Possibly—but the available evidence does not prove that workplace AI strategies cause employees’ critical-thinking skills to decline. What it does show is a gap between employees’ concerns about skill erosion and leaders’ expectations that staff will supervise, validate, and sometimes override AI. If your strategy teaches people how to use a tool but not when to question it, who owns the decision, or how to check the result, it leaves a crucial part of the job undefined.
What the evidence says—and what it does not
IBM’s September 21, 2026 announcement of its CHRO study says the research surveyed 1,500 CHROs and senior executives and 8,800 employees globally. IBM reports that 60% of employees worry their skills are eroding, with critical thinking cited most often. That is a report of concern, not a measurement showing that AI caused a loss of critical-thinking ability.
The same IBM release reports a difference in priorities: 71% of CHROs identify supervising, validating, and overriding AI outputs as an essential workforce skill, while 29% of employees rank judgment as important. These are survey responses, not an objective test of how well employees judge AI output. IBM SVP and CHRO Nickle LaMoreaux said, “AI is changing not only how work gets done, but where people can contribute the greatest value.”
Gartner’s May 13, 2026 announcement describes its 1Q26 Global Labor Market Survey of 12,004 employees and managers across 40 countries. Gartner reports that people proficient with AI across multiple use cases were more likely to report high productivity, quality work, and effective process improvements. That association does not establish that broader AI use caused those outcomes. Gartner’s practical point is that access or adoption alone can miss whether people use AI effectively and across varied tasks.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Together, these findings justify a closer look at work design and training. They do not establish that a particular AI strategy causes cognitive decline, or identify a proven training regimen that prevents it over time.
Define the work before you teach the tool
IBM reports that organizations clearly defining workflows as human-led, AI-assisted, or AI-executed also reported 18% risk reduction and 20% quality improvement. Those are outcomes reported alongside workflow definition; the release does not show that labels alone caused the improvements. The categories are still a useful starting point for making responsibility visible. The working definitions below are practical recommendations, not a claim that IBM prescribes these exact role boundaries.
| Workflow type | AI’s role | People should | Decision authority and accountability |
|---|---|---|---|
| Human-led | Offer limited support, such as organizing information or suggesting options. | Frame the problem, judge the evidence, and decide whether to use the suggestion. | A designated human decision-maker retains authority and responsibility for the decision. |
| AI-assisted | Draft, analyze, summarize, or recommend within a defined task. | Check material claims and assumptions, resolve uncertainty, and revise or reject the output as needed. | A designated person approves the work before it is relied on; the organization should identify who owns that approval. |
| AI-executed | Complete a bounded, sufficiently specified task with limited routine intervention. | Set the limits, monitor performance, and know when an exception requires review or a pause. | The organization should name an accountable owner and define escalation and override authority before deployment. |
These categories should describe a workflow, not serve as blanket permission to trust or ignore AI. A task may shift from AI-assisted to human-led when the stakes rise, the input is incomplete, or the output falls outside the system’s intended use. For each workflow, make the handoff and override point explicit: who checks what, what counts as a problem, and who has authority to stop or correct the work.
What AI training should teach beyond prompts
Training that stops at tool operation can teach employees how to produce an answer without preparing them to decide whether it is fit for use. Tie instruction to the actual workflow and the responsibility the employee holds in it. The UK Department for Education’s employer guide, “What works for AI upskilling in the UK,” is practical, UK-specific guidance informed by 23 workshops, 10 case studies, and a 536-response employer survey. Those inputs make it useful context for employers, not proof of a universal training model.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Task framing: Have employees specify the problem, relevant context, constraints, and intended use before asking AI for help. A polished answer to the wrong question is still a poor result.
- Verification: Teach staff to check consequential claims against appropriate evidence or source material, identify missing context, and distinguish a plausible response from a verified one.
- Judgment and limits: Use examples from the role to practice deciding when AI is suitable, when a person must make the call, and when uncertainty or risk calls for escalation.
- Override and escalation: Explain how to reject or correct an output, where to report a recurring problem, and who can pause or override an AI-assisted process.
- Human accountability: Tell employees which decisions remain theirs and which require approval. Do not leave staff to infer responsibility from the fact that a tool produced a recommendation.
- Changing work: Communicate how tasks and skills may change as AI use expands. Gartner recommends transparent, ongoing communication about jobs and skills alongside clear human-AI norms.
Use realistic practice cases rather than prompt exercises alone. Ask learners to inspect an output, find a consequential error or unsupported assumption, decide whether to use it, and explain their decision. The aim is not to make every employee a technical AI specialist; it is to build the judgment needed for the work they are expected to perform.
Measure quality and judgment, not just adoption
Counting licenses, logins, or AI-assisted tasks can show whether a tool is being used; it cannot, by itself, show whether employees are using it well. Gartner’s findings support looking at the depth and diversity of use as well as access. For a fuller picture, pair adoption measures with checks tied to the workflow:
Rank #4
- Output quality: Are completed tasks accurate and fit for their intended use? Choose checks that match the consequences of error.
- Verification behavior: Do employees check important claims and recognize when an output needs human review?
- Escalation and overrides: Can staff identify situations that require a person to intervene, and do they know how to do so?
- Workforce confidence: Do people understand where they remain responsible, and do they feel prepared for that responsibility?
- Range of use: Is AI applied across suitable tasks, rather than simply generating more use in one narrow activity?
These are practical management questions, not a validated scoring system. Interpret them in the context of the task and its risks; a high number of uses is not inherently a sign of better work.
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For each workflow where employees use AI, leaders can ask:
Best Value
- Is the work human-led, AI-assisted, or AI-executed—and does everyone involved understand what that means?
- Who frames the task, checks the output, makes the final decision, and owns the result?
- What errors, missing information, uncertainty, or higher-stakes conditions require a person to review, override, or stop the process?
- Does training let employees practice those decisions using realistic work, not just learn the tool’s controls?
- Do measures show quality and responsible judgment as well as adoption?
- Are leaders communicating how the work and required skills may change?
If those answers are missing, the issue is not proof that employees have stopped thinking. It is that the strategy may be rewarding use of AI without clearly defining the human thinking the work still requires.
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