Stock plan administration often involves repetitive, manual processes and multiple handoffs that slow operations and introduce errors. By leveraging workflow automation and intelligent technologies, equity compensation processes can be streamlined for greater efficiency and accuracy. We'll examine the fundamentals of automation, identify which processes stand to benefit most, and explore how emerging technologies are transforming stock plan administration.

Innovative issuer SoFi shares how they built AI tools to improve equity compensation processes and the employee experience—from a centralized knowledge base and automated cross-functional workflows to enhanced employee communications. You'll see live demos of tools including an interactive ESPP calculator and AI-powered support solutions, with practical guidance on setting up these tools in your own organization. Gain insights into how these approaches can reduce manual effort, increase efficiency, and support more informed decision-making.

Equity compensation professionals at three companies share how they’re applying AI to improve efficiency, accuracy, and employee communications—automating monthly reporting with AI-assisted coding tools, building bots to streamline employee data collection and validation, and enhancing intranet content for greater clarity and engagement. They'll walk through what they learned about precise prompting, thorough testing, and the ongoing human oversight that makes it work. You’ll leave with practical ideas and actionable steps to begin implementing AI in your own programs. 

As AI adoption accelerates in equity plan administration, the opportunity extends well beyond basic automation. From grant processing and participant communications to reconciliation and reporting, AI can streamline core workflows—and agentic AI is pushing further, enabling autonomous operations that shift administrators from reactive to proactive plan management. We'll outline practical enablement strategies for equity compensation teams, including how to integrate AI into existing systems, maintain compliance, and scale capabilities responsibly.

Hear from equity compensation professionals using AI to streamline time-intensive compliance tracking, reconciliations, reporting, and employee communications—including how they design effective prompts, apply governance guardrails, and integrate AI into existing workflows. We’ll also discuss confidentiality considerations and validation processes for compliant AI use, with concrete examples and templates you can apply to automate tasks, enhance reporting, and strengthen collaboration across finance and HR.

Manual spreadsheets, tight deadlines, and high transaction volumes make vest-day validation a perfect candidate for AI transformation. See how Walmart’s AI-driven dashboard replaced repetitive tasks with automated validation, exception reporting, and output generation—saving 180 hours annually—and how you can apply the same approach to your own workflows using accessible tools like GitHub Copilot.

Using real-world examples, you'll see how equity compensation teams are applying AI to automate manual tasks and enable more efficient workflows across traditionally high-touch areas—including data validation, financial and flux analysis, reporting, and personalized stakeholder communications. Beyond individual use cases, you'll also explore how to build a practical AI strategy that identifies high-impact opportunities, aligns them to business goals, and moves from ideation to execution. You'll come away with actionable insights to unlock the broader potential of AI across your equity compensation function.

Hear from equity compensation professionals that have used human-led AI solutions to dramatically reduce execution time, improve quality and accuracy, and lower headcount and cost—across tasks including developing employee education sessions, drafting communications and FAQs, and reviewing executive compensation agreements. You'll also leave with hands-on activities to build your own AI solutions to common equity challenges.

Explore two real-world approaches to building AI agents for Atlassian’s stock plan support—one trained on curated data, another that learns dynamically from user interactions. See how these models perform in production across two environments: within Jira Service Desk to intercept employee questions before tickets are submitted, the other as a global assistant via Rovo with broad access to plan data. Through a side-by-side comparison, you’ll see how differences in design, behavior, and tone impact the user experience and outcomes. You’ll come away understanding how each approach delivers accuracy, scales effectively, and earns user trust—and what that means for reducing support volume and improving response quality.

Know what you’re working with—explore what AI and generative AI actually are, how leading tools like ChatGPT, Copilot, Claude, and Gemini compare, and how to write effective prompts for equity comp tasks. We’ll cover implementation lessons, risks, and how to evaluate solutions, with an emphasis on responsible use: confidentiality, verification, company policies, and working within approved tools.