment process, identify gaps and friction points. Define your AI strategy. Prioritize use cases like resume screening, assessment, or entry. Pilot in one area, test with specific role in our department. Scale organization wide, build integration with existing tools, continuously optimized track KPIs, gather feedback and retain model organization that follow this roadmap. Report 28% highest satisfaction and see ROI within the first quarter of deployment. The future of AI equipment. The next frontier in AI powered equipment includes immersive assessment using AR or VR for job simulations. Autonomous AI recruiters that conduct and analyze fully interviews predictive workforce planning, that forecast hiring needs and hyper personalized career pathing for employee retention. The future of recruitment is not just faster, it is more strategic, inclusive, and aligned with long-term success. Thank you for joining me today. AI and recruitment isn't just about automation. It's about make data, everyone, and impactful. I would love to connect with further hear your thoughts or answer any questions you may have. Thank you once again.