Building Enterprise-Grade AI at Scale: Inside Workday's Journey with Generative AI
Nov 15, 2024
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Eliza Cabrera, Principal Product Manager at Workday, and Beau Lyddon, Principal Engineer, share their insights on implementing generative AI at scale. They discuss the journey of integrating AI into enterprise products, emphasizing user adoption and effective collaboration. Key topics include selecting initial use cases, prompt engineering, and maintaining data privacy. Eliza and Beau highlight the balance between innovation and reliability, offering practical lessons for teams navigating the complexities of AI in a corporate environment.
Workday successfully identifies initial use cases for generative AI by focusing on measurable success metrics and user needs.
The company balances innovation with rigorous data privacy and compliance protocols to safeguard enterprise customer information.
Collaboration between product managers and engineers is crucial for developing an effective AI playbook that facilitates smooth deployment and scaling.
Deep dives
Harnessing the Power of Generative AI
Generative AI has transformed the landscape of enterprise software, with significant potential to enhance user experiences. The excitement surrounding tools like GitHub's Copilot reflects a growing recognition of generative AI's capabilities beyond conventional applications. Companies like Workday have started integrating these technologies to streamline operations and improve product offerings. This shift has prompted organizations to rethink how they approach software development by emphasizing the importance of scaling generative AI applications effectively.
Navigating Technical and Compliance Challenges
The integration of generative AI in enterprise software is met with obstacles, notably data privacy and compliance concerns. Workday's teams must navigate various layers of governance to ensure that customer data is secure while leveraging AI technology for functionality. As generative AI evolves, companies have to be mindful of maintaining high accuracy standards, particularly in sensitive areas such as HR and finance. This ongoing tension between innovation and compliance highlights the need for robust frameworks that balance risk with the desire for AI enhancements.
Creating User-Centric Solutions
Workday emphasizes the importance of crafting solutions that meet the needs of a diverse range of users, from small enterprises to large corporations. The company has focused on intuitive usability, allowing users to generate outcomes like employee FAQs with minimal technical expertise. This approach fosters broader adoption of generative AI features across user segments while ensuring that customer experience remains at the forefront. By simplifying the interaction with complex AI technologies, Workday enables users to gain value without requiring deep knowledge of AI operations.
Collaboration Between Development and Product Teams
The relationship between product managers and engineering teams is critical in successfully deploying generative AI applications. This collaboration involves a cross-functional effort that combines insights from product strategy with technical expertise, facilitating the transition from idea to execution. Early-stage projects have led to the development of playbooks that guide teams in leveraging generative AI while ensuring compliance and quality control. This cooperative approach reduces bottlenecks and elevates the overall efficiency of AI-driven project rollouts.
Strategic Experimentation and Feedback Loops
The iterative process of developing generative AI capabilities encompasses experimentation, requiring teams to learn from early adopters and customer feedback. Understanding user needs leads to identifying viable use cases, which is essential for refining product offerings. Workday's strategy includes establishing solid metrics to measure the success of AI features and adjusting based on user interaction and feedback. This focus on continuous improvement and adaptation is essential in navigating the fast-evolving landscape of AI technologies and ensuring they meet enterprise expectations.
Join us for an insightful conversation with Eliza Cabrera (Principal Product Manager) and Beau Lyddon (Principal Engineer) of Workday as they share their journey implementing generative AI at enterprise scale. As one of the first major enterprise software companies to roll out GenAI features, Workday offers valuable lessons for product and engineering teams navigating this technology.
Learn how Workday approached everything from their first MVP features to scaling AI across their platform, including:
How they identified the right initial use cases and measured success
Their approach to prompt engineering, model selection, and RAG architectures
Managing enterprise customer expectations around data privacy and compliance
Creating an AI playbook to help teams across the company adopt GenAI
Balancing innovation with enterprise-grade reliability
This episode offers practical insights for anyone working to bring generative AI capabilities to enterprise software products. Eliza and Beau share candid perspectives on what worked, what didn't, and how to think about scaling AI responsibly in complex enterprise environments.
Whether you're just getting started with GenAI or working to scale it across your organization, this conversation provides valuable lessons from leaders who have successfully navigated these challenges at one of the world's largest enterprise software companies.
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