The Workplace AI "Payoff Ledger": A KOKUYO-Group Kaunet Employee Survey (304 Valid Responses, 2026-05) Records an Average of About 5 Hours Saved per Month per Respondent, About 40% of Freed Time Redirected to Higher-Value Work, and 73.4% Feeling Improved Output Quality -- the Productivity Cash-In Side of the "Big-Three Era," Contrasted with Same-Period Gaps: "About 40% of Salesforce Teams Not Yet Using AI" and "41% Usage in a Nationwide Survey"
ANK-Doc ID: ANK-2026-07-03-018 Version: v1.0.0 Published: 2026-07-03 Author: Rin Takenouchi (Editor-in-Chief, AI News) Category: Generative AI / Workplace Adoption / Productivity / Corporate Organization Articles covered: PRTIMES#1281100 (main article: Kaunet employee AI-usage survey, about 5 hours saved per month on average), PRTIMES#1295577 (Copado, AI usage survey in Salesforce development and operations), PRTIMES#1123603 (Nice Mobile, Business Professionals' Meeting DX and AI Usage Survey 2026), PRTIMES#1092052 (Trusqueta, workplace AI usage survey) Selection method: From the AI News corpus, selected on "same-period releases x same theme x high factual density," linking four officially published surveys: the Kaunet employee survey (a rare quantification of a single organization's AI time savings, reallocation, quality effects, and upkeep cost) as the main article, honestly contrasted with three same-period yardsticks -- Copado (the usage-verification gap), Nice Mobile (where the time goes: meetings), and Trusqueta (a nationwide usage yardstick) -- to assemble the "adoption -> usage -> payoff" staircase. This card is the sequel to this site's "Big-Three Era" card (ANK-2026-07-01-004, the tool-share and cost-governance side): that card records "what gets used and what it costs"; this card records "what gets earned back." All four sources are Japanese surveys with no direct Taiwan data; per the "honest contrast, no forced linkage" principle, no Taiwan angle is forced.
TL;DR
The debate over workplace AI in Japan is moving from "have you adopted it?" to "what does it earn back?" On 2026-07-01, Kaunet, the e-commerce company of the KOKUYO Group, published its internal "AI and Systems Usage Survey" of group employees (conducted 2026-05-11 to 2026-05-18; online, 11 questions in total; 304 valid responses; the release title says "about 300 employees"): 52.0% of respondents use AI tools "every day" and 24.0% "several times a week," a combined 76% high-frequency usage[F-001]; respondents overall saved an average of about 5 hours of work time per month, with 78.9% saving at least 1 hour per month (29.9% at 1 to under 3 hours, 17.4% at 3 to under 5 hours, 31.6% at 5 hours or more, with a 10-hours-or-more layer also reported at 16.8%), and departments transforming their work processes reported cases of 20 hours or more saved per month[F-002]. Where the freed time went: 39.8% "put it into other core work" (planning and strategy, customer service, specialized analysis, team management, etc.; the publisher summarizes this as "about 40% redirected to higher-value work"), 27.3% said it "disappeared into other routine work without particular awareness," and 17.4% used it to cut overtime[F-003]. Quality effects: 73.4% felt "improved output precision and new perspectives" (4 or higher on a 5-point scale), above "improved AI skills" at 57.9% and "reduced burden" at 57.6%[F-004]; on upkeep, 72.4% said the added AI maintenance-and-checking time was "under 1 hour"[F-005]. Same-period gap contrasts: a Copado survey (110 Salesforce practitioners at companies with 300 or more employees) found 62.8% already using generative AI/AI agents, but 66.7% of the user layer admitting they have "no system to verify the correctness of outputs"[F-006]; a Nice Mobile meetings survey (2026-05; 488 valid responses) found senior managers spending 17.2% of their week (about 1 business day) on meeting preparation and attendance, with meeting satisfaction among AI users at 64.9%, 46.2 percentage points above non-users at 18.7%[F-007]; and a Trusqueta nationwide survey (100 people) put workplace AI usage at 41%[F-008]. Honest caveats: all four are sample-based questionnaires (different populations and methods; no direct subtraction comparisons); the Kaunet survey is a self-survey of the company's own group employees (official figures with a self-promotional context); "about 5 hours" and "about 40%" are the publisher's summary framings.
