The AI gain equation
Or replacement formula
Context of use
The artificial intelligence is profoundly transforming societal and professional paradigms, but how does this manifest itself in concrete terms? What realities are hidden behind the announcements, beyond fears and fantasies?
The AI Gain Equation is an analytical formula designed to quantify the tangible benefits of artificial intelligence in all the dimensions it touches. It is a valuable tool to encourage responsible adoption, raise awareness, and confront the brutal reality of what it reveals.
It helps businesses, ordinary citizens and decision-makers navigate the complex AI ecosystem more effectively, maximizing its benefits while considering potential risks.
Why this equation?
To answer the question “Why use AI?” first of all, because this question has not yet found sufficiently tangible answers, especially outside the digital domain.
Many do not yet grasp the importance of AI or do not feel concerned. Some, focused on observing the transition efforts rather than on the benefit of cumulative effects, put off their transition until tomorrow, failing to consider the benefit/effort ratio in its proper measure. Others, on the other hand, already rely heavily on AI, observing its benefits empirically, without however fully evaluating them.
The gain equation aims to factually illuminate the extent of AI's impacts.
It reveals economic advantages (direct and indirect) of such magnitude, thanks in particular to a cumulative effect, that it clearly expresses a reality that is still denied: a programmed and irrevocable destruction of employment, through almost generalised replacement.
To the economic benefit of employers, to the detriment of employees. Which raises both the need for rapid adoption in the name of competitiveness and the importance of responsible integration, because everyone will have to arbitrate between their own economic survival and the social consequences of the announced replacement.
Without denying the obligation to adopt, without denying the inevitable job losses, particularly in a context that favors cost reduction, it is by facing the reality revealed by the gain equation that societal upheavals could be better anticipated, including support measures and the redesign of work models in order to mitigate social impacts.
A decision-making tool for managers
By quantifying the impact of AI, the gain equation allows decision-makers to objectively assess the benefits of integrating AI into their processes.
They are thus able to make informed decisions about necessary investments, potential reorganizations and employee training and support strategies.
An asset for AI professionals
The gain equation is also a valuable asset for professionals looking to position themselves on the AI market. Faced with denial, objections and reluctance from potential customers, this tool makes it possible to present concrete and quantified arguments demonstrating the extent of the benefits of AI.
By leveraging the gain equation, AI professionals can highlight the savings, productivity gains, and competitive advantages their solutions can deliver. This helps them better convince decision-makers and overcome barriers related to lack of awareness or mistrust of the technology.
A tool to help raise awareness among the general public
For the average citizen, understanding the gain equation means realizing that AI is profoundly transforming many professions and sectors of activity. It also means understanding that if they do not yet feel concerned, it will not be long. The gain equation allows us to realize that AI will have a lasting and brutal impact on everyone's daily life, whether as a consumer, an employee or a user of public services.
This is for a simple reason: the savings it allows are far too significant to be ignored, especially in a situation which is pushing for economy.
This awareness is essential, because it allows us to directly understand the societal issues of AI: evolution of skills and professions, transformation of the job market, ethical questions, etc. By better understanding the realities that AI implies, everyone is better equipped to form an informed opinion and make decisions for their future.
The gain equation therefore has a real educational utility through the brutal and inescapable reality that it reveals.
A tool for awareness and accountability
For public authorities and social partners, the AI gain equation is a wake-up call that forces them to face the reality of the evolution of work and the need to fundamentally reform training and social protection systems. It places them face to face with their responsibility to engage in a genuine societal debate on the political issues related to AI and to implement courageous public policies to support this transition while preserving social cohesion.
The indicators
KPI
The advantages of the time factor do not seem to require further arguments… and yet. By systematically segmenting the integration of AI at the level of tasks and micro-tasks, the cumulative effects at the end of the day prove to be prodigious. Almost imperceptible during execution, these effects become real exponential levers thanks to their accumulation. (CF: ESP Method)
The variables:
GTT
- Time Saving per Task
GTT: (x)minutes / task of (x) min / Employee
GTTj: (x)minutes / day / Employee
GTTm: (x)minutes / month / Employee
GFT
- Financial Gain per Task (Employer Contributions Included)
GFT: (x)€ / task of (x) min / Employee
GFTj: (x)€ / day / Employee
GFTm: (x)€ / month / Employee
AI is criticized for generating excess electricity consumption. However, on the scale of each task that it accelerates for each employee, each individual, it can on the contrary impact electricity consumption downwards. On the other hand, this acceleration directly impacts the cognitive load of employees. Drastically reduced and resulting in caloric savings, the latter constitutes a performance lever hitherto ignored, but how much impact on productivity and performance.
The variables:
EKH
- Economy Kwh
EKH: Kwh / task of (x) min / Employee
EKHj: Kwh / day / Employee
EKHm: Kwh / month / Employee
EWH
- Euros saved on Kwh
EWH: (x)€ / task of (x) min / Employee
EWHj: (x)€ / day / Employee
EWHm: (x)€ / month / Employee
ECDT
- Caloric savings over task duration
ECDT: (x)cal / task of (x) min / Employee
ECDTj: (x)cal / day / Employee
ECDTm: (x)cal / month / Employee
In this era of disruption, all savings in CO2 and precious resources such as water must be accounted for. Here again, AI is considered problematic, while it does indeed allow savings on a micro scale, especially through the accumulation effect.
The variables:
EcoCO2
- CO2 savings by reducing KWh
EcoCO2: Kg / Kwh / task of (x) min / Employee
EcoCO2j: Kg / Kwh / day / Employee
EcoCO2m: Kg / Kwh / month / Employee
EcoWater
- Liters of water saved by lowering KWh
EcoWater: Liters of water / task of (x) min / Employee
EcoEauj: Liters of water / day / Employee
EcoEaum: Liters of water / month / Employee
The caloric-saving effects of lowering cognitive load, cumulative over the course of a day, result in an increased ability to concentrate for longer, with greater acuity.
Everyone is aware that the efficiency of a worker differs between Monday morning and Friday at the end of the day. No one is unaware of the implications that this has.
What happens when one's maximum ability to concentrate lasts longer for each task completed, every day, every day?
This has significant consequences on quality, error rates, satisfaction and many other factors that the concentration variables consider.
The variables:
CDCMax
- Share of caloric expenditure on ECDT at maximum concentration
x% of calories available for prolonged maximum concentration
ECDT * Coef_Max_Concentre
DCMS
- Maximum Additional Concentration Duration (min)Duration equivalent to CDCMax
CDCMax / ref_cal_per_minute
Considering the multiple benefits that AI brings, whether in terms of speed, purely economic gains, energy and concentration factors, it is appropriate to establish global indicators that integrate all these levers, and which can be directly correlated to turnover.
