Incerto Launches 'Gyomu Kanri AI' to Document Remote/Hybrid Work Operations as AI Summaries
Incerto LLC has launched 'Gyomu Kanri AI', an AI system for companies operating remote/hybrid work systems. This tool automatically summarizes and records PC work activities, allowing management to objectively understand organizational operations through data, thereby overcoming the challenges of self-reporting and direct monitoring while prioritizing psychological safety.
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- 📰 Published: May 2, 2026 at 03:47
- 🔍 Collected: May 1, 2026 at 19:31
- 🤖 AI Analyzed: May 1, 2026 at 19:46 (14 min after Collected)
Incerto LLC (Headquarters: Arakawa-ku, Tokyo; Representative: Aihito Sato; https://www.incerto.tech/) today announced the launch of 'Gyomu Kanri AI,' an AI system for companies operating remote/hybrid work systems. Product details are available on the product page (https://www.incerto.tech/products/gyomu-kanri-ai).
'Gyomu Kanri AI' is an AI tool for organizations where AI automatically summarizes and records employees' PC work activities, allowing management and managers to grasp the actual state of organizational operations as data. The goal is to accumulate data on the actual state of organizational operations, which were not recorded through self-reporting or time clocking alone, as organizational data.
The accumulated data can be used as decision-making material for future organizational management, such as resource allocation, organizational restructuring, work style reform, and personnel planning. At the time of introduction, prior notice and consent design for employees are set as operating conditions, and either cloud or on-premise configurations can be selected according to the company's information handling policy.
■ Background of Provision
With the normalization of remote/hybrid work systems, companies have continuously faced a situation where management and managers lack objective means to understand 'what kind of work is being done in the organization, and to what extent.'
Self-reporting through daily or weekly effort reports places a burden on employees for input, and the granularity of records depends on individual differences, making it difficult for aggregated data to meet the needs of organizational management decision-making. While presence time can be understood from clocking in/out or PC login times, it doesn't show what was accomplished during that time. Resource allocation, organizational restructuring, and personnel planning decisions tend to rely on individual manager observations and reports, a tendency that is particularly strong in remote environments.
Traditional tools such as PC operation logs and activity monitoring exist, but many companies hesitate to introduce them due to concerns about employee privacy and psychological safety. The situation where organizational work understanding methods are biased towards either 'self-reporting' or 'direct monitoring,' failing to establish a sustainable operational data foundation for the organization, has become a prominent management challenge for remote/hybrid work companies.
■ Challenge: Invisibility of Organizational Work Reality
In remote/hybrid work companies, the following situations are commonly observed:
Limitations of self-reported data. Daily/weekly effort reports vary in recording granularity among employees, and the input burden is significant, making it difficult for aggregated data to be directly useful for organizational management decision-making.
Mismatch between 'time spent' and 'actual output.' While PC login times and clocking in/out show presence time, they don't reveal what kind of work was progressing in the organization during that time. It's difficult to distinguish between days filled with meetings and days of concentrated work based solely on attendance data.
Trade-off between aversion to surveillance and work visibility. Traditional monitoring tools often sacrifice employees' psychological safety for visibility, risking damage to trust in the remote system itself.
Absence of an organization-wide operational data foundation. Many companies manage work status individually within departments/teams using separate spreadsheets, task management tools, or verbal reports via chat, failing to achieve a state where organizational work reality can be analyzed across the entire organization.
Lack of decision-making material for organizational management. Management decisions such as resource allocation, organizational restructuring, work style reform, and personnel planning require reliable data on actual work, but currently, organizations lack such data, leading decisions to often depend on manager reports and experience-based rules.
Commonly, the actual work being done in the organization is not accumulated as data, preventing organizational management decisions from being made based on objective materials.
■ Solution: 'Gyomu Kanri AI'
'Gyomu Kanri AI' is an AI tool for organizations that accumulates data on the actual state of work being performed in the organization as AI-summarized data, neither through self-reporting nor direct monitoring.