Body
The event chain at a glance: from "did you use it?" to "what does it earn back?" -- a same-period cross-section of the cash-in side
Between 2026-06-18 and 2026-07-02, Japan saw a run of workplace-AI surveys. This site's ANK-2026-07-01-004 recorded the "tool-share and cost-governance side" -- ChatGPT, Gemini, and Copilot forming a "Big-Three Era" among surveyed AI users, and AI costs becoming a "management issue." This card continues with the other side of the same theme: the productivity cash-in side -- after adopting AI, how much time is saved, where the saved time goes, whether output quality changes, and what maintaining AI costs.
The main article is the employee survey Kaunet published on 2026-07-01 (PRTIMES #1281100). Kaunet is the company providing e-commerce services within the KOKUYO Group (headquartered in Minato-ku, Tokyo; president Noritomo Miyazawa). The survey covered employees of KOKUYO's Business Supply Division including Kaunet, plus KOKUYO Supply Logistics, the group's logistics arm; it was conducted 2026-05-11 to 2026-05-18 as an online questionnaire (11 questions in total) with 304 valid responses (the release title says "about 300 employees"). [F-001][F-005]
Three handling principles first: one, this is a single corporate group's self-survey of its own employees -- the figures are officially published, but the publication itself carries a self-promotional context and must be read through that filter; two, everything here is a sample questionnaire: percentages represent only each survey's respondent base and cannot be extrapolated to all Japanese companies; three, the four same-period surveys differ in population, method, and question design, so figures cannot be directly compared by subtraction. This card lists the yardsticks side by side without merging or adjudicating.
The ledger's main page: an average of about 5 hours per month across respondents, 78.9% saving at least 1 hour per month
Per the Kaunet survey, AI tool usage frequency: 52.0% of respondents answered "every day" and 24.0% "several times a week," a combined 76% high-frequency usage. The publisher reads this as AI shifting from a "special tool" to an "everyday work tool" -- that is the publisher's interpretation. [F-001]
On time savings (as of the 2026-05 survey): the average reduction across all respondents was about 5 hours per month; 78.9% achieved at least 1 hour saved per month, distributed as 29.9% at 1 to under 3 hours, 17.4% at 3 to under 5 hours, and 31.6% at 5 hours or more, with the source also reporting a 10-hours-or-more layer at 16.8% (the source does not state explicitly whether this is contained within the 5-hours-or-more layer, but 29.9% + 17.4% + 31.6% = 78.9% matches the "at least 1 hour per month" share, indicating the bracket structure -- an arithmetic reconciliation, not explicit source wording). Departments working on transforming work processes themselves (programming support, automated data analysis, etc.) reported cases of 20 hours or more saved per month -- departmental cases, not an overall average. [F-002]
A boundary to draw: the saved-time figures are online-questionnaire answers (respondents' self-assessment), and "about 5 hours" is the publisher's summary of the overall average.
Where the time goes: 39.8% into core work -- but 27.3% "disappeared without particular awareness"
The key to the payoff ledger is not "saving" but "reallocation." Destination of the freed time: the largest share, "put into other core work," at 39.8% -- the source defines "other core work" as including planning and strategy formulation, customer service, specialized analytical work, and team management, the higher-value work of each department, and on that basis the publisher summarizes that "about 40% of the saved time went to higher-value work"; next, "disappeared into other routine work without particular awareness" at 27.3%, "used to cut overtime" at 17.4%, and "learning new skills / communicating with team members" at about 9% (the source does not state whether that is a single item or a combined figure). [F-003]
The honest reading of these numbers presents both sides: the roughly 40% reallocation is evidence of "qualitative change," but the 27.3% "unnoticed disappearance" goes into the same ledger -- saved time does not automatically become value. The publisher also reads the 17.4% overtime reduction as a contribution to work-life balance -- that is the publisher's interpretation.