The variables:
BPGBQ
- Quantified Gross Overall Performance Profit
(x)% performance / Employee
G_CMAX
- Gain Concentration on Caloric Expenditure in Max Concentration
(x)% Max concentration capacity
Usage rate
- Weighting of usage
(x)% tasks actually performed by AI
Quality is a direct consequence of operational performance gains and cognitive load reduction factors.
Including it as a weighting factor is essential to understand and measure incremental effects at both a micro and macro scale within any organization.
The variables:
Qa_perf
- Quality factor
(x)% in quality rendered
BPPQ
- Quality Weighted Performance Benefit
(x)% in overall performance
The results of the gain equation are generated by multiple weighting steps reflecting the real impacts and their extent. These results allow to extract interpretations and scenarios, ranging from a single task to the repercussions of its optimized deployment on the entire working time of each employee.
This approach allows to measure the gains directly related to an optimization and to evaluate their cumulative potential. By analyzing the results of the gain equation, it becomes possible to quantify the benefits of a process improvement, both at the level of an individual task and at the scale of the company as a whole.
The variables:
- Result Equation Gain
(x)€ / Employee
GFTm: (x)€ / employee / month
BPGBQ Weighting: (x)% performance
G_CMAX Weighting: (x)% Max Concentration Capacity
QA_PERF Weighting: (x)% in quality rendered
BPPQ Weighting: (x)% in overall performance weighted by quality
Usage rate: (x)% of tasks actually completed by AI
In-depth exploration of variables and indicators
Time
GTT: time saving per task
GTT, or Time Saving per Task, refers to the time saved thanks to the speed of execution brought by the integration of artificial intelligence in a particular process. This gain is determined by comparing the time normally required to complete a task without the help of AI with that required when AI is used.
Objective and interest of the GTT
- Evaluate the effectiveness and influence of AI on employee productivity.
- Provide an accurate perspective on how much time can be saved.
- Quantify the time freed up so that employees can focus on higher value-added tasks.
Calculs
The GTT is formulated as follows:
- Usual Task Time (TPTH) : Time needed to complete the task without AI.
- Time per Task with AI (TPTIA*) : Time needed to complete the task with AI.
- Time Saving per Task (GTT) : [GTT = TPTH – TPTIA] This calculation allows you to determine the gain in minutes per task.
GTTj : time saved per day
GTTj, or Daily Time Savings, is the sum of time savings made each day. It allows companies to see how integrating AI can optimize their daily operations and support the decision to invest in this technology. Calculating GTTj is crucial because it provides a clear perspective on the time savings made daily.
Calculs
The GTTj is calculated as follows:
- Task Frequency (freqTache) : Number of times a task is performed per day.
- Time Saving per Task (GTT) : Calculated as described previously.
Formula : [ GTTj = GTT x freqTache ]
This allows you to determine the total gain in minutes per day.
GTTm : time saving per month
GTTm, or Time Savings per Month, provides a global view of monthly time savings, allowing companies to analyze the cumulative effects of AI on their processes over an extended period.
Calculs
The GTTm is calculated as follows:
- Time Savings per Day (GTTd) : Calculated as described previously.
- Number of Working Days per Month : Generally, 20 working days are considered.
Formula : [ GTTm = GTTj x 20 ]
This allows you to determine the total gain in minutes per month.
TPTIA*: time per task with ia
TPTIA, or Time per Task with Artificial Intelligence, indicates the time needed to complete a task by integrating artificial intelligence.
Calculs
TPTIA is calculated by reducing the usual task time (TPTH) according to a percentage of improvement provided by the AI (COEFTPTIA**).
Thus, the TPTIA is given by the following formula:
- Algorithm :
- TPTIA = TPTH – (TPTH * COEFTPTIA)
variables:
- *TPTH: Usual Task Duration
- This is the typical time it takes to complete a task without the help of artificial intelligence. It represents the benchmark against which improvements are measured.
- **COEFTPTIA: TPTIA coefficient
- This coefficient is derived from the percentage of improvement provided by the AI. It is calculated by converting the percentage of improvement into a multiplier factor (for example, if the improvement is 20%, the COEFTPTIA will be 0,20).
Calculation example
If a task took 60 minutes (TPTH) and we have a coefficient of improvement of 60% (COEFTPTIA = 0,60), the TPTIA would be calculated as follows:
- TPTIA = 60 minutes – (60 minutes * 0,60)
- TPTIA = 60 minutes – 24 minutes
- TPTIA = 36 minutes
So, with AI, the task would now take 36 minutes instead of 60 minutes, demonstrating a significant time saving through optimization.
The Financial Gain per Task (GFT) accurately assesses the savings made for each task completed, taking into account employer charges.
It illustrates the economic benefit that comes from integrating artificial intelligence (AI) into a company's operations.
By tangibly highlighting the economic benefits of improving task efficiency, the GFT justifies the investments needed to transition to AI.
It provides a critical metric for businesses looking to assess the ROI of AI integration.
Calculs
The calculation of GFT is based on the following elements:
- GTT : Time saved per task in minutes.
- TH : The Hourly Rate of the cost of labor, expressed in euros per hour.
The formula is: GFT = (GTT * (TH / 60)) + (GTT * (TH / 60) * patronal_charges / 100)
Explanation of variables:
- GTT : Represents the time saved by automating the task.
- TH : Hourly rate which represents the cost of labor per hour.
- employer_charges : Additional charges to be taken into account to assess the real cost of the employee.
GFTj: financial gain per task per day
Financial Gain per Task per Day (GFTj), represents the financial savings achieved on a daily basis for each task optimized by AI, thus facilitating decision-making regarding resource allocation.
Calculs
The calculation of GFTj is carried out as follows: GFTj = GFT * frequency of tasks per day
GFTm: financial gain per task per month
The Financial Gain per Task per Month (GFTm) represents the financial savings achieved on a monthly basis for each optimized task, thus providing a clear vision of long-term savings.
Calculs
The calculation of GFTm is carried out as follows: GFTm = GFTj * (Number of working days per month)
The related energy
EKH (Economy kWh) represents the amount of energy saved by integrating artificial intelligence (AI) into the tasks performed.
The EKH assesses the energy savings achieved, allowing companies to assess the energy impact of their move to AI.
This measure is valuable because it highlights the reduction in energy costs and its beneficial effects on the environment.
By reducing their energy consumption, businesses can not only save money, but also play an active role in combating climate change.
Calculs
EKH is calculated by subtracting the energy consumption with AI from the energy consumption without AI.
Formula :
EKH = Consumption without AI – Consumption with AI
Explanation of variables:
- Consumption without AI: This is the amount of energy (in kWh) used to perform a task without AI assistance. This is calculated from the power* of the equipment used (in Watts) multiplied by the duration of use of the equipment (in hours) and converted into kWh.
- Consumption with AI: This is the amount of energy (in kWh) used to perform the same task, but with the support of AI. This calculation also takes into account the power of the hardware used (in Watts) and the duration of the hardware request (in hours).
