Information processing system, information processing method, and information processing program

Abstract

To visualize the AI ​​talent needed for implementing generative AI in each company, as well as the benefits of such implementation. [Solution] A task classification unit that classifies multiple business tasks within a company according to their affinity level with the generating AI, A personnel suggestion unit that indicates the required level and quantity of personnel to provide an AI agent that processes classified business tasks, The results presentation department estimates the results if the quantity and level of personnel presented by the personnel presentation department are assembled, An information processing system equipped with [the following features].

Description

Technical Field

[0001] The present invention relates to an information processing system, an information processing method, and an information processing program.

Background Art

[0002] In the above technical field, Patent Document 1 discloses a technique for measuring the gap between a sales target and current sales and generating an action plan including targets related to unit price, number of customers, purchase frequency, and execution power.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the technique described in the above document cannot propose what kind of AI talent cultivation is necessary for each company.

[0005] An object of the present invention is to provide a technique for solving the above problems.

Means for Solving the Problems

[0006] To achieve the above object, an information processing system according to the present invention includes By using a table that correlates multiple AI utilization levels, which indicate the degree of AI involvement when humans perform business tasks, with the workload reduction rate due to AI implementation at each AI utilization level, a plurality of business tasks in a company For each of these, it is determined which of the aforementioned multiple AI utilization levels it falls under, and the business reduction rate pre-associated with the determined AI utilization level is identified. section, Multiple AI utilization capabilities required of the person in charge of each task in order to perform multiple business tasks at the aforementioned company. level Identify present AI utilization capability level a presenting unit, The AI ​​utilization capability level is calculated by multiplying the current working time of the aforementioned business task by the identified work reduction rate. at the level presented by the presenting unit AI utilization capabilities are acquired. if Achievable reduction in workload time present Business reduction effect a presenting unit, and comprises 、 The aforementioned AI utilization level and the aforementioned AI utilization capability level correspond at least partially. It is an information processing system.

[0007] In order to achieve the above object, an information processing method according to the present invention is an information processing method executed by an information processing system, By using a table that correlates multiple AI utilization levels, which indicate the degree of AI involvement when humans perform business tasks, with the workload reduction rate due to AI implementation at each AI utilization level, A plurality of business tasks in an enterprise For each of these, it is determined which of the aforementioned multiple AI utilization levels it falls under, and the business reduction rate pre-associated with the determined AI utilization level is identified. Steps, <00OO057>Level Identify To present AI utilization capability level Presentation step, The AI ​​utilization capability level is calculated by multiplying the current working time of the aforementioned business task by the identified work reduction rate. Of the level presented by the presentation unit AI utilization capabilities are acquired. When Achievable reduction in workload time To present Business reduction effect Presentation step, Including fruit, The aforementioned AI utilization level and the aforementioned AI utilization capability level correspond at least partially. It is an information processing method.

[0008] In order to achieve the above object, an information processing program according to the present invention is By using a table that correlates multiple AI utilization levels, which indicate the degree of AI involvement when humans perform business tasks, with the workload reduction rate due to AI implementation at each AI utilization level, A plurality of business tasks in an enterprise <OO00074>Steps, The multi-stage AI utilization capabilities required of the person in charge of each task in order to perform multiple business tasks within the aforementioned company. Level Identify To present AI utilization capability level Presentation step, The AI ​​utilization capability level is calculated by multiplying the current working time of the aforementioned business task by the identified work reduction rate. Of the level presented by the presentation unit AI utilization capabilities are acquired. When Achievable reduction in workload time To present Business reduction effect Presentation step, An information processing program that causes a computer to execute And, Information processing program in which the AI ​​utilization level and the AI ​​utilization capability level correspond at least partially. It is.

Advantages of the Invention

[0009] According to the present invention, it is possible to visualize the AI human resources required for the introduction of generative AI in each enterprise and the merits of such introduction.