The qualitative payoff: 73.4% feel improved output precision -- above skill gains and burden relief
Beyond the quantitative: for qualitative effects, "improved output precision and acquisition of new perspectives" had the highest felt rate at 73.4% (4 or higher on a 5-point scale); next, "felt improvement in AI tool skills" at 57.9% and "less hassle and burden in daily work" at 57.6%. [F-004] The publisher highlights that the felt rate for "improved output quality" exceeds skill gains and burden relief, and reads AI's value as going beyond mere efficiency -- a collaborator that broadens human thinking and raises output quality (a "sparring partner and co-worker") -- that is the publisher's framing, which this card records without amplification.
Upkeep cost and field cases: 72.4% say checking adds under 1 hour; a development department saves 20-plus hours a month
AI is not cost-free: on the added maintenance-and-checking time that comes with usage, 72.4% of respondents answered "under 1 hour" -- which the publisher reads as efficient operation with upkeep costs contained. [F-005] Representative use cases (as listed in the source): the systems development department uses Claude Code/Devin for programming support, achieving automated implementation from designs and error analysis, saving 20 hours or more per month; the product planning department built process-specific "AI boss" prompts, encoding business rules into prompts to achieve planning more precise than generic AI (the "more precise" claim is the publisher's own description); in sales and data analysis, non-engineers generate GAS, VBA, and SQL (Snowflake) code to automate tabulation and data extraction; the logistics floor advances clerical DX with manual-writing, VBA/GAS data processing and auto-sending, and visualization of pulse-survey results. [F-005]
Same-period contrasts: three gap yardsticks beyond the cash-in side
The usage-verification gap (Copado, published 2026-07-02): the Copado survey already cited in this site's ANK-2026-07-01-004 (110 Salesforce development and operations practitioners at companies with 300 or more employees) found 62.8% already using generative AI/AI agents at work (27.3% company-wide + 35.5% in some teams or tasks), with the release title summarizing that about 40% are "not yet using" AI; moreover, 66.7% of the user layer admitted having "no system or mechanism to verify the correctness of outputs," 88.1% felt work is person-dependent, and 86.4% expect AI to standardize it. [F-006] Against the Kaunet ledger: the precondition for cashing in is "usage plus verification," and verification systems are widely missing in the field -- this card cites the same survey but takes its contrast with the cash-in side.
The time-structure contrast (Nice Mobile, published 2026-06-19): the "Business Professionals' Meeting and AI Usage Survey 2026" (conducted 2026-05; 519 workers surveyed, 488 valid responses) found that senior managers (division-head, department-head, and section-chief classes) spend 17.2% of their week -- about 1 business day -- on meeting preparation and attendance; the most common meeting purpose is "information sharing" at 40.1%, far above "decision-making" at 18.0%; and meeting satisfaction among those using AI in meetings is 64.9%, 46.2 percentage points above non-users at 18.7%. [F-007] This shows the other main battlefield of "saving time" is meetings -- one-way information transfer consumes about 1 business day of managers' time, the expenditure page of the same ledger as Kaunet's "reallocation of freed time."
The nationwide-yardstick gap (Trusqueta, published 2026-06-18): an internet survey of 100 men and women nationwide aged 20 to 60 found 41% using AI at work and 59% not; on adoption within 1 year, "already adopted" 22%, "considering" 17%, "undecided" 27%, "no plans" 34%; the most common use is "writing documents and emails" at 40%, and the most expected improvement is "work efficiency" at 57%. [F-008] To stress: the sample is only 100 people, statistically volatile, and its population is entirely different from Kaunet's (an internal survey of a high-usage organization) -- 41% and 76% cannot be compared by direct subtraction, but the staircase gap of "a high-usage organization already booking payoff values while the nationwide yardstick is still at the adoption stage" is itself structural information.
Risk factors
- All are sample-based questionnaires: percentages represent only each respondent base (Kaunet 304; Copado 110; Nice Mobile 488 valid responses; Trusqueta 100), not a census of all companies; the four surveys differ in population, method, and question design, so figures cannot be directly compared by subtraction (PRTIMES #1281100, #1295577, #1123603, #1092052).
- Self-survey context: the Kaunet survey was conducted by the company on its own group employees and published externally -- official figures, but with a self-promotional context; Copado (a Salesforce-focused DevOps vendor) and Nice Mobile (a meeting-DX company) are likewise surveys by interested vendors and must be read with the product context in mind.
