“We have to change. We need to be more productive.”
Fine. But why does every proposal for a different way of working hit a wall?
Then comes the wonderfully revealing question: “What will you do with the time you save?”
Better work. Fix the next bottleneck. Or go home on time. If keeping people busy is the objective, why stop at banning AI? Ban email, handwrite a letter to every colleague, then walk across the building and read each one aloud. The hours would be spectacular. The output, less so.
That joke sums up a real frustration: a workplace can talk endlessly about productivity while rewarding the appearance of effort instead of the value produced.
1. The motivation had already disappeared before the first assignment
The first week of a new job felt promising. There was energy and curiosity.
In the following week, however, the training began to feel repetitive. Different instructors delivered much the same material, with little sense of a shared plan. The question gradually changed from “What will I learn?” to “Has anyone designed this training as one coherent experience?”
By the day of the first assignment, enthusiasm had already dropped. The actual job then introduced manual spreadsheet entries, one-by-one checks, and hard-to-follow documents.
The timing matters. Rejected improvement ideas did not cause the initial loss of enthusiasm. That had started in training. The later experiences reinforced an impression already forming: perhaps this organization would keep consuming effort without seriously reviewing the method.
2. “Innovate” is a strange instruction when experimentation is off-limits
Managers called for change, but conversations about using AI tended to end with “information security won't allow it.” The discussion rarely progressed to identifying safe tasks, acceptable data, or a controlled pilot.
Security is a real requirement. A model running locally still needs access controls, maintenance, legal review where relevant, output checks, and protection against data leaks. “I can run it” is not the same as “an organization can use it safely.”
But that argues for assessing the risks, not abandoning the assessment. Define data boundaries. Test a low-risk use case. Measure benefits and failure modes. Make a documented decision.
The US National Institute of Standards and Technology offers a framework for governing, mapping, measuring, and managing AI risks. Between “roll it out everywhere” and “ban everything,” there is a great deal of engineering.
3. Saved time is not an embarrassing problem to explain away
Imagine producing the same quality report in 30 minutes instead of four hours. The three and a half hours saved are an illustrative calculation, not a measurement from a particular employer.
That time could go toward a better process, more careful quality checks, customers, or lower overtime. OECD's productivity measure is based on output per hour worked. Producing the same value in less time is precisely the sort of improvement worth noticing.
Yet in some workplaces, the question “What will you do with the extra time?” carries an accusation: surely you plan to slack off.
A legitimate manager can ask how resources will be allocated. The distinction is whether they are trying to plan useful work or trying to prove that every minute must remain occupied.
If merely filling hours is the goal, hand-delivered letters win. Thankfully, workplaces adopted email because results matter.
4. When good performance is rewarded with more work, people learn the lesson
Finish a task early and another arrives. Finish that one, and another arrives. There is no visible ceiling on the workload, while pay, autonomy, or protected time barely changes.
The incentive becomes perverse: working efficiently can mean losing the very time your efficiency created.
Research on effort–reward imbalance examines why high effort combined with inadequate returns can be harmful. It does not prove the exact causes of one worker's experience, but it gives us a useful question: who actually benefits from the improvement?
A better system protects workload limits and shares the gains through development opportunities, recognition tied to real results, and time that employees can reclaim.
5. Sometimes the useful question is “Why does this task exist?”
One response to slow data entry is faster data entry. Another is asking why the data must be entered twice.
You can speed up a document review. You can also ask why it requires so many rounds of approval.
Organizational learning research distinguishes fixing an action within existing rules from reconsidering the rules and assumptions themselves. That deeper questioning is powerful, though it can be wrong when unseen legal, safety, or operational constraints matter.
A small automated publishing project illustrates the alternative. If publishing personal information is dangerous, establish exclusion rules and review checks rather than abandoning publishing altogether. Those safeguards still need testing; they are not a guarantee.
The point is not “ignore the restriction.” It is “find out what the restriction protects, then design around it where possible.”
6. Your career is built out of hours you never get back
This is bigger than a spreadsheet. Years spent performing company-specific rituals can leave fewer portable skills than years spent solving problems, learning tools, and improving systems.
Titles and certificates may open doors. Praise feels good. But “great job” is not a substitute for becoming more capable or keeping future options open.
That is also why recommending an employer to someone you care about can feel like a serious test. It is one thing to tolerate your own job. It is another to believe the conditions might genuinely help somebody else. They still need to check the role and terms for themselves.
Conclusion: It's not about avoiding work. It's about not wasting a life
Meaningful challenges can be engaging, even when they are difficult. What drains motivation is being told to innovate while the tools, decisions, and incentives all push in the opposite direction.
“So what will you do with the time you've saved?”
Create something better. Develop skills you can take with you. Sometimes, simply finish on time.
And if management still needs proof that everyone is working hard, I have a proposal: a company-wide meeting on replacing email with handwritten letters. Quill pens optional.
References (5)
- OECD, “Labour productivity and utilisation”: https://www.oecd.org/en/data/indicators/labour-productivity-and-utilisation.html
- NIST, “AI Risk Management Framework”: https://www.nist.gov/itl/ai-risk-management-framework
- Auqui-Caceres et al., “Revitalizing double-loop learning in organizational contexts” (2023): https://doi.org/10.1111/emre.12615
- Siegrist, “Adverse health effects of high-effort/low-reward conditions” (1996): https://pubmed.ncbi.nlm.nih.gov/9547031/
- Burris, “The Risks and Rewards of Speaking Up” (2012): https://doi.org/10.5465/amj.2010.0562