Brief Description of the Drawings

[0010] [Figure 1]This is a block diagram showing the configuration of the information processing system according to the first embodiment. [Figure 2] This figure shows the objective of the information processing system according to the second embodiment. [Figure 3] This figure shows the background of the information processing system according to the second embodiment. [Figure 4A] This figure shows an example of the effects of the information processing system according to the second embodiment. [Figure 4B] This figure shows an example of the effects of the information processing system according to the second embodiment. [Figure 5] This is a block diagram showing the configuration of the information processing system according to the second embodiment. [Figure 6] This diagram illustrates the task classification of the information processing system according to the second embodiment. [Figure 7] This figure illustrates an example of the classification results of the information processing system according to the second embodiment. [Figure 8] This is a diagram illustrating the personnel classification system of the information processing system according to the second embodiment. [Figure 9A] This figure illustrates an example of the results of the information processing system according to the second embodiment. [Figure 9B] This figure illustrates an example of the results of the information processing system according to the second embodiment. [Figure 10] This is a flowchart illustrating the processing flow of the information processing system according to the second embodiment. [Modes for carrying out the invention]

[0011] Embodiments of the present invention will be described in detail below with reference to the drawings. However, the components described in the following embodiments are merely illustrative and are not intended to limit the technical scope of the present invention to them alone.

[0012] [First Embodiment] An information processing system 100 as a first embodiment of the present invention will be described with reference to Figure 1. As shown in Figure 1, the information processing system 100 includes a task classification unit 101, a personnel presentation unit 102, and a results presentation unit 103.

[0013] The task classification unit 101 classifies multiple business tasks 111 within a company according to their affinity level with AI.

[0014] The personnel suggestion unit 102 suggests the required personnel levels for implementing an AI that processes the classified business task group 121.

[0015] The results presentation unit 103 presents the results that would be achieved if personnel of the level specified by the personnel presentation unit 102 were assembled.

[0016] Based on the above configuration, it is possible to visualize the AI ​​personnel required for the introduction of generative AI in companies, as well as the benefits of doing so.

[0017] [Second Embodiment] In describing the second embodiment of the present invention, Figure 2 shows the objectives of the present invention as a premise. In countries where the introduction and utilization of generative AI are lagging and the impact on labor is underestimated, the objective of the present invention is to promote the introduction and utilization of generative AI by providing more concrete proposals to individual companies.

[0018] In other words, the planning phase involves generating a vision that strategically utilizes generative AI tailored to the characteristics of each company. Specifically, it involves classifying the business operations for each company and proposing which tasks should utilize generative AI. Furthermore, it clarifies the level of generative AI skills that employees of that company should acquire.

[0019] In the execution and implementation phase, the company will effectively utilize generative AI by introducing personnel capable of using it, or by educating such personnel, thereby increasing the economic value of the company.

[0020] Figure 3 is a simplified diagram of Figure 6. a Distribution of Tasks by Worker-Desired and Expert-Assessed Feasible HAS Levels from the Stanford University paper "Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the US Workforce" by Yijia Shao, Humishka Zope, Yucheng Jiang, Jiaxin Pei, David Nguyen, Erik Brynjolfsson, Diyi Yang (https: / / arxiv.org / pdf / 2506.06576), which presents the results of a study investigating the suitability of using generational AI for various business tasks in companies.

[0021] Domain 301 represents tasks where users desire automation, but in reality, AI-driven automation is difficult, making human intervention and the use of AI for task advancement more promising. Approximately 17% of tasks fall into this category.

[0022] Domain 302 represents business tasks that users perceive as primarily human-driven, but which are actually promising for automation by AI. Approximately 29% of business tasks fall into this category.

[0023] Because of this gap between user expectations and AI experts' perspectives, approximately 45% of field-driven generative AI applications experience a mismatch between "what users want to do" and "what can actually be done." In other words, introducing generative AI without expert advice and a plan inevitably leads to losses due to poorly implemented applications and missed opportunities for promising applications.

[0024] Therefore, this embodiment analyzes internal business operations to visualize, in terms of time and cost, "which jobs have the potential to utilize generative AI and to what extent." Then, in order to maximize that potential, it designs a generative AI talent development plan optimized for each organization or job function. In other words, it promotes the use of generative AI from a management perspective and leads to results.

[0025] Specifically, as shown in Figures 4A and 4B, we classify each task within each organization or job type and propose the proactive introduction of generative AI to organizations 401 or job types 402 that have high potential for improving operational efficiency and a large number of people. In other words, we visualize the impact (time reduction, cost reduction) of introducing generative AI, i.e., the potential for improving operational efficiency, according to the magnitude of the role of AI in various tasks. We then diagnose the level of education required to realize this operational efficiency for employees corresponding to the organization or job type.