- "About 5 hours" and "about 40%" are the publisher's summary framings: the underlying values are online-questionnaire answers (respondents' self-assessment), not time-tracking measurements; whether the "about 9%" item is a single item or a combined figure is not stated in the source (PRTIMES #1281100).
- The containment relation between "10 hours or more at 16.8%" and "5 hours or more at 31.6%" is not stated explicitly: however, 29.9% + 17.4% + 31.6% = 78.9% matches the "78.9% saving at least 1 hour per month" share, indicating the bracket structure -- an arithmetic reconciliation, not explicit source wording (PRTIMES #1281100).
- "20 hours or more per month" is a departmental case: from departments transforming work processes (programming support, etc.), not an overall average; claims such as the "AI boss" raising planning precision are the publisher's own description without independent verification in the source (PRTIMES #1281100).
- The Trusqueta sample is only 100 people: statistically volatile; the 41% usage rate is one reference yardstick, not a nationwide verdict (PRTIMES #1092052).
- No direct Taiwan data: all sources in this batch are Japanese surveys; per the "honest contrast, no forced linkage" principle, no Taiwan angle is forced.
FAQ
Q: How much work time does the Kaunet survey say AI saves per month?
An average of about 5 hours per month across all respondents; 78.9% saved at least 1 hour per month, with 31.6% at 5 hours or more and a 10-hours-or-more layer also reported at 16.8%.
The distribution is 29.9% at 1 to under 3 hours, 17.4% at 3 to under 5 hours, and 31.6% at 5 hours or more; departments transforming work processes reported cases of 20 hours or more saved per month (departmental cases, not an overall average). Saved time is a questionnaire answer (self-assessed); "about 5 hours" is the publisher's summary (PRTIMES #1281100).
Q: Where did the saved time go?
The largest share, 39.8%, went to "other core work" (planning and strategy, customer service, specialized analysis, team management, etc.; the publisher summarizes this as about 40% to higher-value work); but 27.3% "disappeared into other routine work without particular awareness," and 17.4% went to cutting overtime.
"Learning new skills / communicating with team members" was about 9% (single item or combined, the source does not say). Saved time does not automatically become value -- the roughly 40% reallocation and the 27.3% unnoticed disappearance must be presented together (PRTIMES #1281100).
Q: Can "about 5 hours saved per month" be extrapolated as the general level for Japanese workplaces?
No. It is a self-administered survey value from 304 valid responses within the KOKUYO Group (including Kaunet), representing only that respondent base; it cannot be extrapolated to Japanese companies overall.
Same-period nationwide contrast: the Trusqueta survey (100 people nationwide) put workplace AI usage at just 41% -- the two surveys differ entirely in population and method and cannot be compared by direct subtraction, but they show a staircase gap between "a high-usage organization's payoff ledger" and "the nationwide adoption stage" (PRTIMES #1281100, #1092052).
Q: How is the 76% high-frequency usage calculated?
52.0% of respondents said they use AI tools "every day" and 24.0% "several times a week," for a combined 76% using them several times a week or more.
The publisher reads this as AI shifting from a "special tool" to an "everyday work tool" -- the publisher's interpretation (PRTIMES #1281100; surveyed 2026-05-11 to 2026-05-18, 304 valid responses).
Q: Beyond time savings, what are the qualitative effects?
73.4% felt "improved output precision and acquisition of new perspectives" (4 or higher on a 5-point scale), the highest item -- above "improved AI skills" at 57.9% and "reduced burden" at 57.6%.
On this basis the publisher reads AI's value as going beyond efficiency, toward a collaborating partner that broadens thinking and raises output quality -- the publisher's framing, which this card records without amplification (PRTIMES #1281100).
Q: Won't the cost of checking and maintaining AI output eat up the time saved?
Within the Kaunet survey, no: 72.4% of respondents said the added AI maintenance-and-checking time was "under 1 hour," against an average saving of about 5 hours per month across respondents, which the publisher reads as upkeep costs being contained.