Detailed calculation:
- Consumption without AI (in kWh) = (Hardware power in Watts / 1000) × Duration of demand without AI (in hours)
- Consumption with AI (in kWh) = (Hardware power in Watts / 1000) × Duration of demand with AI (in hours)
Savings per period:
- EKHj (Saving kWh per day): Calculated by multiplying the EKH by the frequency of tasks performed per day.
- EKHm (kWh saving per month): Calculated by multiplying the EKHj by the average number of working days in the month
*Hardware power refers to the electrical consumption of a computer, expressed in watts. Each task performed by an employee requires the use of a computer, which results in energy consumption measured in kWh.
EWH: financial savings on kWh
The EWH quantifies the financial savings achieved based on the EKH.
This allows businesses to accurately assess the impact of AI on their energy costs, providing a concrete assessment of efficiency in terms of sustainability and profitability.
Calculs
- EWH (Financial Economy on Kwh) : EWH is obtained by multiplying EKH by the price of kWh. This allows converting energy savings into concrete financial savings, providing a clear indicator of the financial benefits associated with the use of AI.
- EWH = EKH x Price of kWh
Variables involved
- EKH : Kwh Economy, representing the difference in energy consumption between running a task without AI and with AI. This variable is crucial to understand the impact of AI on energy consumption.
- Price per kWh : Unit cost of electrical energy, which can vary depending on contracts or suppliers. This price is essential for calculating the financial savings, because it determines the monetary value of the energy savings achieved.
In summary, EWH integrates the importance of energy efficiency into operating cost management.
Ecology
EcoCO2 is a variable that quantifies the reduction in CO2 emissions achieved by using AI for a given task. This reduction is calculated based on the energy savings achieved by reducing the time needed to perform the task with the help of AI.
The calculation of EcoCO2 is done by multiplying two values:
- Kwh_ECO : which represents the energy savings in kWh achieved thanks to the reduction in the time spent using the computer hardware for the task, made possible by AI.
- kgCO2PerKWh : which is a constant representing the quantity of CO2 emitted per kWh of electricity consumed. In France, this value is estimated at around 0,06 kg of CO2 per kWh (source: ADEME).
Thus, EcoCO2 highlights one of the environmental benefits of integrating AI into work processes: by reducing the time needed to complete a task, AI reduces the energy consumption of IT equipment, and therefore the CO2 emissions associated with this consumption.
The EcoCO2j, EcoCO2m and EcoCO2m_incluant_tout_employe variables then make it possible to extend this calculation to different time scales (day, month) and organization (individual, entire company), in order to give a global vision of the positive impact of AI in terms of reducing CO2 emissions.
EcoEau: water savings on KwH reduction
EcoWater is a variable that represents the amount of water saved in liters for a given task, thanks to the reduction in the time needed to complete this task with the help of AI. This water saving is calculated based on the energy savings achieved.
The calculation of EcoEau is done by multiplying two values:
- Kwh_ECO : which represents the energy savings in kWh achieved thanks to the reduction in the time spent using the computer hardware for the task, made possible by AI.
- litersWaterPerKWh : which is a constant representing the quantity of water required to produce one kWh of electricity. In France, it is estimated that the production of one kWh of electricity requires approximately 2,5 liters of water (source: ADEME, 2016).
Thus, EcoEau makes it possible to quantify another environmental benefit of the integration of AI: by reducing energy consumption, AI indirectly makes it possible to save the water needed to produce this energy.
For example, if using AI saves 1 kWh of electricity for a given task (Kwh_ECO), this means that 2,5 liters of water have been saved for this task (EcoWater), because this is the amount of water that would have been needed to produce this kWh of electricity.
As with EcoCO2, the variables EcoEauj, EcoEaum and EcoEaum_incluant_tout_employe allow this calculation to be extended to different time scales (day, month) and organization (individual, entire company), in order to give a global vision of the positive impact of AI in terms of water savings.
Consequential concentration
ECDT, or Caloric Economy Over Task Duration, is a metric that quantifies the reduction in caloric expenditure while performing a task through the use of artificial intelligence (AI).
This indicator is crucial for measuring the impact of AI on the cognitive availability of employees when performing specific tasks.
The ECDT determines how many calories are saved by using AI compared to performing the task unassisted, which can have significant implications for employee health and operational efficiency.
By reducing the caloric load associated with performing tasks, employees can improve their concentration and overall well-being.
Positive consequences on maximum concentration duration
Using AI to complete tasks not only saves calories, but also increases employees' maximum concentration span.
By decreasing caloric consumption per task, individuals are able to maintain a higher level of attention over longer periods (DCMS – below).
This translates into tangible benefits on the overall performance of the team.
Impact on overall performance
Better focus, driven by AI-enabled calorie savings, can lead to:
- Increased productivity : Employees can accomplish more tasks in less time, improving overall efficiency.
- Improved quality of work : With increased concentration, the accuracy and quality of tasks performed increases, reducing the error rate and necessary rework.
- Employee satisfaction : Less energy-intensive workload and better performance can lead to increased employee satisfaction, fostering a positive work environment.
Calculs
ECDT is calculated by subtracting the caloric expenditure related to the duration of the task with AI from the caloric expenditure without AI.
- ECDT = cal_for_the_duration_of_task_without_ia – cal_for_task_duration_with_ia
Where:
- cal_for_the_duration_of_task_without_ia represents the calories expended to complete the task without AI assistance.
- cal_for_task_duration_with_ia represents the calories spent to perform the same task with AI assistance.
This calculation results in a value that indicates how many calories are saved by using AI for a given task, while promoting maximum focus and improved overall performance.
The variable DCMS (Additional Maximum Concentration Time) is designed to measure the length of time an individual can maintain a high level of concentration.
In other words, it represents the time during which an employee can concentrate at their maximum on tasks, without being distracted or tired. This metric is essential for assessing the impact of AI on cognitive performance.
DCMS Interest
Le DCMS plays a crucial role in several areas:
- Optimization of cognitive performance : By determining how long an employee can maintain peak concentration, companies can better plan for tasks that require sustained attention. This is especially important for tasks that require creativity or complex thinking.
- Prevention of mental fatigue : By knowing the maximum concentration span, managers can organize strategic breaks to avoid cognitive overload, which improves employee well-being and prevents burnout.
- AI effectiveness in processes : The DCMS allows us to assess the extent to which the introduction of AI and automation tools can free up time and improve the intensity of concentration, by reducing distractions linked to repetitive tasks.
Calculs
Le DCMS is calculated as follows:
DCMS = CDCMax / ref_cal_per_minute
Details of the variables involved:
- CDCMax (Calories Available to Expend at Maximum Concentration) : This represents the total amount of calories that can be allocated from the Calorie Savings over Task Duration (ECDT). In other words, it is the maximum duration of additional concentration possible over a work period, made possible by the calories saved.