[0026] Figure 5 is a diagram illustrating the configuration of an information processing system according to a second embodiment of the present invention. In Figures 4A and 4B, the information processing system 500 identifies organizations 401 or job types 402 that are particularly likely to benefit from the introduction of AI, and further derives the effect of reducing workload through the introduction of AI. As shown in Figure 5, the information processing system 500 includes a corporate information acquisition unit 501, a business task definition unit 502, a task classification unit 503, a time reduction effect estimation unit 504, a capability level presentation unit 505, and a workload reduction effect presentation unit 506.

[0027] The Corporate Information Acquisition Unit 501 acquires corporate information such as the organization name and organizational overview, or the job title and job title overview. The business task definition unit 502 uses AI generation to define business tasks expected in daily operations (tens of tasks per organization / job type) based on information such as the organization name and organizational overview or the job title and job title overview.

[0028] The task classification unit 503 classifies each business task according to its GAL (Generative AI Agency Level). GAL is the Generative AI business utilization level defined as shown in Figure 6, and indicates the extent to which the AI ​​agent is active in the business. Specifically, a Level L0 task represents a task that is performed solely by humans, with no AI involvement. In this level of task, there is no role for the AI, and the workload reduction rate is defined as 0%. Level L1 represents a task that is fully led and performed by humans, with the AI ​​acting as a knowledgeable assistant. In this level of task, the AI's role is collaborative, and the workload reduction rate is defined as 15%. Level L2 represents a task that is led and performed by humans, with the AI ​​acting as a highly skilled and knowledgeable assistant. In this level of task, the AI's role is collaborative, and the workload reduction rate is defined as 30%. Level L3 represents a task where the AI ​​acts as a co-creative partner, forming an equal partnership with humans. In this level of task, the AI's role is collaborative, and the workload reduction rate is defined as 45%. Level 4 involves the AI ​​acting as a competent execution agent, leading the task execution. For tasks at this level, the AI's role is automation, and the workload reduction rate is defined as 60%. Level 5 involves the AI ​​acting as a perfect task performer, completely leading the task execution. For tasks at this level, the AI's role is automation, and the workload reduction rate is defined as 75%.

[0029] Figure 7 shows an example of the analysis results for each specific task, illustrating the sub-categories 701 resulting from the task classification by the task classification unit 503. For each sub-category 701, a workload 702 is defined. The workload 702 is provided as parameters for the workload, which are the workload percentage and workload time defined by the person in charge at the company (such as a member belonging to an organization or job type) for each task, based on the daily workload. Furthermore, for each of the subcategories 701, a level 703 based on the GAL definition is assigned as a result of the judgment. Here, categories A.1.1 to A.1.2 are judged as L3, and all other categories are judged as L2. In addition, based on the GAL as a result of the judgment, the reduction rate as the business reduction effect 704 is determined, and the time reduction effect estimation unit 504 calculates the business reduction time for each task by multiplying the reduction rate by the business time. The competency level display unit 505 estimates the required education level for each task using the GCL (Generative AI Competency Level) shown in Figure 8. GCL is the Generative AI Utilization Competency Level and defines the ability (mind and skills) to utilize Generative AI in business operations.

[0030] Specifically, GCL Level L0 is the ability to perform tasks using only conventional business knowledge and skills, without utilizing generative AI. This corresponds to GAL Level L0. GCL Level L1 is the ability to improve the efficiency of existing operations by utilizing AI for information retrieval and as a sounding board. This corresponds to GAL Level L1. GCL Level L2 is the ability to improve the quality of operations by providing clear instructions tailored to the purpose, including background and constraints. This corresponds to GAL Level L2. GCL Level L3 is the ability to dramatically improve the quality of creative and analytical tasks through interaction with AI. This corresponds to GAL Level L3. GCL Level L4 is the ability to create simple tools for process improvement in one's own work and automate tasks. This corresponds to GAL Level L4. GCL Level L5 is the ability to plan and lead the use of AI agents to improve team business processes. This corresponds to GAL Level L5.

