As AI agents enter real-world deployment, organizations are under pressure to define where they belong, how to build them effectively, and how to operationalize them at scale. At VentureBeat’s Transform 2025, tech leaders gathered to talk about how they’re transforming their business with agents: Joanne Chen, general partner at Foundation Capital; Shailesh Nalawadi, VP of project management with Sendbird; Thys Waanders, SVP of AI transformation at Cognigy; and Shawn Malhotra, CTO, Rocket Companies.
A few top agentic AI use cases
“The initial attraction of any of these deployments for AI agents tends to be around saving human capital — the math is pretty straightforward,” Nalawadi said. “However, that undersells the transformational capability you get with AI agents.”
At Rocket, AI agents have proven to be powerful tools in increasing website conversion.
“We’ve found that with our agent-based experience, the conversational experience on the website, clients are three times more likely to convert when they come through that channel,” Malhotra said.
But that’s just scratching the surface. For instance, a Rocket engineer built an agent in just two days to automate a highly specialized task: calculating transfer taxes during mortgage underwriting.
“That two days of effort saved us a million dollars a year in expense,” Malhotra said. “In 2024, we saved more than a million team member hours, mostly off the back of our AI solutions. That’s not just saving expense. It’s also allowing our team members to focus their time on people making what is often the largest financial transaction of their life.”
Agents are essentially supercharging individual team members. That million hours saved isn’t the entirety of someone’s job replicated many times. It’s fractions of the job that are things employees don’t enjoy doing, or weren’t adding value to the client. And that million hours saved gives Rocket the capacity to handle more business.
“Some of our team members were able to handle 50% more clients last year than they were the year before,” Malhotra added. “It means we can have higher throughput, drive more business, and again, we see higher conversion rates because they’re spending the time understanding the client’s needs versus doing a lot of more rote work that the AI can do now.”
Tackling agent complexity
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