Join Workday’s VP of AI, Kathy Pham, in uncovering how agentic AI is moving beyond reactive responses to drive efficiency and innovation in the enterprise.
Ghadeer Redler
Head of Multimedia, Thought Leadership & Customer Stories
Workday
Audio also available on Apple Podcasts and Spotify.
AI is no longer simply an incremental step in automation. It’s an active force in enterprise strategy, and represents a fundamental shift in how work gets done. The next wave of AI, known as agentic AI, isn’t waiting for instructions. It’s anticipating needs, making decisions, and executing tasks that used to require human intervention.
Kathy Pham, vice president of artificial intelligence at Workday, explored this transformation in a recent episode of The Future of Work. She highlighted how AI agents are driving personalized solutions across industries, streamlining operations, and fundamentally changing enterprise technology. Let’s break down how agentic AI is shaping the workforce, and why it matters.
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AI has come a long way from simple rule-based automation. Historically, AI systems relied on predefined instructions to execute tasks. Over time, advancements in machine learning, deep learning, and neural networks enabled AI to make predictions based on patterns in data. But agentic AI takes this a step further. These systems perceive their environment, reason through possible next steps, and autonomously execute tasks—sometimes even learning from experience.
A practical example? AI agents in recruiting. Instead of just sorting through resumes, they can proactively source candidates, analyze business needs, and even schedule interviews—all while refining their recommendations based on prior outcomes.
Agentic AI isn’t one-size-fits-all. Different types of agents serve different functions:
Reactive agents: These follow predefined rules and respond to changes in their environment. Example: A chatbot retrieving FAQs.
Model-based agents: These analyze context to make informed decisions, such as predicting inventory shortages in retail.
Goal-based agents: These optimize for specific outcomes, like scheduling projects around deadlines and available resources.
Utility-based agents: These weigh various factors to determine the best course of action, like suggesting treatment plans in healthcare.
Learning agents: These improve over time by adapting to new data, such as refining fraud-detection models based on evolving threats.
Role-based agents: These are designed to support humans by understanding their roles and responsibilities within an organization and by taking on specific tasks.
Each type of AI agent contributes to streamlining complex processes across industries, reducing manual effort, and enhancing decision-making. Agentic AI is already making an impact across various industries:
Higher Education: AI-powered academic advisors help students select courses based on career goals, availability, and past performance trends.
Healthcare: AI assists in diagnosis, treatment planning, and optimizing staffing levels for hospitals.
Retail: AI agents handle multi-step customer service processes, manage inventory, and forecast hiring needs.
AI agents are moving beyond simple automation to create adaptive, proactive systems that enhance workplace efficiency.
While agentic AI presents significant benefits, it also requires thoughtful implementation. Businesses should:
Clearly define the problem AI is solving.
Continuously gather user feedback to improve experiences.
Prioritize privacy and security to protect sensitive data.
Assess risks at every stage of development.
Ensure AI seamlessly integrates with existing enterprise systems.
Responsible AI design ensures that these technologies enhance, rather than replace, human decision-making.
The evolution of AI agents is just beginning. As natural language processing and large language models improve, these systems will become even more intuitive, enabling deeper personalization and seamless collaboration between human workers and AI-driven assistants. In the near future, expect AI agents to not only optimize business processes but also redefine how we interact with enterprise technology.
Here are a few highlights from Kathy, edited for clarity. Be sure to follow us wherever you get your podcasts and remember you can browse our entire podcast catalog.
“This technology allows us to move beyond simply analyzing data and making predictions to executing tasks autonomously... It enables enterprise technology that can anticipate our needs and proactively complete tasks... AI agents deliver a simple, personalized experience, while behind the scenes, breaking down complex processes, combining that with individual contexts, and then coordinating tasks together to solve complicated business problems.”
“AI has a rich history that dates back to the '50s when the term 'artificial intelligence’ was first coined... Since historical context can help us better understand how we build our future, I'll focus here on the recent history that is most relevant to understanding how we can make use of agentic AI today. I'll take us through a progression of early automation with simple rules to prediction capabilities, and now advancements that can connect systems together, helping us solve complex problems.”
“...we apply human-centered design practices and user research to help us identify the right opportunities. This lays a foundation so we can responsibly design the system to solve the right problems for our people and our business. We also need to ask if we should even build these agentic AI systems at all. We assess the risk of training our models on the data we have, the risk of connecting systems together, and how much automation is acceptable and responsible for our environment.”
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