How to apply AI to enterprise operations?
4/3/24
In the post-GenAI era, a new way of working has emerged–"AI-first", prioritizing AI to complete tasks within its capabilities, allowing employees to focus on more complex tasks.
According to research from MIT, employees using AI can significantly increase efficiency by 40% compared to those who do not use AI. AI not only boosts the speed of task completion but also makes the work outcome more stable and standardized.
So, how should companies transition to "AI-first"?
1. Identify AI Opportunities
When we discuss AI, a common question is: "What are the application scenarios for AI?"
However, for companies, a better approach would be to think about these issues from their own perspective: "What work are we currently doing? What problems are we trying to solve in our business? Where are the bottlenecks?"
Not all tasks are of equal importance, and we can define the scope of AI work by dividing tasks into levels.
Try classifying daily tasks into the following three categories:
1) Tasks that can be objectively judged for correctness, such as answering customer inquiries.
2) Tasks that require subjective judgment scores, such as guiding customers to place orders.
3) Creativity-intensive tasks that require stronger judgment and professional capabilities, such as developing a customer support system.
Once you've sorted existing tasks, you'll likely find that a significant amount of work belongs to the first two types. These tasks are ideal entry-points for AI because it excels at handling these more rule-based and standardized tasks.
With AI helping complete the first and second types of tasks, employees can focus more on the third type. These tasks require in-depth professional knowledge, innovative thinking, and high-level decision-making processes, thereby allowing for efficient utilization of human resources.
2. Find the Right System
Once AI operation-assistance opportunities are identified, companies need to start system construction. In reality, many companies are still using one-time-use tools. These tools often cannot learn from past operations or optimize themselves.
Therefore, discovering systems that truly can self-improve and allows the company to intervene for further improvement is a significant step towards the "AI-first" working modality.
To begin, you should sort out your business processes. Let's use customer service as an example, simplified SOP (Standard Operating Procedure) would look like:
The customer asks a question.
Customer service retrieves relevant knowledge from the database.
Once knowledge is found, respond to the customer.
If the knowledge isn't found, collect more information from the customer and raise a ticket for the technical department.
You can identify which parts of this process are suitable for AI-assisted applications and thus find an entry point for AI to take over tasks. This process also helps the company define specific requirements for the AI system.
However, setting up a system is only the first step. Achieving excellent performance requires integrating company-specific knowledge into the system.
3. Integrate Professional Knowledge
The more specific and comprehensive we are in describing our desired results, the more substantial the results we can achieve with AI. The key is to tell AI exactly what you want it to do and provide the relevant material, enabling the system to continuously refine itself.
Here are a few suggestions:
Provide AI with as much knowledge as possible and supplement information to issues as they are discovered.
Use clearly structured knowledge.
Provide examples to inform AI about the expected output.
List the standards and checklists that excellent answers should meet.
4. Human Participation and Improvement
AI projects often fail due to high expectations and inadequate practical planning. Once the initial preparations are complete, we may feel like we've got everything under control–a dangerous mindset given the high expectations. Many times, like most projects, unexpected challenges will be encountered during the progress of an AI project and initial disappointments and difficulties may be commonplace.
Many prematurely declare that AI can't manage the work. In reality, we should adjust expectations to a reasonable level and realize that human intervention is necessary.
In the initial stage of AI system operation, companies should arrange for someone to monitor AI output, provide immediate feedback, and make adjustments; as the system matures, human intervention gradually reduces, but human review is still required for sensitive tasks. Throughout this process, the knowledge base continually expands, and correction cases get documented. A significant improvement in results can typically be seen after a few iterations. This is how to make maximum use of AI.
The progress of the autonomous vehicle industry is a good example. They have established five different degrees of automation, clearly demonstrating their specific expectations and considerations about AI.
No matter the application scene, the company should pinpoint its position in this process. There’s no need to feel discouraged if you can't start directly from the fifth level.
Start the shift to "AI-first"
Given the continuous innovation in technology, the focus of future work will increasingly turn to "how to effectively hand over tasks to AI." Companies, when encountering potential AI application opportunities, should consider how to maximize the potential of AI.
There's no rush to see immediate, wholesale shifts to AI transformation overnight. However, the sooner you start, the sooner you reach your goal. We can start with small-scale pilot projects and gradually expand the scope of AI applications in business as we accumulate experience and confidence. Simultaneously, cultivate team members' understanding and use of AI, making them active participants in driving the process of AI adoption.
In summary, transitioning to "AI-first" is a systematic project that requires patience and careful planning and implementation. By identifying AI opportunities, selecting appropriate systems, integrating professional knowledge, and involving human participation for improvements, we can steadily move towards a more efficient and intelligent work ecology, making AI a robust instrument for improving productivity, stimulating innovative vitality, and consolidating competitive advantages.
Information source：
AI Exchange CEO Rachel Woods - The AI & Operations Playbook: How to take AI and apply it to the operations of the business
In the post-GenAI era, a new way of working has emerged–"AI-first", prioritizing AI to complete tasks within its capabilities, allowing employees to focus on more complex tasks.
According to research from MIT, employees using AI can significantly increase efficiency by 40% compared to those who do not use AI. AI not only boosts the speed of task completion but also makes the work outcome more stable and standardized.