[0031] Here, we have assumed that GCL levels L0 to L5 correspond to GAL levels L0 to L5, but the present invention is not limited to this.

[0032] In Figure 7, the task of "investigating industry trends" can be streamlined by generating AI for basic information gathering and summarizing key points, resulting in a rating of L3 (Collaborative Partner). In other words, AI plays a role in enhancing human capabilities. To effectively utilize this AI, L3 individuals are needed who possess the mindset of "Can we come up with even better ideas by interacting with AI?" and who "can dramatically improve the accuracy of output by repeatedly engaging with AI and repeating the cycle of generation and feedback. They can perform creative and analytical tasks that would be difficult to accomplish alone by making full use of advanced AI utilization techniques, such as combining multiple prompts (prompt chaining)."

[0033] The business reduction effect presentation unit 506 simulates the required competency level based on the definition of GCL and the achievable business reduction effect according to the acquisition of each competency level, based on the business tasks and the GAL judgment results, and uses a generating AI to determine the possibility of automation by the AI ​​agent.

[0034] Figures 9A and 9B visualize the educational effects. In this organization, if human resource development is successful up to level 3 in Figure 8, a 90% reduction in GAL time can be achieved. Such educational effects can be calculated by cumulatively calculating the reduction ratio.

[0035] In Figure 9A, 901 is a value aggregated based on the GAL diagnostic results, and 902 is linked to the GCL definition in Figure 8. Figure 9B shows the workload reduction effect 903 and educational economic effect 904 calculated based on these factors. This organization consists of 5 people and has a total working time of 800 hours. If the above educational effect is achieved at 100%, the workload reduction rate will be 28%, which translates to a monthly workload improvement equivalent to 680,000 yen. In other words, when calculated per employee, it is possible to improve work efficiency by up to 140,000 yen per month. Figures 4A and 4B plot the workload reduction rates calculated in this way.

[0036] The objective variable for this AI agent is the visualization of educational effectiveness (estimation of the time and monetary effects obtained through education corresponding to GCL), and the explanatory variable is the Generative AI Business Application Diagnosis (GAL Diagnosis).

[0037] It is important that "visualizing the time-saving effects of business process inventory and AI utilization" and "defining the human resource development capabilities and providing training materials to achieve this" are connected. According to this embodiment, a human resource development plan for achieving results in business is proposed.

[0038] Therefore, we provide specific calculation processes and algorithms that analyze the business tasks (inputs) of client companies based on GAL and convert them into a unique indicator (output) called "Generative AI Implementation Potential." This involves classifying, weighting, and scoring multiple tasks.

[0039] It generates and sets "GCL capability targets" that are automatically associated with the "AI implementation potential" calculated for each company.

[0040] The system automatically simulates and visualizes the business outcomes (e.g., cost reductions, productivity improvements) that would result from implementing an education plan based on the established GCL (Global Community Leadership).

[0041] Figure 10 is a flowchart showing the processing flow in this embodiment. First, in step S1001, organizational or job title information is acquired. Specifically, information on the organization name and organizational overview or the job title name and job title overview is acquired. In step S1002, the AI ​​generates and defines business tasks expected in daily operations (several dozen tasks per organization / job title).

[0042] In step S1003, each business task is classified according to the GAL, and the potential for automation by the AI ​​agent is determined by the generating AI.

[0043] In step S1004, the time-saving effect of the AI ​​agent is estimated for each business task by multiplying the current work time by the time reduction rate.

[0044] Step S1005 involves compiling the total time reduction effect of AI for each organization / job type.

[0045] In step S1006, the system simulates the required competency levels based on the GCL definition and the achievable workload reduction effects (estimated educational effectiveness) for each competency level achieved, based on the business tasks and the GAL assessment results. Then, a display screen showing the relationship between the AI-generated workload reduction rate and the number of people, as shown in Figures 4A and 4B, is generated and made available for viewing by the user, who is a representative of the company. Note that the output screen of this embodiment is not limited to Figures 4A and 4B; for example, the horizontal axis may represent work hours by organization or job type. Alternatively, the vertical axis may represent the workload reduction effect (amount) by organization or job type.