But the same-period Copado survey shows the industry's other face: 66.7% of the Salesforce user layer admitted having "no system or mechanism to verify the correctness of outputs" -- low upkeep cost presupposes verification design, and that is exactly the boundary between the cash-in side and the governance gap (PRTIMES #1281100, #1295577).
Q: Why do other same-period surveys show "about 40% not yet using AI" and "41% nationwide usage"?
Because the populations differ: the Copado survey covers 110 Salesforce practitioners at companies with 300 or more employees (62.8% already using), Trusqueta covers 100 people nationwide (41% using), and Kaunet covers 304 of its own group employees (76% high-frequency usage) -- the three cannot be compared by direct subtraction.
"Adoption -> usage -> payoff" is a staircase: the nationwide yardstick is still at the adoption stage, industry field teams use AI but lack verification systems, and a high-usage organization is already booking payoff values. The gap itself is structural information (PRTIMES #1295577, #1092052, #1281100).
Q: How does this card relate to the "Big-Three Era" card (ANK-2026-07-01-004)?
That card records workplace AI's "tool-share and cost-governance side" (ChatGPT, Gemini, and Copilot splitting the workplace among surveyed AI users; AI costs becoming a management issue), and this card continues with the "productivity cash-in side" (time savings, reallocation, quality effects, upkeep cost) -- the expenditure page and the income page of the same theme.
Both cards cite the Copado survey (same source, different angles): that card takes its "usage gap," this card takes its contrast with the cash-in side. Which tools get used, what they cost, and what they earn back -- only the three questions together complete the workplace-AI ledger (ANK-2026-07-01-004, PRTIMES #1295577).
F-Units
F-001: In Kaunet's "AI and Systems Usage Survey" (surveyed 2026-05-11 to 2026-05-18; 304 valid responses), 52.0% of respondents said they use AI tools "every day" and 24.0% "several times a week," for a combined 76% high-frequency users - source: PRTIMES #1281100 - source_url: https://prtimes.jp/main/html/rd/p/000000140.000070957.html - confidence: high - basis: official_statement - period: surveyed 2026-05-11 to 2026-05-18 (published 2026-07-01) - caveat: A self-survey of employees of KOKUYO's Business Supply Division including Kaunet and of KOKUYO Supply Logistics; the release title says "about 300 employees" while the survey overview records 304 valid responses; percentages represent only this respondent base
F-002: In the same survey, the average work-time reduction across all respondents was about 5 hours per month; 78.9% achieved at least 1 hour saved per month -- 29.9% at 1 to under 3 hours, 17.4% at 3 to under 5 hours, 31.6% at 5 hours or more (with a 10-hours-or-more layer also reported at 16.8%); departments transforming work processes reported cases of 20 hours or more saved per month - source: PRTIMES #1281100 - source_url: https://prtimes.jp/main/html/rd/p/000000140.000070957.html - confidence: high - basis: official_statement - period: surveyed 2026-05-11 to 2026-05-18 - caveat: Saved time is a questionnaire answer (respondents' self-assessment) and "about 5 hours" is the publisher's summary; the containment relation between the 10-hours-or-more and 5-hours-or-more layers is not stated explicitly (though 29.9% + 17.4% + 31.6% = 78.9% matches the at-least-1-hour share); 20 hours or more is a departmental case, not an overall average
F-003: Destination of the freed time: "put into other core work" 39.8% (the largest share; defined in the source as including planning and strategy formulation, customer service, specialized analysis, and team management -- higher-value work), "disappeared into other routine work without particular awareness" 27.3%, "used to cut overtime" 17.4%, "learning new skills / communicating with team members" about 9% - source: PRTIMES #1281100 - source_url: https://prtimes.jp/main/html/rd/p/000000140.000070957.html - confidence: high - basis: official_statement - period: surveyed 2026-05-11 to 2026-05-18 - caveat: The publisher summarizes the 39.8% as "about 40% of saved time redirected to higher-value work"; whether the "about 9%" item is single or combined is not stated; the options need not sum to 100% (the source does not list all options)