- ref_cal_per_minute (Calories Burned per Minute Reference) : This figure reflects the average energy an employee expends per minute when engaged in a task. It allows you to convert the calories saved into additional concentration time.
In summary, DCMS is a key indicator that assesses the additional time an employee maintains a high level of concentration, thanks to AI.
It allows businesses to see how AI can not only increase productivity, but also improve the quality of work by helping employees stay focused longer.
The induced performance
The BPGBQ (Gross Overall Performance Benefit Quantified) evaluates the improvement in a company's performance by taking into account all the dimensions affected by the implementation of AI.
Le BPGBQ is crucial for several reasons:
- Justification of investments : By demonstrating the multidimensional benefits of AI, the BPGBQ helps justify the efforts and expenses associated with the transition to AI.
- Identification of areas for improvement : By analyzing the components of the BPGBQ, companies can identify areas that require adjustments or additional investments to maximize profits.
- Performance monitoring : The BPGBQ allows to follow the evolution of performance over time, thus facilitating comparisons between different periods or improvement strategies implemented.
- Building stakeholder confidence : By providing tangible evidence of improvements, the BPGBQ can build confidence among investors, customers and employees in an AI transition strategy.
Calculs
Le BPGBQ is calculated using the following formula:
BPGBQ = (Cost of internal defects before – Cost of internal defects after) + (Cost of warranties before – Cost of warranties after) + Increase in sales + Productivity gains – Cost of investment in quality
Explanation of the variables involved:
- Cost of internal defects:
- before: This metric represents the percentage of financial losses due to internal defects in the company's processes before AI improvements were implemented. This includes costs related to errors, product returns, or other inefficiencies.
- after: This metric represents the adjusted percentage of financial losses resulting from internal defects, after AI implementation. A significant reduction in this cost is a direct indicator of the effectiveness of the improvements.
- Cost of guarantees:
- before: This percentage represents the expenses incurred by the company due to warranty claims before implementing AI improvement initiatives.
- after: This parameter represents the warranty-related expenses after process optimization. A decrease in this cost indicates an improvement in the quality of products or services.
- Sales increase : This parameter represents the percentage increase in sales linked to the improvement in customer satisfaction, resulting from the optimization of processes and the improvement in quality induced.
- Productivity gains: This percentage represents the improvement in productivity resulting from the changes implemented, such as reducing working hours or increasing production.
- Investment cost in quality: This percentage represents costs incurred to improve the quality of products or services, including training, purchasing new equipment or implementing new technologies.
BPGBQ variable reference rates:
The default reference rates are average values that can be adjusted as needed.
Objective of G_CMAX
Le G_CMAX represents the increase in maximum concentration through the use of artificial intelligence (AI).
It measures how well AI improves employees' ability to stay focused intensely on a task for an extended period of time.
By extending this period of peak concentration, employees can perform their tasks more efficiently, which improves overall quality and performance.
Calculs
The calculation of G_CMAX is done using the following variables:
- CMAXinit : This is the time during which the concentration intensity is at its maximum without the use of AI.
- CMAX_IA : This is the time during which the concentration intensity is at its maximum with the use of AI.
- G_CMAX : This result is expressed as a percentage, indicating the increase in the ability to concentrate at the highest possible intensity thanks to the use of AI.
The concentration gain is calculated with the following formula:
G_CMAX = ((CMAXinit - CMAX_IA) / CMAXinit) * 100In summary, G_CMAX is a key intensity indicator that shows how AI integration can improve employees' concentration intensity, contributing to better productivity.
Reference values:
The default standard states that, regardless of the type of task, maximum concentration is maintained for 60% of the time needed to complete it.
Thus, any acceleration of processing time combined with cognitive performance induced by DCMS, increases energy availability for high-intensity caloric expenditure. This has the effect of improving the ability to maintain a maximum level of concentration for the current task or for other tasks.
The resulting quality
The indicator QA_PERF represents the adjusted performance after taking into account various improvement factors such as concentration gains and caloric savings. It is calculated by applying a total increase factor to the baseline performance, expressed as a percentage.
Le QA_PERF provides an accurate measure of overall performance improvement, weighted by the effects of induced quality. Allowing companies to assess the impact of AI even more accurately.
Calculs
To calculate QA_PERF, we follow these steps:
- Convert the quantified gross overall performance benefit (BPGBQ) in decimal.
- Calculate the weighted concentration gain in decimal.
- Calculate weighted calorie savings.
- Add these gains together to get the total increase factor.
- Apply this factor to BPGBQ_decimal to get QA_PERF_decimal.
- Convert QA_PERF_decimal in percentage to obtain QA_PERF.
Each variable used in this calculation has a key role:
- BPGBQ_decimal : Represents the base performance in decimal.
- gain_concentration_decimal : Gain in concentration after weighting.
- weighted_calorie_savings : Caloric savings after weighting.
- total_increase : Sum of weighted gains, representing the total increase in performance.
In summary, QA_PERF is a crucial tool to assess and capture the quality improvement resulting from AI integration and its impacts on overall performance.
BPPQ, or Benefit Weighted Performance by Quality, is a measure that assesses the overall improvement in a company's performance by taking into account gains in productivity, quality and concentration.
The BPPQ provides an overview of the benefits obtained after the implementation of artificial intelligence. This allows to quantify the positive impact on overall performance.
Calculs
The BPPQ is calculated by applying the increases due to the concentration gain and the QA_PERF quality to the basic performance (BPGBQ).
- BPGBQ : Represents basic performance.
- G_CMAX : Max concentration gain in percentage, calculated by comparing concentration durations with and without AI.
- QA_PERF : Performance after adjustment, taking into account caloric savings and weighted concentration gains.
Formulas
- Intermediate calculation:
bppqIntermediate = BPGBQ * (1 + G_CMAX / 100) - Final BPPQ:
BPPQ = bppqIntermediate * (1 + QA_PERF / 100)
Each element of the calculation plays a role in achieving an accurate assessment of performance improvement, incorporating not only financial benefits, but also focus and quality.
The Incremental Exponential
The ESP, or Exponential Segmentation Process, is a task segmentation method that allows work management to be approached from the perspective of artificial intelligence (AI). By breaking down tasks into micro-tasks, this approach facilitates more detailed and optimized management. Thanks to theESP, each micro-task can be introduced into a process SROC (Optimized Context Distribution System), thus maximizing the use of resources and improving the quality of results. While theESP focuses on breaking down tasks, the SROC ensures the efficient execution of these micro-tasks using AI.
>Learn more about the ESP method (Exponential Segmentation Process)
Impact on REG (Result of the Gain equation)
The ultra-segmentation of tasks into micro-tasks has a direct and powerful impact on each variable in the gain equation, thus leading to exponential effects by accumulation.
