Cutting your mental load with AI: the personal-effectiveness method

By Paul-Antoine TUAL, AI Transformation Leader, Croissance et Transitions, August 2026.

Stance. I have kept the practices that help me work with agents, while narrowing their rationale to what the evidence can support: a variable effect on time, sometimes lower perceived effort and a verification burden that needs organising.

• I use research findings as boundaries, not as universal proof of productivity.
• I distinguish time saved, perceived effort, output quality and review cost.
• I present the method as a personal discipline to test on your own work.

Measuring time, effort and quality

The impression that AI makes the day feel lighter can be sincere without establishing a time saving, because studies examine different tasks, populations and forms of assistance.

• Experience describes how the work feels; it does not replace timing it.
• Speed depends on difficulty, tool proficiency and verification cost.
• A credible method tracks duration, quality, rework and perceived effort separately.

The preregistered study by Yu et al., presented at the Cognitive Science Society in 2026, mainly shows a gap between anticipated and measured speed on simple cognitive tasks across 1,237 participants [1].

• Completion time did not differ significantly between independent and AI-assisted conditions across the main set of simple tasks.
• Participants nevertheless expected AI assistance to be faster and underestimated the time it would take.
• Perceived effort fell despite comparable completion times, so comfort and acceleration should be measured separately.
• The few difficult tasks suggest that difficulty may change the effect, without supporting a general claim about executive work.

METR’s experiments on software development show why one figure ages badly: its first trial measured a slowdown, while the follow-up suggested acceleration but did not, in METR’s view, provide a reliable estimate of the current effect [2][3].

• In 2025, 16 experienced developers completed 246 tasks 19% more slowly with AI, while believing they had saved time.
• In 2026, the original cohort showed an estimated 18% speed-up, with a confidence interval from a 38% gain to a 9% loss.
• The newer experiment covered 57 developers, 143 repositories and more than 800 tasks, with acknowledged selection and measurement biases.
• METR now calls the 2025 result outdated for current tools, without endorsing a replacement percentage.

These results support neither a general promise of acceleration nor a dismissal of AI as mere comfort; they call for measures that make its distinct effects on work visible.

• Time the whole workflow, including instruction, waiting, review and rework.
• Assess quality against criteria set before execution.
• Record perceived effort under a consistent protocol to see whether the day actually feels lighter.
• Reserve the word “productivity” for an observed improvement in outputs relative to resources.

Organising delegation to preserve judgement

AI often moves part of the work into what I call the “verification tax”: the accountable person may no longer produce every line, but must still establish that the output is fit for use.

• Producing builds an answer; verifying searches for omissions, errors and hidden assumptions.
• Fluent wording can ease reading without guaranteeing accuracy.
• Review therefore needs criteria, sources and a named owner.
• The article on asynchronous work develops this move from execution to supervision.

The sense of ease becomes hazardous when it doubles as approval criteria, because an output that reads well may still be wrong, incomplete or unsuitable for its context.

• Identify which claims need sources.
• Compare the output with a reference example or a second proposal.
• Make uncertainties and decisions reserved for a person explicit.

The method divides this burden into three distinct problems so that each routine addresses a specific mechanism rather than a general promise of efficiency.

• A decision session reduces switching during consequential judgements.
• Structured supervision organises delegation and verification.
• A suitable communication channel reduces friction between expression and control.

Technique no. 1: the decision session

In my practice, the decision session runs from 8 a.m. to noon and separates consequential judgements from autonomous execution, although the clarity of the boundary matters more than the time of day.

• During the session: judgement calls, architecture choices, plan approvals and written mandates.
• After the session: autonomous execution within the authorised scope.
• Late in the day: one batched review window to accept, correct or stop.
• In another organisation: move the windows rather than copy my timetable.

Two popular explanations do not justify a universal morning slot: large replications weaken the simple ego-depletion account, and a recent study finds no general, robust benefit from matching chronotype to time of day [4][5].

