Ai doesn’t reduce work it intensifies it

In the recent past, we’ve spoken both here and here on recent developments regarding the use of Generative Ai and Agentic Ai in the workplace. 

Continuing my fascination, I came across a recent article in the Harvard Business Review authored by Aruna Ranganathan and Xinqgi Maggie Ye entitled Ai doesn’t reduce work it intensifies it.

By now, we have probably all heard of the low-hanging fruit. Ai drafting routine documents, summarizing financials, data analytics, even debugging code (thanks to Claude). From this, it is implied or inferred that Ai allows workers more time for high-value tasks.

Recent research 

Found that employees worked at a faster pace, took on a broader scope of tasks and responsibilities, and extended work into more hours of the day. This without being asked to do so. Left unchecked or mis-managed suddenly we have the potential to unearth untold number of rabbit holes; certainly surfacing some “unknown-unknowns” but at the same time surfacing dead-ends. 

The caveat

Changes brought about by enthusiastic Ai adoption can be unsustainable, causing unforeseen problems down the line.

Once the initial excitement of experimenting with Ai wears off, workers can find that their workload has quietly grown. By extension, and by virtue of unlimited number of Ai prompts and a chat bot, employees can now have a lot of additional considerations and perspectives on their plate!

In engineering and project terms, this is scope creep!  Scope creep can in turn lead to cognitive fatigue, burnout, and weakened decision-making. The productivity surge enjoyed at the beginning can give way to lower quality work, turnover, and other problems.

The solution?

Companies need to develop a set of norms and standards around Ai use—what the authors call an “Ai practice.” Here’s what leaders need to know, and what they can do to set their employees up for success.

Generative Ai Intensifies Work

Task expansion

Because Ai can fill in gaps in knowledge, workers increasingly stepped into responsibilities that previously belonged to others. For example, product managers and designers began writing code; researchers took on engineering tasks previously the exclusive domain of engineers; and individuals across the organization attempted work that they previously would have outsourced, deferred, or avoided entirely in the past.

The panacea? Autonomy!

You mean I no longer have to depend on others? 

Engineers, in turn, spent more time reviewing, correcting, and guiding Ai-generated or Ai-assisted work produced by colleagues. These demands extended beyond formal code review.

Blurred boundaries between work and non-work. 

The boundary between work and non-work did not disappear, it just became easier to cross a/k/a as “keeping a lot of balls in the air.” 

What This Means for Organizations—and How an “Ai Practice” Can Help

This has a way of producing self-reinforcing cycle also known as a “doom loop.”

Ai accelerated certain tasks, which raised expectations for enhancing team speed and higher productivity; higher speed made workers more reliant on Ai. Increased reliance widened the scope of what workers attempted, and a wider scope further expanded the quantity and density of work. 

Employees don’t feel less busy, and in some cases, felt busier than before!

As a result, organizations might see this voluntary expansion of work as a clear win.

“You don’t get something for nothing”

What looks like higher productivity and speed in the short run can mask silent workload creep and growing cognitive strain as employees juggle multiple Ai-enabled prompts & workflows.

Over time, overwork can impair judgment, increase the likelihood of errors, and make it harder for organizations to distinguish genuine productivity gains from unsustainable intensity.

The cumulative effect is fatigue, burnout, and a growing sense that work is harder to step away from.

Take a disciplined & systematic approach to Ai practice

Intentional pauses

Review your team’s Ai cadence. Protect natural intervals to continually assess alignment with team goals cross-functionally. This way the team stays on track a.) in use of Ai prompts and at the same time focused on the objective.  Additionally, intervals allow us to constructively consider and/or challenge assumptions, perhaps discuss even more “worthy” prompts, while at the same time absorb information before moving forward.

Said another way, and in the words of a photographer, we open up the aperture just enough to allow needed light on the subject.

Sequencing

Sequencing encourages work to advance in coherent phases, fewer costly and time-consuming context switches, while teams maintain overall throughput and focus on the goal or objective. 

Human grounding

Short opportunities to connect with others—one-on-one, via taskforce or as a team whether through brief check-ins, shared reflection moments, or structured dialogue. This acts as a “pattern interrupt” on continuous solo engagement with Ai tools.