Influence on time savings (GTT)
By breaking down tasks into micro-tasks, each component of processing time can be optimized. This means that the Time Per Task with AI (TPTIA) can be reduced significantly for each microtask. The accumulation of these reductions results in a much larger total time savings (GTT), because every second saved at each step adds up.
Financial Gain Improvement (GFT)
Fine segmentation of tasks increases the Financial Gain per Task by reducing errors and associated costs, while optimizing processing time and resource utilization.
Reduction of energy costs (EKH)
Segmentation allows to precisely target energy-consuming and time-consuming steps. By optimizing the use of resources at each micro-task, the total energy consumption decreases. This directly improves the Kwh economy (EKH) and the associated financial economy (EWH), since the energy consumed is reduced at each step.
Strengthening concentration (G_CMAX)
Because each microtask is shorter and more focused, employees' maximum concentration can be maintained more easily. This increases the Concentration Gain (G_CMAX), because switching from one task to another is made easier, reducing cognitive fatigue and increasing efficiency.
Cumulative and exponential effect
The cumulative effect of optimizing each variable manifests itself exponentially. When each micro-task is optimized, the gains are not simply added together, but multiplied, as the gains in time, money, energy, and concentration amplify each other.
In short, the ultra-segmentation of tasks into micro-tasks maximizes the effects of the gain equation by optimizing each variable, leading to exponential results by accumulation.
The SROC for Optimized Context Distribution System is a content management system and systematic, transversal and multidimensional integration of generative AI in management applications and integrated software packages. The SROC is based on the categorized and hierarchical management of data, content and contexts dedicated to AI, thus ensuring optimal distribution to specialized anthropomorphic AIs, through an approach considering the intrinsic limits of generative AI.
>Learn more about SROC: Optimized Context Distribution System
Impact on REG (Result of the Gain equation)
The ability to easily direct microtask processing to specialized AIs offers significant advantages, amplifying the effects on the variables in the gain equation.
Improved relevance
Each specialized AI is designed to excel in a particular area, ensuring that each micro-task is handled with increased accuracy and relevance. This improves the quality and accuracy of results, reducing errors and increasing overall efficiency. Error Reduction Rate (TRDH) benefits directly from this specialization, reducing the costs associated with corrections.
Performance increase
By assigning specialized AIs to specific microtasks, the performance of each task is optimized by achieving immediate relevance. These AIs can execute processes faster and with better adaptation to the specific requirements of each microtask. This results in increased time savings (GTT) and financial gains (GFT) as tasks are completed more efficiently.
Optimization of financial gains
Improving accuracy and speed through specialized AI reduces operational costs. By increasing the efficiency of each micro-task, the financial gain per task (GFT) is maximized. The savings achieved through the optimization of human and material resources translate into an increase in the REG, because every euro invested in automation and segmentation generates a higher return.
Reduction of energy costs
Optimizing micro-tasks by specialized AIs results in significant time savings. This time saving reduces the duration of equipment use, which in turn reduces energy consumption. Indeed, less time spent on a task means less time during which machines consume energy. This reduction in the duration of equipment use allows a saving of Kwh (EKH) and improves the associated financial economy (EWH). By optimizing energy consumption, we also contribute to the sustainability of operations by reducing the overall carbon footprint.
Cumulative and multiplier effect
Using specialized AI for each micro-task creates a multiplier effect on gains. The benefits of each optimized micro-task combine to produce exponential improvements in efficiency and cost savings across the organization. REG reflects this positive impact, because each variable in the gain equation is magnified by specialization.
In summary, segmented distribution of microtasks to AIs optimized for their specific functions maximizes relevance, performance, and savings, thereby multiplying the effects on the variables in the gain equation.
All variables
- nameTask : Name of the task.
- TH : Hourly rate of labor cost (in euros per hour).
- rateUsage : AI usage rate (in percentage).
- freqTaches : Frequency of the task per day (in number of tasks per day).
- TRDH : Error reduction rate (in percentage).
- TPTH : Usual duration of the task (in minutes).
- POA : Percentage of improvement via AI (in manual mode).
- COEFTPTIA : Time coefficient per task with AI (multiplier factor).
- TPTIA : Time per task with AI (in minutes).
- GTT : Time saving per task (in minutes).
- GTTj : Time saved per day (in minutes).
- GTTm : Time saved per month (in minutes).
- nbrEmployee : Number of employees.
- EcoWater : This variable calculates the amount of water saved per task by multiplying the number of kilowatt hours saved (Kwh_ECO) by the amount of water saved per kilowatt hour (litersWaterPerKWh).
- EcoEauj : This variable represents the amount of water saved per day. It is calculated by multiplying the amount of water saved per task (EcoWater) by the frequency at which the task is performed (freqTache).
- EcoEaum : This variable calculates the amount of water saved per month. It is obtained by multiplying the amount of water saved per day (EcoEauj) by 20, which is the number of working days in a month.
- EcoEaum_including_all_employees : This variable represents the total amount of water saved per month across all employees. It is calculated by multiplying the amount of water saved per month by an employee (EcoEaum) by the total number of employees (nbrEmployee).
- EcoCO2 : This variable calculates the amount of CO2 saved per task. It is obtained by multiplying the number of kilowatt hours saved (Kwh_ECO) by the amount of CO2 saved per kilowatt hour (kgCO2PerKWh).
- EcoCO2j : This variable represents the amount of CO2 saved per day. It is calculated by multiplying the amount of CO2 saved per task (EcoCO2) by the frequency at which the task is performed (freqTache).
- EcoCO2m : This variable calculates the amount of CO2 saved per month. It is obtained by multiplying the amount of CO2 saved per day (EcoCO2d) by 20, which is the number of working days in a month.
- EcoCO2m_including_all_employees : This variable represents the total amount of CO2 saved per month across all employees. It is calculated by multiplying the amount of CO2 saved per month by an employee (EcoCO2m) by the total number of employees (nbrEmployee).
- THE : Hourly rate of an employee (in euros per hour).
- VHH : Hourly volume of an employee per week (in hours).
- employer_charges : Employer contributions (as a percentage).
- GFT : Financial gain per task (in euros).
- GFTj : Financial gain per day (in euros).
- GFTm : Financial gain per month (in euros).
- price_per_kWh : Price per kWh (in euros).
- PVC : Energy consumption in watts.
- PCKW : Energy consumption in kilowatts.
- DUH : Duration of use without AI (in hours).
- CEKWH : Energy consumption without AI (in kWh).
- CUE : Cost of use without AI (in euros).
- DUHIA : Duration of use with AI (in hours).
- CEKWHIA : Energy consumption with AI (in kWh).
- CUEIA : Cost of use with AI (in euros).
- Kwh_ECO : Saving kWh.
- Kwh_ECO_Day : Saving kWh per day.
- Kwh_ECO_MONTH : Saving kWh per month.
- metabolic_equivalent_of_task : Metabolic equivalent of the task.
