Part of “The Operator’s View: AI Workforce”
Part 2 — Article 9
3 min read
When Low Value Work Stops Getting Done
What disappears as systems optimise
AI systems optimise for efficiency. As they do, not all work is treated equally.
Tasks that are repeatable, measurable, and high confidence are prioritised. Tasks that are ambiguous, low impact, or difficult to evaluate are deprioritised.
Over time, low value work begins to disappear from the system.
This does not happen through decision. It happens through optimisation.
In operations, this pattern is familiar. When teams are under pressure, essential but low visibility tasks are delayed. Documentation, maintenance, and edge case handling are pushed aside.
The same dynamic applies here.
As optimisation increases:
measurable work improves
non-measured work declines
gaps form quietly
These gaps are rarely visible in reporting. Because reporting reflects completed work, not omitted work.
Research across labour and automation systems shows that efficiency driven environments consistently deprioritise low-visibility tasks (OECD labour studies).
Over time, this creates hidden risk, systems appear efficient while resilience declines.
Operators need to actively identify what is not being done. Because that is where exposure builds.
Efficiency does not remove work. It changes what survives inside the system.
Continue the series
Previous: AI is Coordinating
Next: AI takes Control
References
OECD — Labour Market and Automation
McKinsey — Productivity and Automation Studies
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If you would like to read more on this topic, below is expanded breakdown:
What Happens When AI Agents Stop Doing Low Value Work
Part of “The Operator’s View: AI Workforce” Part 2 Article 9 (extended)
6 min read
Asking why AI can eliminate low value work? That’s the wrong question.
It sounds like an obvious win.
Remove low value tasks = increase efficiency = improve performance.
But that assumes low value work is optional.
In real systems, it rarely is.
What people think
Organisations assume:
low value work is waste
automation should remove it
efficiency improves when it disappears
Examples typically include:
administrative tasks
edge case handling
low priority activities
On paper, removing these makes sense.
But that’s not how systems behave at scale.
What’s actually happening
As AI systems optimise, they don’t explicitly “remove” work.
They deprioritise it.
Because low value work tends to be:
harder to standardise
lower confidence
less measurable
less impactful on performance metrics
So the system naturally shifts toward:
high confidence tasks
repeatable outcomes
measurable outputs
Which means:
Low value work doesn’t disappear.
It stops getting done consistently.
Where it breaks
Most organisations assume:
“If it’s assigned, it will be completed.”
But in optimised systems:
prioritisation overrides assignment
efficiency overrides completeness
measurable outcomes override invisible work
Which leads to:
certain tasks consistently being delayed
others being skipped entirely
gaps forming without immediate visibility
This isn’t failure.
It’s optimisation doing its job.
What this looks like in the real world
In infrastructure operations, low value work is never truly low value.
It just appears that way.
Examples:
preventative maintenance
documentation updates
edge case fault handling
low frequency issues
Under pressure:
these tasks get pushed out
resources focus on high impact work
short term performance improves
But over time:
system quality degrades
issues accumulate
failure risk increases
AI introduces the same pattern, faster and more consistently.
Where this fails at scale
This is where the real impact shows up.
As AI systems deprioritise low value work:
operational blind spots grow
edge cases become systemic issues
resilience decreases
From a reporting perspective:
efficiency improves
output increases
costs reduce
But underneath:
risk accumulates
system integrity weakens
failure becomes more likely
And by the time it’s visible.
It’s no longer isolated.
Operator insights
Low-value work is often:
low visibility
not well measured
poorly understood
But it plays a critical role in:
system stability
long term performance
risk management
At scale:
systems optimise away what isn’t measured
efficiency hides degradation
short term gains mask long term risk
AI accelerates this dynamic.
If AI agents stop doing low-value work.
Then the system becomes:
more efficient
less complete
And that trade-off is rarely explicit.
Because no one decides:
“we will stop doing this work”
It happens gradually.
Through optimisation.
What this means for enterprises
Organisations are not designed to detect this shift.
They rely on:
performance metrics
dashboards
reporting
But these only capture: what is being done
Not: what is being consistently avoided
This creates a hidden gap between:
perceived performance
actual system health
What happens next
As AI systems continue to evolve:
optimisation becomes more aggressive
prioritisation becomes more refined
coordination reinforces behaviour
We will see:
low value work systematically deprioritised
systems becoming more efficient but less resilient
organisations reacting to issues after they emerge
At that point:
The cost of not doing the work becomes visible.
In closing
AI won’t just remove low value work.
It will stop it from being done.
And if you don’t understand what that work was supporting.
You won’t see the impact until the system starts to fail.
References
McKinsey — The Economic Potential of Generative AI
Stanford HAI — AI Index Report
IEA — Energy and Infrastructure Reports
OECD — AI and Productivity Dynamics
Stay Connected
If you're interested in discussions on AI or how AI is actually delivered — across infrastructure, energy, networks, materials, and supply chains — subscribe:
https://digitalbackbone-be8806.beehiiv.com/
Footnote
This article is part of a series exploring topics:
AI is constrained by physical infrastructure — and increasingly shaped by economic behaviour at scale
Disclaimer
The views expressed in this article are my own and are intended for general information and discussion purposes only. They do not represent the views of any employer, organisation, or client.
© 2026 Rodney Terry – Digital Backbone. All rights reserved.