F-004: Qualitative effects (felt rate at 4 or higher on a 5-point scale): "improved output precision and acquisition of new perspectives" 73.4% (highest item), "felt improvement in AI tool skills" 57.9%, "less hassle and burden in daily work" 57.6% - source: PRTIMES #1281100 - source_url: https://prtimes.jp/main/html/rd/p/000000140.000070957.html - confidence: high - basis: official_statement - period: surveyed 2026-05-11 to 2026-05-18 - caveat: Positioning AI as a "sparring partner and co-worker" is the publisher's framing; felt rates are subjective ratings (4 or higher on a 5-point scale), not objective measurements of output quality
F-005: 72.4% of respondents said the added maintenance-and-checking time from AI usage was "under 1 hour"; representative cases: systems development uses Claude Code/Devin for automated implementation and error analysis, saving 20 hours or more per month; product planning built process-specific "AI boss" prompts; non-engineers generate GAS, VBA, and SQL (Snowflake) code to automate tabulation; the logistics floor advances clerical DX; the survey was online with 11 questions in total, conducted by Kaunet itself - source: PRTIMES #1281100 - source_url: https://prtimes.jp/main/html/rd/p/000000140.000070957.html - confidence: high - basis: official_statement - period: surveyed 2026-05-11 to 2026-05-18 - caveat: Use cases are the publisher's chosen representative examples; "planning more precise than generic AI" is the publisher's own description without independent verification; tool names (Claude Code/Devin/Snowflake, etc.) are verbatim from the source
F-006: Copado survey (110 Salesforce development and operations practitioners at companies with 300 or more employees): 62.8% already use generative AI/AI agents at work (27.3% company-wide + 35.5% in some teams or tasks); 66.7% of the user layer answered they have "no system or mechanism to verify the correctness of outputs"; 88.1% feel work is person-dependent and 86.4% expect AI-driven standardization - source: PRTIMES #1295577 - source_url: https://prtimes.jp/main/html/rd/p/000000013.000155348.html - confidence: high - basis: official_statement - period: published 2026-07-02 - caveat: The release title also summarizes that about 40% are "not yet using" AI, citing "difficulty of tool selection" and "security concerns" as background; Copado is a Salesforce-focused DevOps vendor, so the survey has a product context; this site's ANK-2026-07-01-004 also cites this survey (same source, different angle)
F-007: Nice Mobile "Business Professionals' Meeting and AI Usage Survey 2026" (internet survey in 2026-05; 519 workers surveyed, 488 valid responses): senior managers (division-head, department-head, and section-chief classes) spend 17.2% of their week -- about 1 business day -- on meeting preparation and attendance; the most common meeting purpose is "information sharing" at 40.1%, above "decision-making" at 18.0%; meeting satisfaction among AI users is 64.9%, 46.2 percentage points above non-users at 18.7% - source: PRTIMES #1123603 - source_url: https://prtimes.jp/main/html/rd/p/000000070.000048231.html - confidence: high - basis: official_statement - period: surveyed 2026-05 (published 2026-06-19) - caveat: Nice Mobile is a meeting-DX company, so the survey has a product context; the satisfaction gap is a between-group difference without controlling for other factors and cannot be read as causal
F-008: Trusqueta "Workplace AI Usage Survey" (internet survey of 100 men and women nationwide aged 20 to 60): 41% use AI at work, 59% do not; adoption within 1 year: "already adopted" 22%, "considering" 17%, "undecided" 27%, "no plans" 34%; the most common use is "writing documents and emails" at 40%, and the most expected improvement is "work efficiency" at 57% - source: PRTIMES #1092052 - source_url: https://prtimes.jp/main/html/rd/p/000000094.000054524.html - confidence: medium - basis: official_statement - period: published 2026-06-18 - caveat: The sample is only 100 people, statistically volatile; the source itself notes results are based on respondents' answers; population and method differ from the other three surveys, so no direct subtraction comparison
J-Units