- weight_human : Human weight (in kg).
- DC : Calorie expenditure for the task.
- DC_DUREE : Calorie expenditure for the duration of the task without AI (in minutes).
- DC_DUREE_IA : Calorie expenditure for the duration of the task with AI (in minutes).
- TCJ : Conversion rate from calories to joules.
- ref_cal_jour : Caloric reference per day (in calories).
- ref_cal_per_hour : Caloric reference per hour (in calories).
- ref_cal_per_minute : Caloric reference per minute (in calories).
- ref_joule_day : Reference joules per day (in joules).
- ref_joule_hour : Reference joules per hour (in joules).
- ref_joule_per_minute : Reference joules per minute (in joules).
- CDT : Calories for the duration of the task without AI (in calories).
- CDTIA : Calories for duration of task with AI (in calories).
- EH_HUMAN : Human energy for the duration of the task without AI (in joules).
- EH_WITH_IA : Human energy for the duration of the task with AI (in joules).
- ECDT : Calorie savings per task (in calories).
- ECDT_day : Daily calorie savings (in calories).
- ECDT_month : Calorie savings per month (in calories).
- EJDT : Joules saved per task (in joules).
- EJDT_day : Joules saved per day (in joules).
- EJDT_month : Joules saved per month (in joules).
- Coef_Max_Concentrate : Weighted coefficient of energy gain.
- CMAXinit : Maximum concentration over the initial duration (in minutes).
- CMAX_IA : Maximum concentration over time with AI (in minutes).
- G_CMAX : Percentage gain in concentration per task (in percentage).
- CMAXinit_PER_DAY : Maximum concentration over the initial duration per day (in minutes).
- CMAX_IA_PAR_JOUR : Maximum concentration over time with AI per day (in minutes).
- G_CMAX_PAR_JOUR : Percentage gain in concentration per task per day (in percent).
- CMAXinit_PER_MONTH : Maximum concentration over the initial duration per month (in minutes).
- CMAX_IA_PAR_MOIS : Maximum concentration over time with AI per month (in minutes).
- G_CMAX_PER_MONTH : Percentage gain in concentration per task per month (in percentage).
- actual_duration_of_concentration_available_min : Actual concentration time available in minutes (in minutes).
- CDCMax : Calories available attributable to a maximum concentration.
- DCMS : Additional time of maximum concentration in minutes (in minutes).
- DCMSj : Additional time available per day in minutes (in minutes).
- DCMSm : Additional time available per month in minutes (in minutes).
- cost_internal_defaults_before : Cost of internal defects before improvement (in percentage).
- cost_internal_defaults_after : Cost of internal defects after improvement (in percentage).
- cost_guarantees_before : Cost of guarantees before improvement (in percentage).
- cost_guarantees_after : Cost of guarantees after improvement (in percentage).
- increase_sales : Increase in sales/customer satisfaction (in percentage).
- productivity_gains : Productivity gains before concentration improvement (in percentage).
- increase_concentration : Increase in productivity due to improved concentration (in percentage).
- cost_investment_quality : Investment cost in quality (in percentage).
- BPGBQ : Quantified Gross Overall Performance Profit (in percentage).
- k1 : Weighting coefficient for concentration gain.
- k2 : Weighting coefficient for caloric savings.
- gain_concentration_weighted : Weighted concentration gain.
- weighted_calorie_savings : Weighted calorie savings.
- BPGBQ_decimal : BPGBQ in decimal.
- gain_concentration_decimal : Weighted concentration gain in decimal.
- total_increase : Total increase factor.
- QA_PERF_decimal : QA_PERF in decimal.
- QA_PERF : QA performance after percentage adjustment.
- bppqIntermediate : Intermediate value for calculating the BPPQ.
- BPPQ : Final percentage.
- advantageFinancial_gbl : Overall financial advantage.
- gain_final_ponderation : Weighted final gain.
- nbrEmployee : Number of employees.
- VHHm : Human hourly volume per month (in hours).
- gross_cost_employee : Gross cost per employee each month (in euros).
- cost_employee_inc_charge : Net cost per employee each month (in euros).
- replacement_coefficient : Replacement coefficient (ratio of gains to costs).
- gainTime_hour_per_month : Time saving in hours per month.
- gainTime_hour_per_month_all_employees : Time saving in hours per month for all employees.
- tache_euro_full_day_taxed : Financial gain per task for a day of work with charges (in euros).
- tache_euro_full_month_taxed : Financial gain per task for one month of work with charges (in euros).
- tache_euro_full_year_taxed : Financial gain per task for a year of work with charges (in euros).
- spot_euro_full_day_taxed_all_pax : Financial gain per task for a working day for all employees with charges (in euros).
- spot_euro_full_month_taxed_all_pax : Financial gain per task for one month of work for all employees with charges (in euros).
- stain_euro_full_year_taxed_all_pax : Financial gain per task for a year of work for all employees with charges (in euros).
- replacement_coefficient_scenario : Replacement coefficient for the scenario (ratio of gains over costs).
- Task Name (TaskName) : This is the unique identifier of the task to be evaluated, allowing this task to be specifically tracked and analyzed within the framework of the study.
- Hourly rate of labor cost (TH) : This parameter represents the hourly cost associated with labor, expressed in euros per hour. It is crucial for evaluating the financial savings generated by the automation of tasks.
- AI usage rate (UsageRate) : This percentage indicates how often artificial intelligence will be used to complete the task. It is essential to determine the overall impact of AI on the work process.
- Task frequency per day (freqTache) : This figure reflects how many times the task is performed daily, providing a basis for calculating time and cost savings over a given period.
- Error Reduction Rate (TRDH) : This percentage represents the anticipated improvement in task accuracy through the use of AI, highlighting the positive impact it can have on the quality of work.
- Usual task duration (TPTH) : This metric indicates the average time it takes to complete the task without AI assistance, allowing time savings to be quantified once AI is implemented.
Financial variables are essential to assess the economic impact of integrating artificial intelligence into work processes. They allow quantifying the savings and financial gains associated with the automation of tasks. Here are the main financial variables to consider:
- Financial gain per task (GFT) : This amount, expressed in euros, represents the savings made on each task thanks to automation. It is calculated based on the time saved and the hourly rate of the work.
- Financial gain per day (GFTj) : This metric represents the total financial gains made per day, by multiplying the financial gain per task by the task frequency. This allows you to visualize the daily impact of AI on costs.
- Financial gain per month (GFTm) : This amount is calculated by multiplying the financial gain per day by the average number of working days in a month (usually 20 days). This gives an overview of the savings made over a monthly period.
- Cost of Use without AI (CUE) : This cost represents the expenses associated with performing the task without AI assistance, including labor and resource costs.
- Cost of Use with AI (CUEIA) : This amount calculates the expenses related to performing the task with AI, thus allowing to compare the financial efficiency between the two methods.