• The 2016 replication, conducted in 23 laboratories with 2,141 participants, reported a very small effect (d = 0.04).
• The 2021 preregistered study, conducted in 36 laboratories with 3,531 participants, reported d = 0.06 and data four times more likely under the null.
• These findings challenge a general “stock of willpower” model without proving that subjective fatigue or individual variation never occurs.
• Rey-Mermet and Rothen found no general, robust synchrony effect at the latent-variable level.

The better-supported argument is structural: reducing context switches limits opportunities for unfinished work to retain attention, although these historical measurements do not predict the exact cost of your own day [6][7].

• Leroy describes attention residue when someone moves from an unfinished task to another, especially when the first was under time pressure.
• Mark and colleagues observed 24 knowledge workers spending an average 11 minutes 4 seconds in one working sphere.
• In that small field study, 57% of working spheres were interrupted.
• Interrupted work resumed on the same day was picked up an average 25 minutes 26 seconds later, after 2.26 other spheres.

The often-repeated claim of “23 minutes and 15 seconds to refocus” does not appear in Mark’s paper and should be replaced with the published measure, its context and its wide dispersion [7].

• The study measures a resumption delay, not a universal time needed to recover concentration.
• The published mean is 25 minutes 26 seconds for work resumed on the same day.
• The standard deviation is 54 minutes 48 seconds, so the mean should not be applied to an individual interruption.

The decision session works chiefly as a calendar rule: choose the period you can genuinely protect and reduce interruptions around the most demanding judgements.

• Keep notifications, email and status meetings outside the window.
• Batch decisions that share the same dossier or constraints.
• Admit emergencies through written criteria rather than ad hoc requests.
• After one week, check whether switching and resumption have actually decreased.

The session produces a mandate precise enough to enable execution and bounded enough to stop the agent before a consequence that only the accountable person can authorise.

• Objective: the intended outcome and its recipient.
• Scope: authorised files, data, tools or people.
• Quality: acceptance criteria, sources and expected checks.
• Limits: forbidden assumptions, escalation cases and the stop point before irreversible action.
• The articles on the decision-complete plan and Decision-Gated Development develop this mechanism.

Technique no. 2: in the afternoon, manage your agents like a team

Supervising agents borrows clarity of roles, delegation and accountability from management, while requiring controls suited to systems that can act quickly without understanding the human context.

• Frame the work before it begins.
• Allow execution to proceed within explicit limits.
• Batch review without delaying a necessary control.
• Keep consequential decisions and actions under human authority.
• The article on asynchronous work develops this supervisory responsibility.

The mandate works as an execution contract: it makes delegation controllable without assuming that an informal conversation will keep the work on course.

• It defines the outcome, scope and acceptance criteria.
• It states when to proceed, request a decision or stop.
• It leaves a shared record for review and process improvement.
• The end of prompt engineering explains this move towards structured instructions.

Reviewing every intermediate output recreates the interruptions the method is intended to reduce, except where a risk or dependency requires action before the scheduled review.

• Disable progress notifications that require no decision.
• Escalate a blocker, scope drift or irreversible action immediately.
• During execution, work on something independent that can tolerate the occasional escalation.

A batched review turns a stream of notifications into one coherent control step, where several outputs can be assessed against the same criteria.

• Bring together the output, sources, tests and reported deviations.
• Compare alternatives where a design or strategic decision remains.
• Record acceptance, the correction requested or the reason for rejection.
• Review immediately when delay would create a material risk.

The operational risk here is uncritical approval; research on problematic AI use remains too heterogeneous to equate frequent use with addiction, while automation bias is well documented [8][9].

• The 2026 scoping review covers 37 empirical studies and criticises instruments that sometimes measure frequency or preference rather than functional harm.
• Parasuraman and Manzey’s synthesis reports automation complacency and bias among novices and experts.
• Their analysis suggests that training and general instructions do not always prevent these biases; it does not show that either is useless in every setting.
• Concrete safeguards include review criteria, comparison, a stop point and named approval.

