Hi, my name is Hema and I work as a release train engineer in Tyler Technologies. So my role is all around how Agile works, scrum works. So I am even like agile coach, so I know in the real world how Agile works and what problems we face, what team problems will be. I felt like AI can be useful in this area. That's where I'm here with AI as your agile, copilot, predictability, risk aware, and human-centric delivery in the age of prompt engineering. So what are the problems that right now Agile has? Has like challenges, the real, the reality gap. Enterprise agile teams frequently encount significant gaps between strategic plan and actual delivery outcomes leading to missed deadlines and resource misalignment. And the common pain points are sprint overruns and missed commitments, inaccurate effort estimate, hidden dependencies, surface late. Resource allocation will be insufficient. AI beyond task automation, it can be a strategic CoLab pilot, intelligent planning and proactive risk detection, how a strategic co-pilot works. AI transforms from simple automation to intelligent partnership, augmented human development decision making across the delivery life cycles. Intelligent planning, so how this works. Machine learning models analyze historic patterns to enhance backlog, refinement, and capacity forecasting, proactive risk detection. So how can AI help in this area? Real time monitoring identifies collaboration issues and burn down signals and delivers blockers before they get escalated. So there are some AI driven capacity models like skill alignment analysis, seasonal productive patterns, historic velocity data, productive success rates, so we can better use them. So it, it also helps in enhancing sprint success, how the opportunities by integrating AI powered capacity modeling with traditional agile planning teams can significantly improve their sprint completion rates and estimate accuracy. The key lies in combining machine intelligence with human expertise to create more realistic, achievable sprint commitments. How is that going to be helpful? Predictive ANA analytics for agile velocity forecasting where predictive sprint capacity on team composition, complexity patterns, and historical performance trends can be delivered, so can be derived multi-dimensional prioritization, BA balance, business values, technical dependencies, risk factors, and resource availability for optimal backlog sequencing. And shortened planning cycles reduce planning overhead while whitelist improving accuracy through AI assisted estimations and dependency mapping. We are good that we have the all predictability. So how does it works actually? Graph paste, backlog, intelligence dependency mapping. AI analyzes relation between user stories, identify hidden dependencies and cross team coordination needs that traditional planning might miss. If there is a human who is planning, they might miss some parts of our dependencies between cross-functional teams, but AI helps us in that area and technical depth. Visibility. So the common problem that today's Agile team mostly fails is they realize there is a technical depth which is hidden after too many sprints pattern. So in this space, even AI can help us with identifying the technical depths upfront. So real time risk protection. So NLP, power Insights, natural language crossing analysis, standup notes, comments and communications to detect sentiment shifts and collaboration issues. So in Agile, there is a most common dysfunction that we observe. The team don't communicate, so we can have an AI as a spy or a help or a coordinator, which can help us with that. Then velocity tracking monitors, sprint over sprint trends to identify declining performance pattern before they impact delivery commitments. Then behavioral signals, track meeting participation and collaboration patterns. In a, as I there are too many meetings and we don't know how many of them are effective. So AI can be can give us some important notes out of that meeting, which will help the team. Then burnout prevention. We might tend to overload a particular person with lots of loads. So AI help us identify this particular person has so much load in the current sprint, and he's expected to work the same in the next sprint. Then there's a chance of burnout. So AI help us even to identify the moods of the team. In that case, cross-functional resource optimization, intelligent as assignment. So AI assisted resource allocation enhances throughput by mapping team member performance availability and skill development goals to optimal work assignments. The system considers individual growth objectives alongside delivery needs, creating a balance between project success and professional development. So it helps with the core functional resources as well. Responsibility, AI adoption, transparency frameworks, clear documentation of ai decision making processes, ensures steam understand how recommendations are generated and can challenge assumptions. Ethical guardrails, establish boundaries around AI usage particularly. Regarding team monitoring to maintain trust and respect individual privacy explainability models. So AI must provide some clear reasoning for their suggestions only that there will be trust built with the team while they take the suggestions of from ai. Keeping humans in the loop argument, not replacement. So AI serves as a decision support system, providing insights and recommendations while reserving human judgment and team anatomy. Final decision still remains with humans. We are not trying to replace any human with ai. We are just trying it to be a helpful model for us. Preserving team dynamics. Successful AI integration strengthens rather than undermines collaboration, moral and psychological safety within agile teams, the technology supports the human elements that make agile effective communication, trust, and continuous improvement. See your practical playbook. Assess current state. Start with pilot projects, train and enable teams. Iterate and improve. The future of Agile delivery. AI powered Agile isn't about replacing human expertise. It's about amplifying it By combining machine intelligence with human creativity, empathy, and judgment, we create delivery systems that are more predictable, more resonant, and more humane. I would like to thank each one of you for giving me this opportunity. As I say, AI is a helpful model, not seriously replacing any humans, so we can best use AI in our agile world as well.