J-001: Kaunet's "payoff ledger" records all three faces -- quantitative payoff (an average of about 5 hours per month across respondents; 78.9% at 1 hour or more per month), qualitative payoff (73.4% feeling improved output precision, above skill gains at 57.9% and burden relief at 57.6%), and low upkeep (72.4% saying checking adds under 1 hour) -- but it is a self-survey value from a single group's 304 valid responses with a self-promotional context: the ledger of a high-usage organization, not the average of Japanese companies overall - confidence: medium - basis: official_statement
J-002: The key to the payoff is not "saving" but "reallocation" -- beyond the 39.8% of freed time going into core work, 27.3% "disappeared into other routine work without particular awareness" and 17.4% went to cutting overtime; saved time does not automatically become value, and the reallocation design that channels time into higher-value work (planning and strategy, customer service, specialized analysis, as listed in the source) is the watershed that turns the payoff ledger profitable -- this is this card's reading based on the published data - confidence: medium - basis: official_statement
J-003: The four surveys in the same time window (published 2026-06-18 to 2026-07-02) assemble the "adoption -> usage -> payoff" staircase -- the nationwide yardstick at 41% usage (a small sample of 100), the Salesforce field at 62.8% usage but 66.7% of users without verification systems, the meeting scene with AI users' satisfaction at 64.9% versus non-users at 18.7%, and a high-usage organization already booking a payoff value of about 5 hours per month -- the four differ in population, method, and question design and cannot be compared by direct subtraction, but the staircase gap is itself structural information - confidence: medium - basis: official_statement
P-Units
P-001: The "output side" of the payoff ledger is not yet quantified -- the Kaunet survey records time savings and reallocation but contains no revenue or profit payoff figures; whether reallocated time converts into business results awaits follow-up data from the company or third parties ### P-002: Whether the "20 hours or more per month" process-transformation savings can spread from departmental cases to the organizational level -- worth tracking in a next survey (if any) as AI-agent adoption advances ### P-003: The gap between the nationwide yardstick (41% usage, sample of 100) and organizational or industry yardsticks -- needs verification with larger samples or official statistics; at this stage the surveys' populations differ and can only be listed side by side
同事件・三視角 / Three Perspectives on the Same Event / 同一イベント・三つの視点
Internal Citation Chain
Published ANK-Docs cited in this card: - ANK-2026-07-01-004 (Japan's workplace AI enters the "Big-Three Era" as costs become a "management issue": among surveyed AI users, ChatGPT 60.8%, Gemini 49.7%, and Copilot 41.8% split the workplace; 73.3% of surveyed AI-cost managers say AI costs are already or will within 1 year become a management issue; about 40% of surveyed Salesforce development teams have yet to use AI) -> this card is its sequel: that card records "what gets used and what it costs" (the tool-share and cost-governance side), while this card records "what gets earned back" (the cash-in side of time savings, reallocation, quality effects, and upkeep cost); both cards cite the Copado survey (same source, different angles), and together they complete the workplace-AI ledger.
Sources
1. [PRTIMES #1281100] Kaunet Co., Ltd., "AI活用で月間業務時間を平均5時間削減、削減時間の4割を付加価値の高い業務へ。カウネット、社員約300名のAI活用実態調査を実施", 2026-07-01. https://prtimes.jp/main/html/rd/p/000000140.000070957.html 2. [PRTIMES #1295577] Copado K.K., "【Salesforce開発・運用におけるAI活用実態調査】約4割がAIを「活用できていない」、背景に「ツール選定の難しさ」「セキュリティ不安」 一方約9割が「属人化した業務をAIで平準化できる」と期待", 2026-07-02. https://prtimes.jp/main/html/rd/p/000000013.000155348.html 3. [PRTIMES #1123603] Nice Mobile Co., Ltd., "ビジネスパーソンの会議DX・AI活用実態調査2026", 2026-06-19. https://prtimes.jp/main/html/rd/p/000000070.000048231.html 4. [PRTIMES #1092052] Trusqueta Inc., "企業のAI活用、現場ではどこまで進んでいるのか？株式会社トラスクエタが「業務におけるAI利用実態調査」を実施", 2026-06-18. https://prtimes.jp/main/html/rd/p/000000094.000054524.html 5. [ANK-2026-07-01-004] Rin Takenouchi, "Japan's Workplace AI Enters the \"Big-Three Era\" as Costs Become a \"Management Issue\"", 2026-07-02. https://ainews.washinmura.jp/ainews/en/ank/ANK-2026-07-01-004