If AI is doing the work, leaders need to redesign jobs

Most managers are using AI the same way they use any productivity tool: to move faster. It summarizes meetings, drafts responses, and clears small tasks off the plate.

That helps, but it misses the real shift.

The real change begins when AI stops assisting and starts acting. When systems resolve issues, trigger workflows, and make routine decisions without human involvement, the work itself changes. And when the work changes, the job has to change too.

Let’s take the example of an airline and lost luggage. Generative AI can explain what steps to take to recover a lost bag. Agentic AI aims to actually find the bag, reroute it, and deliver it. The person that was working in lost luggage, doing these easily automated tasks, can now be freed to become more of a concierge for these disgruntled passengers. 

As agentic AI solves the problem, the human handles the soft skills of apologizing, and offering vouchers to smooth the passenger’s transition to a new locale that was disrupted by a misplaced bag, and perhaps going a step further to make personal recommendations for local shops to pick up supplies. With AI moving from reporting information to taking action, leaders can now rethink how jobs are designed, measured, and supported to best maximize on the potential of the position and the abilities of the person in it.

According to data from McKinsey, 78% percent of respondents have said their organizations use AI in at least one business function. Though some are still applying it on top of existing roles rather than redesigning work around it.

1. When tasks disappear, judgment becomes the job

Many roles are still structured around task lists: answer tickets, process requests, close cases. As AI takes on more repeatable execution, what remains for humans are exceptions, tradeoffs, and judgment calls that don’t come with a script. 

Take for example a member of the service team at a car dealership. Up until now the majority of their tasks have been scheduling appointments, sending follow-up emails, making follow-up calls and texts. Agentic AI can remove the bulk of that work. 

Now that member of the team can make the decisions that require nuance and critical thinking. They know that the owner of a certain vehicle is retired and has trouble getting around. They can see that their appointment is on a morning when it might snow. The human then calls the customer and rebooks them for when the weather is more favorable. These sorts of human touches are what will now set this dealership apart and grow customer loyalty. 

2. Measure what humans now contribute

As AI absorbs volume, measuring people on speed and responsiveness pushes them to compete with machines on machine strengths. Instead, evaluation should reflect what humans uniquely provide: quality of judgment, ability to prevent repeat issues, and stewardship of systems that learn over time.

In the example above, the service team member at the car dealership could now be assessed not by number of appointments set, or cancellations rescheduled, but by outcomes such as customer satisfaction, and repeat business. The KPIs should be in-person or over the phone touch points with a customer to up-sell, or suggest better services that their vehicle will need. 

3. Human accountability for AI work

When AI is involved, ownership has to be explicit. Someone must own outcomes, even if a system takes the action. Someone must own escalation rules, workflows, and reviews. Without that clarity, AI doesn’t reduce friction, it just shifts it to the moment something goes wrong. 

In the car dealership example, a human should still be overseeing the AI agents doing the work and ensuring that it’s done well. If there are problems, they should be able to catch them and come up with solutions.

One of the biggest risks with AI isn’t failure, it’s neglect from humans overseeing the overall strategy and bigger goals that the AI is completing. Systems that “mostly work” fade into the background until they don’t. Teams need protected time to review where AI performed well, where it struggled, and why. 

Looking ahead

This shift isn’t theoretical. Klarna has publicly described how its AI assistant now handles a significant share of customer service interactions, an example of how quickly AI moves from support tool to frontline worker.

Once AI is doing real work, the old job descriptions stop making sense. Roles, accountability, metrics, and oversight all need to be redesigned together. AI improves fastest when humans actively review and guide it, not when oversight is treated as an afterthought.

The next phase of work isn’t about managing people plus tools. It’s about designing systems where expectations are clear, ownership is explicit, humans focus on meaningful decisions, and AI quietly handles the rest.

If leaders don’t redesign the job intentionally, it will be redesigned for them, by the technology, by urgent failures, and by the slow erosion of clarity inside their teams.

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