AI comprehensively understands the actual state of organizational operations from various operational logs such as activities in internal tools like Slack, Discord, GitHub, Gmail, Notion, Google Workspace, and Microsoft 365, as well as PC screens and work communications. It records each employee's work content, tools used, estimated tasks, and working hours as structured data for the organization, allowing management and managers to refer to AI-organized work summaries instead of individual work screens.
As more data is accumulated within the organization, the accuracy of recognizing work patterns increases, enhancing the precision of decision-making material for organizational management.
'Gyomu Kanri AI' is an AI tool for organizations where AI automatically summarizes and records employees' PC work activities, allowing management and managers to grasp the actual state of organizational operations as data. The goal is to accumulate data on the actual state of organizational operations, which were not recorded through self-reporting or time clocking alone, as organizational data.
The accumulated data can be used as decision-making material for future organizational management, such as resource allocation, organizational restructuring, work style reform, and personnel planning. At the time of introduction, prior notice and consent design for employees are set as operating conditions, and either cloud or on-premise configurations can be selected according to the company's information handling policy.
■ Background of Provision
With the normalization of remote/hybrid work systems, companies have continuously faced a situation where management and managers lack objective means to understand 'what kind of work is being done in the organization, and to what extent.'
Self-reporting through daily or weekly effort reports places a burden on employees for input, and the granularity of records depends on individual differences, making it difficult for aggregated data to meet the needs of organizational management decision-making. While presence time can be understood from clocking in/out or PC login times, it doesn't show what was accomplished during that time. Resource allocation, organizational restructuring, and personnel planning decisions tend to rely on individual manager observations and reports, a tendency that is particularly strong in remote environments.
Traditional tools such as PC operation logs and activity monitoring exist, but many companies hesitate to introduce them due to concerns about employee privacy and psychological safety. The situation where organizational work understanding methods are biased towards either 'self-reporting' or 'direct monitoring,' failing to establish a sustainable operational data foundation for the organization, has become a prominent management challenge for remote/hybrid work companies.
■ Challenge: Invisibility of Organizational Work Reality
In remote/hybrid work companies, the following situations are commonly observed:
Limitations of self-reported data. Daily/weekly effort reports vary in recording granularity among employees, and the input burden is significant, making it difficult for aggregated data to be directly useful for organizational management decision-making.
Mismatch between 'time spent' and 'actual output.' While PC login times and clocking in/out show presence time, they don't reveal what kind of work was progressing in the organization during that time. It's difficult to distinguish between days filled with meetings and days of concentrated work based solely on attendance data.
Trade-off between aversion to surveillance and work visibility. Traditional monitoring tools often sacrifice employees' psychological safety for visibility, risking damage to trust in the remote system itself.
Absence of an organization-wide operational data foundation. Many companies manage work status individually within departments/teams using separate spreadsheets, task management tools, or verbal reports via chat, failing to achieve a state where organizational work reality can be analyzed across the entire organization.
Lack of decision-making material for organizational management. Management decisions such as resource allocation, organizational restructuring, work style reform, and personnel planning require reliable data on actual work, but currently, organizations lack such data, leading decisions to often depend on manager reports and experience-based rules.
Commonly, the actual work being done in the organization is not accumulated as data, preventing organizational management decisions from being made based on objective materials.
■ Solution: 'Gyomu Kanri AI'
'Gyomu Kanri AI' is an AI tool for organizations that accumulates data on the actual state of work being performed in the organization as AI-summarized data, neither through self-reporting nor direct monitoring.
AI comprehensively understands the actual state of organizational operations from various operational logs such as activities in internal tools like Slack, Discord, GitHub, Gmail, Notion, Google Workspace, and Microsoft 365, as well as PC screens and work communications. It records each employee's work content, tools used, estimated tasks, and working hours as structured data for the organization, allowing management and managers to refer to AI-organized work summaries instead of individual work screens.
As more data is accumulated within the organization, the accuracy of recognizing work patterns increases, enhancing the precision of decision-making material for organizational management.