High-volume use cases often handle tasks such as inbound applications, invoices, contracts, purchase orders and customer service requests. “Having generative AI handle these first, triage them, recommend answers and send the prioritized, summarized issues to people can drive significant savings in time and cost,” Greenstein said.
For example, genAI can help customer sales and service reps access data to help customers faster and with great personalization; that saves labor and time and can improve customer experience.
And with software and product development genAI can play a major role in saving time and costs from ideation, requirements, user stories, test cases, code generation, testing, and documentation.
But genAI tools cannot be set on autopilot under the assumption ROI will follow. Chon Tang, founding partner at the Berkeley SkyDeck Fund, an academic accelerator at the University of California-Berkeley, described genAI tools as more akin to humans — they have to be managed.
“Prompts have to be scrutinized, workflow verified, and final output double-checked. So, don't expect a system that automatically completes tasks,” he said. “Instead, generative AI in general, and LLMs in particular, should be seen as very low-cost members of your team.”
"Generative AI is enabling a level of machine intelligence we have never seen before: it will absolutely replace humans in many tasks, and the ROI will be obvious,” Tang said. “The only question is how many humans, and what kind of tasks.
"Today, generative AI remains unstable, unlike other pieces of technology that behave more like tools with very well-defined behavior. ...We wouldn't want to use a dishwasher that failed to wash our dishes 5% of the time.”