Vijay Swami, co-founder and CEO of Draup, believes the future of workforce planning is not about studying people; it’s about studying work itself. After nearly a decade pioneering talent intelligence, Draup has pivoted toward the most urgent question every enterprise faces: how do you redesign work for the age of AI? In this episode we sit down with Vijay to find out exactly how this works and why it’s more important today than ever.

The centerpiece of this evolution is a new product called Etter — named from Norwegian mythology, meaning “always in pursuit of” — because AI transformation is never finished. Insights from Etter help companies model their workforce not around job titles and skills taxonomies, but around processes, workflows, and tasks, then simulate what AI automation would actually mean for each one — quantifying the opportunity in dollar terms for the CFO. For one hospital client, Draup sized the AI opportunity at $2.6–2.8 billion.

The interesting part of this story is that Draup reveals a data problem lurking inside most enterprises: HCM systems are disconnected from where work happens — in ERP, Salesforce, Jira, and other operational systems. Draup closes that gap by integrating across HCMs, operational systems, and an evidence layer built on Microsoft WorkIQ, which captures how people actually spend their time. But all of this is not just a technology exercise: Vijay and his team take a consultative approach to solving customer problems, supported by technology.

Vijay argues you cannot redesign work if you only know the skills people have, you have to start with understanding the work itself and then redesigning workflows and processes with the outcomes in mind. We call this dynamic work design.

Work intelligence platforms like Etter are becoming a critical part of the HR 2030 architecture, and this new market is growing quickly. It’s important to learn about solutions like Draup as you build your AI transformation strategy.

Additional Information

What Is Task or Work Intelligence And Can We Decompose Every Job For AI?

Introducing HR 2030: A Vision For Agentic Human Resources

How To Take A Pragmatic (and winning) Approach To Skills

AI Transformation From the Frontlines (pod)

• (00:00:00) - Introduction and Welcome
• (00:00:49) - Meet Vijay Swamy and Drop's Origins
• (00:02:56) - The Meaning Behind Drop and Etter
• (00:04:25) - Five Key Use Cases for Work Redesign
• (00:06:03) - AI Sandbox and Simulating Automation Costs
• (00:07:03) - Skills Architecture and Business Model Shifts
• (00:07:44) - Industry Convergence and Disruption Examples
• (00:09:03) - Building the Etrontology and Data Integration
• (00:11:19) - Integrating with Enterprise Systems
• (00:13:31) - Data Health and Hidden Enterprise Gaps
• (00:15:05) - HCM vs ERP Disconnect and Workforce Twin
• (00:16:35) - HR's New Muscle: Owning Work, Not Just Jobs
• (00:17:57) - Reframing the Story: Processes Over Tasks
• (00:20:14) - Layered Skills Framework: Root, Core, Tech, Soft
• (00:22:07) - The Future: Visual Factory Modeling
• (00:23:06) - Final Advice: Start with the Analyst, Not the VP
• (00:25:15) - Closing Thoughts and Wrap-Up