Maintaining some direct practice is a sensible precaution for preserving judgement criteria, although the available studies concern learning or writing tasks and do not establish a universal frequency for working without AI [10][11].

• The 2025 randomised study compares four forms of support on a writing task and examines motivation, process and performance.
• The MIT EEG study remains a preprint with 54 participants, only 18 in its final session, in a narrow educational setting.
• In practice, periodically complete or audit a critical task without assistance to test your own internal standard.
• Set the frequency according to the profession, expertise and cost of error rather than an arbitrary weekly rule.

Deferred review is safe only when potentially irreversible actions wait for approval or remain recoverable until the next control point.

• Block client email delivery pending approval.
• Require authorisation for a payment, deletion or production change.
• Grant technical rights for the mandate, then remove them when the work ends.
• Size stop points to the severity and speed of possible harm, as detailed in the human-in-the-loop article.

Technique no. 3: dictate instructions and review written outputs

Channel choice becomes simpler when you separate expressing an intention from receiving an output for review, then fit the format to the task and its setting.

• Voice can speed up capture of a rough thought.
• Text supports detailed checking and comparison.
• A diagram reveals relationships that prose can hide.
• Audio and video mainly support sequential presentation.

Available measurements show that speech recognition can transcribe faster than a keyboard in certain protocols, without establishing that it produces a precise thought or a better instruction more quickly [12][13].

• In 2016, English voice input was three times faster than a small phone keyboard, with a 20.4% lower error rate.
• In 2025, a preprint involving more than 1,000 clinicians measured medians of 93 words per minute for automatic dictation and 21.4 for typing.
• The latter protocol uses a standard 74-word passage, so it measures transcription rather than composition.
• Use voice to capture, then add structure, constraints and approval in a format that can be reread.

For checking an output, writing offers navigation and comparison that average reading speed cannot capture, although Brysbaert’s meta-analysis provides a useful reference point [14].

• Across 190 studies and 18,573 participants, the reported mean for silent non-fiction reading is 238 words per minute.
• Reading aloud averages 183 words per minute across 77 studies and 5,965 participants; it is not the same task as listening to a recording.
• A document allows you to search a term, revisit a clause and compare two versions side by side.
• A linear stream can present an approved decision, but is less suitable for detailed audit of its content.

My practical rule is “dictate to capture, read to approve”, with exceptions for the precision required, confidentiality and accessibility.

• Dictate a rough intention, then have it restructured without delegating the missing decisions.
• Move to text or a diagram to fix the mandate and make differences visible.
• Use audio or video to distribute content after it has been checked.
• Keep the final decision in a medium that preserves evidence and version history.

Three questions for your Monday morning

One week of observation is enough to test whether the method reduces your burden without lowering quality, using three concrete questions.

• How often does your attention switch because an agent requests intervention or announces an output?
• Which recent AI output did you approve without applying your review criteria, and what signal persuaded you?
• Which critical skill can you still exercise without assistance, and when did you last test it?

The intended result is more judgement capacity when consequential decisions arise, assessed as carefully as any claimed time saving.

• Compare a baseline week with a week using batched windows.
• Track interruptions, total time, detected errors and perceived effort.
• Keep the routine only if it improves that set without shifting the cost to somebody else.

The free Junyr AI maturity audit uses a 30-minute video call to position your organisation, identify its main blocker and choose a sensible first project, followed by a one-page deliverable.

• Position on the Junyr Scale™ based on current uses, data, governance and internal skills.
• Identification of the main obstacle to the next level.
• Selection of a first project that fits your context.

Further reading

• The general frame: Asynchronous work: in the age of AI agents, everyone becomes a manager, the origin of the verification tax and the deskilling risk.
• The decision mechanics: Decision-Gated Development, on why the bottleneck moved from execution to decision, and We removed Git from a product in production, the plan registry that follows from it.
• The safety constraint: Engineering agentic systems: human in the loop, to size the stop points before irreversible actions.
• Writing the mandates: The end of prompt engineering, on the shift from conversation to structured command.

