AI rework is a nagging problem, even as technology booms

Often pitched as an efficiency boon, AI has endured some harsh press lately as those promises run up against the hard realities of what the workforce is experiencing.

IT pros are finding the challenges of AI can, at times, outweigh the benefits—and even slow down workflows: Research from the Harvard Business Review in February revealed that, for all the promises of AI efficiency, actual deployment of the technology has led to an increase in task time.

Having to redo work originally created by AI is becoming a problem for IT teams—and looking for solutions is increasingly important for companies and organizations hoping to streamline operations.

Changing priorities. Part of why AI rework has become such a chronic problem is, paradoxically, the rise in automating tasks. As Nullify CEO Shan Kulkarni told IT Brew, removing human oversight of AI work in exchange for trusting the outputs means that staff often must step in to address mistakes that would have been caught earlier on. On the brighter side, it’s an evolution that may eventually smooth itself out.

“It’s going to go down over time, at least for like a subset of tasks,” Kulkarni said. “The model is going to get so good at performing them that the work required to review, or redo, the outputs is going to gradually reduce and then drop off over time.”

Research on AI from Workday in January found that AI efficiency, at least in terms of time saved, hasn’t paid off on the backend: Around 37% of those time savings needed to be invested in rework, according to the global survey of 3,200 leaders and employees in the tech sector.

Choked up. Kulkarni views that kind of rework as a “bottleneck,” he told IT Brew, but one that agents will get through sooner or later. The key to AI efficiency, as he sees it, is to ensure that harnessing AI agents results in efficiency by using agents to review the work of other agents. However, it’s critical to make that chain work correctly to erase the need for rework.

“There’s this agent-to-agent work model starting to evolve as people realize that’s something you can rely on more heavily if the agent has all the right context,” Kulkarni said, adding that, as models and agents get better, “the bottleneck needs to be how to instantiate that reasoning into agents that can do work and review each other’s work and fit really well into how that particular company works—that customization is going to be where the bottleneck is for responsible deployments and adoption.”

By raising the productivity bar, agents are assisting organizations in managing their workflows and changing things for the better, Doug Hughes, CEO of edtech platform Codio, said. That means ensuring that an acceptable step forward now is well ahead of where the industry was five years ago; in practice, that’s where big promises can mean a lot of rework and where IT team leaders need to take an active role.

“We can’t just expect that everybody’s going to go figure that out on their own,” Hughes said. “It’s just not going to happen—and you have employees that have a lot of pressure on them to say that they know how to use these tools, and they do, but the way they’re being used is in an ad hoc fashion.”

Job, secure? Ad hoc deployment equals suboptimal outcomes, Hughes added, meaning that the tech workflows are going to run into unforeseen challenges that mean more work, not less. That’s not necessarily a bad thing, either—another way to look at rework is, for now, job security—as long as you’re able to adapt to the new reality.

“This level of change, which is orders of magnitude, oftentimes requires unappetizing decisions where you might have to let certain talent go that is only capable of seeing the job they’ve been doing for the last 10 years through one lens and bring in new talent that’s able to imagine that work done in a different way,” Hughes said. “This is a multi-year process, and it’s going to be very messy. I think we’re at the beginning stages of the messiness.”