[0046] Furthermore, users may be presented with educational materials corresponding to the GCL (Global Classification of Languages) that they have set as their target.

[0047] Based on the above configuration, it is possible to visualize the AI ​​personnel required for the introduction of generative AI in companies, as well as the benefits of doing so.

[0048] [Other embodiments] Although the present invention has been described above with reference to embodiments, the present invention is not limited to the above embodiments. Various modifications to the structure and details of the present invention can be made, as can be understood by those skilled in the art within the technical scope of the present invention. Furthermore, any system or apparatus that combines the separate features included in each embodiment is also within the technical scope of the present invention.

[0049] Furthermore, the present invention may be applied to a system composed of multiple devices or to a single device. Moreover, the present invention is also applicable when an information processing program that realizes the functions of the embodiment is supplied to a system or device and executed by a built-in processor. The technical scope of the present invention includes programs installed on a computer to realize the functions of the present invention on a computer, or a medium storing such a program, a server that downloads such a program, and a processor that executes such a program. In particular, at least a non-transitory computer-readable medium storing a program that causes a computer to execute the processing steps included in the embodiments described above is included in the technical scope of the present invention.

Claims

1. A business reduction rate identification unit that, by using a table that associates multiple AI utilization levels indicating the degree of AI involvement when humans perform business tasks with the business reduction rate due to AI introduction at each AI utilization level, determines which of the multiple AI utilization levels each of the multiple business tasks in a company belongs to, and identifies a business reduction rate that is pre-associated with the determined AI utilization level. An AI utilization capability level presentation unit identifies and presents multiple AI utilization capability levels required of the person in charge of a task in order to perform multiple business tasks at the aforementioned company, A business reduction effect presentation unit calculates the business reduction time by multiplying the current business time of the aforementioned business task by the identified business reduction rate, and presents the business reduction time that can be achieved if the AI ​​utilization capability level presented by the AI ​​utilization capability level presentation unit is acquired. Equipped with, An information processing system in which the AI ​​utilization level and the AI ​​utilization capability level correspond at least partially.

2. The information processing system according to claim 1, further comprising a business task definition unit that defines multiple business tasks in the company using generation AI.

3. The information processing system according to Claim 1, wherein the workload reduction rate is determined by a generating AI for each organization or job type that performs the workload tasks.

4. The information processing system according to Claim 1, wherein the business reduction effect presentation unit estimates and presents the business reduction effect of introducing AI for each organization or job type.

5. An information processing method performed by an information processing system, A task reduction rate identification step involves using a table that associates multiple AI utilization levels, which indicate the degree of AI involvement when humans perform business tasks, with the business reduction rate due to AI implementation at each AI utilization level, to determine which of the multiple AI utilization levels each of the business tasks in a company falls under, and to identify the business reduction rate pre-associated with the determined AI utilization level. An AI utilization capability level presentation step that identifies and presents multiple AI utilization capability levels required of the person in charge of a task in order to perform multiple business tasks at the aforementioned company, A business reduction effect presentation step that calculates the business reduction time by multiplying the current business time of the business task by the identified business reduction rate, thereby presenting the business reduction time that can be achieved if the AI ​​utilization capability level presented by the AI ​​utilization capability level presentation unit is acquired. Includes, An information processing method in which the AI ​​utilization level and the AI ​​utilization capability level correspond at least partially.

6. A task reduction rate identification step that, by using a table that associates a plurality of AI utilization levels indicating the degree of AI involvement when humans perform business tasks with the business reduction rate due to the introduction of AI at each AI utilization level, determines which of the plurality of AI utilization levels each of the multiple business tasks in a company belongs to, and identifies the business reduction rate that is pre-associated with the determined AI utilization level, An AI utilization capability level presentation step that identifies and presents multiple levels of AI utilization capability required of the person in charge of a task in order to perform multiple business tasks at the aforementioned company, A business reduction effect presentation step that calculates the business reduction time by multiplying the current business time of the business task by the identified business reduction rate, thereby presenting the business reduction time that can be achieved if the AI ​​utilization capability level presented by the AI ​​utilization capability level presentation unit is acquired. An information processing program that causes a computer to execute, An information processing program in which the AI ​​utilization level and the AI ​​utilization capability level correspond at least partially.

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