Paul-Antoine TUAL, AI Transformation Leader · Croissance et Transitions (SAS) · Junyr Method™ · Junyr Mail™ · Junyr Agents™

---

Sources

[1] Sunny Yu, Myra Cheng, Ahmad Jabbar, Ilia Sucholutsky, Katherine M. Collins, Dan Jurafsky, Robert D. Hawkins, “Cognitive offloading and the speedup illusion in human-AI interaction”, arXiv:2605.23177, 22 May 2026, accepted at the 48th Annual Meeting of the Cognitive Science Society. N = 1,237 participants (401 in the prediction sample, 836 in the completion sample). Assistance shortens time only on difficult tasks, three in the protocol, and not at all on simple ones, where it still reduces subjective effort; actual time exceeds predicted time by nearly a minute in the assisted condition; the bias is absent when the imagined help is human, participants then anticipating a saving of about seventeen seconds, close to reality. Affiliations: Stanford (Yu, Cheng, Jabbar, Jurafsky, Hawkins), New York University (Sucholutsky), MIT and Princeton AI Lab (Collins). A preprint accepted at a conference, to be distinguished from a journal article. https://arxiv.org/abs/2605.23177

[2] METR, “Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity”, 10 July 2025, arXiv:2507.09089. Randomised controlled trial, 16 experienced developers, 246 tasks, repositories of more than 22,000 stars to which they had contributed for years: 19% more time with AI tools, while they expected a 24% gain before the trial and still estimated a 20% gain afterwards. METR has since placed a warning on this page: “These results are out of date.” https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/

[3] METR, “We are Changing our Developer Productivity Experiment Design”, 24 February 2026. Continuation of the study: 57 developers, 143 repositories, more than 800 tasks, including 10 developers from the original study. For those, an estimated 18% gain with a confidence interval running from a 38% gain to a 9% loss; for newly recruited developers, a 4% gain with an interval from a 15% gain to a 9% loss. Both intervals cross zero. Quotation used in the article: “We have observed a significant increase in developers choosing not to participate in the study because they do not wish to work without AI, which likely biases downwards our estimate of AI-assisted speedup.” https://metr.org/blog/2026-02-24-uplift-update/

[4] Kathleen D. Vohs, Brandon J. Schmeichel et al., “A Multisite Preregistered Paradigmatic Test of the Ego-Depletion Effect”, Psychological Science, vol. 32 no. 10, 2021, pp. 1566-1581: 36 laboratories, 3,531 participants, non-significant effect (d = 0.06), Bayesian analysis finding the data four times more likely under the null hypothesis. Follows the registered replication report by Martin Hagger et al. (2016), 23 laboratories, 2,141 participants, d = 0.04. https://journals.sagepub.com/doi/abs/10.1177/0956797621989733

[5] Alodie Rey-Mermet, Nicolas Rothen, “The Interplay of Time-of-day and Chronotype Results in No General and Robust Cognitive Boost”, Collabra: Psychology, vol. 9 no. 1, article 88337, 2023. No general and robust synchrony effect at the latent-variable level; the authors conclude a probable methodological artefact. https://online.ucpress.edu/collabra/article/9/1/88337/197502/

[6] Sophie Leroy, “Why is it so hard to do my work? The challenge of attention residue when switching between work tasks”, Organizational Behavior and Human Decision Processes, vol. 109 no. 2, 2009, pp. 168-181. A founding classic, cited as such. No percentage drop in performance is advanced in that paper: the “30 to 40%” figures circulating online are not in it and are not used here.