- Financial economy by task (EWH) : This variable represents the savings made in euros on each task thanks to the reduction in usage costs, taking into account the difference between the cost of use without AI and with AI.
- Financial Economy Daily (EWHj) : This amount is calculated by multiplying the financial savings per task by the task frequency, providing an estimate of daily savings.
- Financial savings per month (EWHm) : This parameter gives the total savings made over a period of one month, by multiplying the financial savings per day by the number of working days.
Artificial intelligence (AI) usage variables are crucial to understanding how and how often AI is integrated into work processes. They help assess the effectiveness and impact of AI on specific tasks. Here are the main AI usage variables to consider:
- AI Usage Rate (UsageRate) : This percentage indicates how often AI will be used to perform a given task. A high usage rate suggests increased reliance on AI, while a low rate could indicate sporadic or limited use.
- Percentage of improvement via AI (POA) : This parameter represents the estimated improvement in the performance or efficiency of a task thanks to the use of AI. It is expressed as a percentage and allows the gains made to be quantified.
- Time per task with AI (TPTIA) : This variable measures the time it takes to complete a task when it is performed with the assistance of AI. It is calculated by taking into account the improvements made by AI compared to the usual duration of the task without AI (TPTH).
- Time saving per task (GTT) : This amount, expressed in minutes, represents the reduction in time needed to complete a task thanks to the use of AI. It is calculated by subtracting the duration with AI (TPTIA) from the usual duration without AI (TPTH).
- Task frequency per day (freqTache) : This metric indicates how many times the task is performed each day, which, combined with the AI usage rate, helps determine the overall impact of AI on the volume of work performed.
- Energy saving (Kwh_ECO) : This variable measures the reduction in energy consumption resulting from the use of AI, taking into account time savings and duration of use. It is important for assessing the environmental impact of AI.
- Reduced calorie expenditure (ECDT) : This variable quantifies the reduction in caloric expenditure related to the performance of a task thanks to AI. It is calculated by comparing the caloric expenditure without and with the use of AI.
Ecological variables are important because they allow quantifying the environmental impact of reducing energy consumption linked to AI, illustrating benefits such as water savings and reduced CO2 emissions, which is essential to promote sustainable and responsible practices.
- EcoWater : This variable calculates the amount of water saved per task by multiplying the number of kilowatt hours saved (Kwh_ECO) by the amount of water saved per kilowatt hour (litersWaterPerKWh).
- EcoEauj : This variable represents the amount of water saved per day. It is calculated by multiplying the amount of water saved per task (EcoWater) by the frequency at which the task is performed (freqTache).
- EcoEaum : This variable calculates the amount of water saved per month. It is obtained by multiplying the amount of water saved per day (EcoEauj) by 20, which is the number of working days in a month.
- EcoEaum_including_all_employees : This variable represents the total amount of water saved per month across all employees. It is calculated by multiplying the amount of water saved per month by an employee (EcoEaum) by the total number of employees (nbrEmployee).
- EcoCO2 : This variable calculates the amount of CO2 saved per task. It is obtained by multiplying the number of kilowatt hours saved (Kwh_ECO) by the amount of CO2 saved per kilowatt hour (kgCO2PerKWh).
- EcoCO2j : This variable represents the amount of CO2 saved per day. It is calculated by multiplying the amount of CO2 saved per task (EcoCO2) by the frequency at which the task is performed (freqTache).
- EcoCO2m : This variable calculates the amount of CO2 saved per month. It is obtained by multiplying the amount of CO2 saved per day (EcoCO2d) by 20, which is the number of working days in a month.
- EcoCO2m_including_all_employees : This variable represents the total amount of CO2 saved per month across all employees. It is calculated by multiplying the amount of CO2 saved per month by an employee (EcoCO2m) by the total number of employees (nbrEmployee).
Performance variables are essential to assess the effectiveness and impact of integrating artificial intelligence (AI) into work processes. They allow you to measure the results obtained and analyze the improvement in performance thanks to AI. Here are the main performance variables to consider:
- Gross time saving per task (GTT) : This variable indicates the total time saved on each task by using AI, measured in minutes. It is calculated by subtracting the time per task with AI (TPTIA) from the usual time without AI (TPTH).
- Time saved per day (GTTj) : This metric represents the total time savings achieved per day, by multiplying the time savings per task by the task frequency. This gives an overview of daily time savings.
- Gross Financial Gain per Task (GFT) : This amount, expressed in euros, represents the financial savings made on each task thanks to AI. It is calculated based on the gross time saving and the hourly rate of the work.
- Financial gain per day (GFTj) : This metric measures the total financial gain made per day, by multiplying the financial gain per task by the task frequency. This helps assess the daily financial impact of AI.
- Error Reduction Rate (TRDH) : This percentage represents the improvement in the accuracy of tasks thanks to the use of AI. It is essential to measure the impact on the quality of the work performed.
- Energy saving (Kwh_ECO) : This variable measures the reduction in energy consumption through the use of AI. It is calculated by comparing energy consumption without AI to that with AI, thus allowing the environmental impact to be assessed.
- Reduced calorie expenditure (ECDT) : This variable quantifies the reduction in caloric expenditure related to the execution of a task thanks to AI. It makes it possible to measure the impact of AI on the well-being of employees by reducing physical fatigue.
- Quantified Gross Overall Performance Profit (BPGBQ) : This percentage assesses the overall impact of AI on business performance, taking into account savings, productivity gains, and quality investment costs.
Energy and caloric variables are essential to understand the impact of artificial intelligence (AI) on energy consumption and caloric expenditure when performing tasks. They allow us to evaluate not only the financial savings, but also the environmental and health benefits associated with the use of AI. Here are the main variables to consider:
- Energy consumption without AI (CEKWH) : This variable measures the energy consumption in kilowatt-hours (kWh) to complete a task without AI assistance. It is calculated by multiplying the power consumed by the duration of the task.
- Energy Consumption with AI (CEKWHIA) : This parameter evaluates the energy consumption in kWh when performing a task with AI. A comparison between this value and the one without AI allows to identify the energy savings achieved thanks to automation.
- Energy saving per task (Kwh_ECO) : This variable represents the reduction in energy consumption per task due to the use of AI. It is calculated by subtracting the energy consumption with AI from that without AI, thus providing a clear overview of the savings achieved.
- Energy saving per day (Kwh_ECO_Day) : This amount measures the energy savings achieved per day, by multiplying the energy savings per task by the frequency of the task. This allows us to assess the daily impact of AI on energy consumption.
- Energy savings per month (Kwh_ECO_MOIS) : This variable quantifies the energy saving over a period of one month, calculated by multiplying the energy saving per day by the average number of working days.
- Caloric expenditure without AI (DC_DUREE) : This variable indicates the total caloric expenditure associated with performing a task without AI assistance, calculated based on metabolism and task duration.
