Something that works today might no longer work tomorrow, either. “You have to constantly iterate,” he adds. “What has worked can fail fast and only work in modules. So don’t try to solve the entire problem in one go.”

And employees don’t just have to keep learning new skills and adapting their work processes. Knowledge workers in particular also have to face the constant fear that AI will make them irrelevant. It doesn’t help when AI leaders amplify these fears. For example, Dario Amodei, CEO of Anthropic, predicted that AI will be capable of doing most or all jobs, not just entry level, in less than five years.

“Today, if lawyers do 10 tasks, maybe four of them will become obsolete,” says Chhabra. But that doesn’t mean four out of every 10 lawyers will be laid off. Even if most of the work is automated, Chhabra adds, there’ll be more for lawyers to do, not less.

“Today, a litigation matter might be worth $1 million,” he says. “But if it’s just $150,000, then a lot more matters are brought forward. So there’s going to be an increase in litigation and, therefore, more work for lawyers.”

RPA isn’t dead

So is RPA over? RPA wasn’t smart, says Traci Gusher, data and analytics leader at EY Americas. “It was useful, but it wasn’t intelligent. You couldn’t rewrite the process with RPA because it wasn’t technologically advanced enough.”

So, about 15 years ago, during the big RPA wave, organizations looked for ways to use RPA inside their processes, but the benefits were extremely limited.

“It was never so demonstrative that it would catch investors’ eyes,” she says. “It never got to that level of impact.”

Today, many companies are making the same mistakes with AI, she says. Instead of adding AI to existing processes, they need to rebuild them from scratch. “If you’re chunking it, you’re not going to get the results you want because it’s too small and incremental. That’s why I think over a period of time, RPA died a slow death.”

But AI can actually bring RPA back to relevance, she adds.

“There’s still very much a place for RPA in the AI wave,” she says. “You can use RPA for tasks and transaction-level activities, and integrate with agents. That might be the most cost-effective way.”

Unlike agentic AI, RPA is deterministic and completely predictable, it can run on-prem without leaking any sensitive data, and it incurs no token costs.

“We’ve seen consultants say we need to do this with agentic AI,” says ABBYY’s Knisley. “And they don’t have any reliability or governance. What they’re ultimately trying to do they could’ve done with regex for a tenth of the price, and 10 times the efficiency. You’ve got to figure out when you need to use agentic, script, or regex.”

So instead of throwing out RPA and going all-in on agentic AI, many companies are taking a more nuanced approach, using traditional RPA for processes that don’t require intelligence. Meanwhile, they use AI to help set up, test, manage, and upgrade the RPA, getting the best of both worlds.

“I think RPA is a very powerful technology and it has a place in the world today,” says Chhabra. “Especially on things that need to be deterministic, or you’re automating high risk or compliance workloads. You can mask the PII, but it’s still a risk to the company, so I’d rather use a script or some form of RPA automation.”

And a lot of governance will be rules-based, he adds, or based on RPA.

“There’s a lot of marketing speak that RPA is dead,” he says. “I don’t think that. Even the AI vendors are using RPA in the back, but now they’re calling it workflow automation.”