[7] Gloria Mark, Victor M. González, Justin Harris, “No Task Left Behind? Examining the Nature of Fragmented Work”, CHI 2005, University of California, Irvine. Direct observation of 24 knowledge workers: 11 minutes 4 seconds on average per working sphere before switching, 57% of spheres interrupted, 77.2% of interrupted work resumed the same day, on average 25 minutes 26 seconds later (standard deviation 54 minutes 48 seconds) and after 2.26 other spheres. Checked against the original text: the figure of “23 minutes and 15 seconds” widely attributed to this study does not appear in it. https://ics.uci.edu/~gmark/CHI2005.pdf

[8] Francesca Maria Dagnino, Chiara Fante, Vittorio Guerrieri, Marcello Passarelli, “Addicted, attached, or just delegating? A scoping review on ‘problematic artificial intelligence use’”, Frontiers in Psychology, 9 July 2026, DOI 10.3389/fpsyg.2026.1900953. Scoping review of 37 peer-reviewed empirical studies. Quotation used in the article: “instruments index frequency or preference […] rather than impairment, thereby conflating efficient, augmentative use with dependence.” https://doi.org/10.3389/fpsyg.2026.1900953

[9] Raja Parasuraman, Dietrich H. Manzey, “Complacency and Bias in Human Use of Automation: An Attentional Integration”, Human Factors, vol. 52 no. 3, 2010. Reference synthesis, cited here as a founding classic: automation complacency and automation bias are observed among novices and experts alike, and are prevented neither by training nor by instruction. https://journals.sagepub.com/doi/10.1177/0018720810376055

[10] Yizhou Fan, Luzhen Tang, Huixiao Le, Kejie Shen, Shufang Tan, Yuan Zhao et al., “Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance”, British Journal of Educational Technology, vol. 56 no. 2, 2025, pp. 489-530, DOI 10.1111/bjet.13544. Randomised experimental study comparing four support conditions on a writing task. https://bera-journals.onlinelibrary.wiley.com/doi/10.1111/bjet.13544

[11] Nataliya Kosmyna et al., “Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task”, MIT Media Lab, 2025. 54 participants across sessions 1 to 3, only 18 of them in session 4; EEG, three groups (assistant, search engine, no tool). Not peer reviewed, small sample, single task in an educational setting: flagged here with those reservations. https://www.media.mit.edu/publications/your-brain-on-chatgpt/

[12] Sherry Ruan, Jacob O. Wobbrock, Kenny Liou, Andrew Ng, James Landay, “Speech Is 3x Faster than Typing for English and Mandarin Text Entry on Mobile Devices”, arXiv:1608.07323v1, 25 August 2016. Voice entry 3.0 times faster in English and 2.8 times faster in Mandarin than a miniature smartphone keyboard; error rate 20.4% lower in English and 63.4% lower in Mandarin. Deep Speech 2 recognition, iOS QWERTY and Pinyin keyboards. A founding classic of the field, cited as such: the measurement covers phone keyboards, not a full keyboard. Referencing note: version 2, filed in January 2018, carries a different title, “Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones”. It is version 1 that is cited here. https://arxiv.org/abs/1608.07323v1

[13] “A multi-country study comparing typed to automatic speech recognition-based medical documentation speeds among Low- and Middle-Income Country Trained Clinicians”, medRxiv 2025.05.11.25327386, 13 May 2025. More than 1,000 clinicians and health workers, more than 60 facilities, more than 15 countries: median typing of 21.4 words per minute against 93 with automatic dictation, a factor of 4 to 5. Preprint, not peer reviewed; the protocol rests on reciting a standardised 74-word passage, which measures transcription rather than composition. https://www.medrxiv.org/content/10.1101/2025.05.11.25327386v1.full

[14] Marc Brysbaert, “How many words do we read per minute? A review and meta-analysis of reading rate”, Journal of Memory and Language, vol. 109, 2019. Meta-analysis of 190 studies and 18,573 participants: 238 words per minute for silent reading of non-fiction, 260 for fiction, and 183 words per minute for reading aloud (77 studies, 5,965 participants). The author notes that these values are lower than the figures commonly cited. https://biblio.ugent.be/publication/8647789