- Calorie expenditure with AI (DC_DUREE_IA) : This metric measures the calorie expenditure when performing a task with AI, allowing to compare the physical efforts required in the two scenarios.
- Caloric Economy by Task (CEPT) : This variable quantifies the reduction in caloric expenditure resulting from the use of AI, calculated by subtracting the caloric expenditure with AI from that without AI.
- Caloric savings per day (ECDT_day) : This amount represents the calorie savings achieved per day, calculated by multiplying the calorie savings per task by the frequency of the task.
- Caloric savings per month (ECDT_month) : This variable quantifies the caloric savings over a period of one month, by multiplying the caloric savings per day by the average number of working days.
Focus and time variables are crucial to assessing the impact of artificial intelligence (AI) on employees’ ability to maintain attention and manage their time effectively. They help analyze how AI can help improve productivity and reduce mental fatigue. Here are the main variables to consider:
- Maximum concentration time without AI (CMAXinit) : This variable represents the maximum time an employee can stay focused on a task without AI assistance. It is calculated by multiplying the duration of the usual task (TPTH) by an energy gain coefficient.
- Maximum concentration time with AI (CMAX_IA) : This parameter measures the maximum concentration time when AI is used to accomplish a task. It is calculated by taking into account the duration of the task with AI (TPTIA) and the energy gain coefficient.
- Gain in concentration per task (G_CMAX) : This variable indicates the percentage of improvement in the ability to concentrate on a task thanks to AI assistance. It is calculated by comparing the durations of maximum concentration with and without AI.
- Cumulative concentration time per day without AI (CMAXinit_PAR_JOUR) : This amount represents the total time of maximum concentration per day without AI, calculated by multiplying the maximum concentration time per task by the task frequency (freqTask).
- Cumulative concentration time per day with AI (CMAX_AI_PAR_JOUR) : This metric measures the total time of maximum concentration per day when AI is used, calculated by multiplying the maximum concentration time per task with AI by the task frequency.
- Concentration gain per day (G_CMAX_PAR_JOUR) : This variable indicates the percentage improvement in concentration per day due to the use of AI, calculated by comparing daily concentration durations with and without AI.
- Cumulative duration of concentration per month without AI (CMAXinit_PAR_MOIS) : This amount represents the total time of maximum concentration per month without AI, calculated by multiplying the cumulative duration of concentration per day by the average number of working days.
- Cumulative duration of concentration per month with AI (CMAX_IA_PAR_MOIS) : This metric measures the total time of maximum concentration per month when AI is used, calculated in the same way as the cumulative time without AI.
- Concentration gain per month (G_CMAX_PAR_MOIS) : This variable indicates the percentage of improvement in concentration per month thanks to AI assistance, calculated by comparing monthly concentration durations with and without AI.
- Additional Maximum Concentration Time (DCMS) : This variable represents the additional time that employees can spend on high-value tasks thanks to the use of AI, calculated from available calories and caloric expenditure references.
Quality driver variables are essential to assess the impact of artificial intelligence (AI) on the quality of processes and outcomes within an organization. They help measure improvements in performance, customer satisfaction, and error reduction. Here are the main quality driver variables to consider:
- Cost of internal defects before improvement (cost_of_internal_defects_before) : This percentage represents the costs associated with internal defects in the process before AI implementation. It is essential to establish a baseline for evaluating improvements.
- Cost of internal defects after improvement (cost_of_internal_defects_after) : This variable indicates the costs of internal defects after the integration of AI, allowing to measure the impact of AI on the reduction of these costs.
- Cost of guarantees before improvement (cost_of_guarantees_before) : This percentage represents the warranty costs before AI implementation. It is used to assess the savings achieved through process improvements.
- Cost of guarantees after improvement (cost_of_guarantees_after) : This variable indicates warranty costs after AI integration, providing a measure of improvements in quality and customer satisfaction.
- Increase in sales/customer satisfaction (increase_sales) : This percentage represents the increase in sales or improvement in customer satisfaction resulting from the implementation of AI. It is crucial to assess the business impact and customer perception of quality.
- Productivity gains (productivity_gains) : This percentage measures the improvement in productivity through the use of AI, taking into account the increase in employee efficiency and focus.
- Investment cost in quality (cost_investment_quality) : This percentage represents the costs associated with the investment needed to improve the quality of processes, such as training and the acquisition of new technologies.
- Gross Product Profit Quality (BPGBQ) : This percentage assesses the overall impact of AI on the quality of products and services, taking into account savings, productivity gains and quality investment costs.
- Total increase factor (total_augmentation) : This variable represents the sum of the weighted impacts of concentration gains and caloric savings on overall performance, providing an overview of qualitative improvements.
- QA Performance after Adjustment (QA_PERF) : This percentage measures the overall performance after integrating AI and qualitative adjustments, indicating the effect of AI on the quality of the results.
Scenario and replacement variables are essential to assess the impact of artificial intelligence (AI) on organizational structure and work processes. They allow analyzing different AI implementation scenarios and assessing the potential for replacing certain functions while measuring savings and productivity gains. Here are the main variables to consider:
- AI implementation scenarios (scenario) : This variable describes the different possible scenarios for the integration of AI into work processes, including approaches such as full automation, decision support or improvement of existing processes.
- Gross cost per employee each month (gross_cost_employee) : This amount represents the total cost associated with each employee, including gross salary and employer contributions. It is essential to evaluate the return on investment of AI integration.
- Net cost per employee each month (cost_employee_inc_charge) : This variable indicates the net cost of an employee per month, taking into account employer contributions. It allows to estimate the savings made by the company when certain tasks are automated.
- Replacement coefficient (coeficient_de_remplacement) : This ratio measures the economic efficiency of AI by comparing the savings achieved through task automation to the cost of an employee. A coefficient greater than 1 indicates that the savings exceed the cost of labor.
- Financial gain per task for a month of work with charges (tache_euro_full_month_taxed) : This amount evaluates the savings made per task over a month, taking into account the charges. This makes it possible to evaluate the financial impact of AI on operational costs.
- Total financial gain for all employees per month (tache_euro_full_month_taxed_all_pax) : This variable measures the total savings achieved by all employees through AI integration over a one-month period, providing an organization-wide view of savings.
- Full optimization scenario (full_optimization_scenario) : This metric describes the company's overall strategy for optimizing its business using AI, including elements such as reducing costs, increasing productivity, and improving quality.
- Impact on payroll (advantageFinancierFinal_tous_employe) : This variable assesses the savings made on the payroll thanks to the automation of tasks, taking into account the total number of employees.
- Profitability analysis (profitability_analysis) : This variable makes it possible to measure the profitability of the investment in AI by comparing the implementation costs to all the savings made and productivity gains.
- Performance scenarios (performance_scenarios) : This metric assesses the impact of AI on organizational performance in different scenarios, allowing to test the flexibility and responsiveness of the company to changes.