So, how should companies transition to "AI-first"?
1. Identify AI Opportunities
When we discuss AI, a common question is: "What are the application scenarios for AI?"
However, for companies, a better approach would be to think about these issues from their own perspective: "What work are we currently doing? What problems are we trying to solve in our business? Where are the bottlenecks?"
Not all tasks are of equal importance, and we can define the scope of AI work by dividing tasks into levels.
Try classifying daily tasks into the following three categories:
1) Tasks that can be objectively judged for correctness, such as answering customer inquiries.
2) Tasks that require subjective judgment scores, such as guiding customers to place orders.
3) Creativity-intensive tasks that require stronger judgment and professional capabilities, such as developing a customer support system.
Once you've sorted existing tasks, you'll likely find that a significant amount of work belongs to the first two types. These tasks are ideal entry-points for AI because it excels at handling these more rule-based and standardized tasks.
With AI helping complete the first and second types of tasks, employees can focus more on the third type. These tasks require in-depth professional knowledge, innovative thinking, and high-level decision-making processes, thereby allowing for efficient utilization of human resources.
2. Find the Right System
Once AI operation-assistance opportunities are identified, companies need to start system construction. In reality, many companies are still using one-time-use tools. These tools often cannot learn from past operations or optimize themselves.
Therefore, discovering systems that truly can self-improve and allows the company to intervene for further improvement is a significant step towards the "AI-first" working modality.
To begin, you should sort out your business processes. Let's use customer service as an example, simplified SOP (Standard Operating Procedure) would look like:
The customer asks a question.
Customer service retrieves relevant knowledge from the database.
Once knowledge is found, respond to the customer.
If the knowledge isn't found, collect more information from the customer and raise a ticket for the technical department.
You can identify which parts of this process are suitable for AI-assisted applications and thus find an entry point for AI to take over tasks. This process also helps the company define specific requirements for the AI system.
However, setting up a system is only the first step. Achieving excellent performance requires integrating company-specific knowledge into the system.
3. Integrate Professional Knowledge
The more specific and comprehensive we are in describing our desired results, the more substantial the results we can achieve with AI. The key is to tell AI exactly what you want it to do and provide the relevant material, enabling the system to continuously refine itself.
Here are a few suggestions:
Provide AI with as much knowledge as possible and supplement information to issues as they are discovered.
Use clearly structured knowledge.
Provide examples to inform AI about the expected output.
List the standards and checklists that excellent answers should meet.
4. Human Participation and Improvement
AI projects often fail due to high expectations and inadequate practical planning. Once the initial preparations are complete, we may feel like we've got everything under control–a dangerous mindset given the high expectations. Many times, like most projects, unexpected challenges will be encountered during the progress of an AI project and initial disappointments and difficulties may be commonplace.
Many prematurely declare that AI can't manage the work. In reality, we should adjust expectations to a reasonable level and realize that human intervention is necessary.
In the initial stage of AI system operation, companies should arrange for someone to monitor AI output, provide immediate feedback, and make adjustments; as the system matures, human intervention gradually reduces, but human review is still required for sensitive tasks. Throughout this process, the knowledge base continually expands, and correction cases get documented. A significant improvement in results can typically be seen after a few iterations. This is how to make maximum use of AI.
The progress of the autonomous vehicle industry is a good example. They have established five different degrees of automation, clearly demonstrating their specific expectations and considerations about AI.
No matter the application scene, the company should pinpoint its position in this process. There’s no need to feel discouraged if you can't start directly from the fifth level.
Start the shift to "AI-first"
Given the continuous innovation in technology, the focus of future work will increasingly turn to "how to effectively hand over tasks to AI." Companies, when encountering potential AI application opportunities, should consider how to maximize the potential of AI.
There's no rush to see immediate, wholesale shifts to AI transformation overnight. However, the sooner you start, the sooner you reach your goal. We can start with small-scale pilot projects and gradually expand the scope of AI applications in business as we accumulate experience and confidence. Simultaneously, cultivate team members' understanding and use of AI, making them active participants in driving the process of AI adoption.
In summary, transitioning to "AI-first" is a systematic project that requires patience and careful planning and implementation. By identifying AI opportunities, selecting appropriate systems, integrating professional knowledge, and involving human participation for improvements, we can steadily move towards a more efficient and intelligent work ecology, making AI a robust instrument for improving productivity, stimulating innovative vitality, and consolidating competitive advantages.
Information source：
AI Exchange CEO Rachel Woods - The AI & Operations Playbook: How to take AI and apply it to the operations of the business
In the post-GenAI era, a new way of working has emerged–"AI-first", prioritizing AI to complete tasks within its capabilities, allowing employees to focus on more complex tasks.
According to research from MIT, employees using AI can significantly increase efficiency by 40% compared to those who do not use AI. AI not only boosts the speed of task completion but also makes the work outcome more stable and standardized.
So, how should companies transition to "AI-first"?
1. Identify AI Opportunities
When we discuss AI, a common question is: "What are the application scenarios for AI?"
However, for companies, a better approach would be to think about these issues from their own perspective: "What work are we currently doing? What problems are we trying to solve in our business? Where are the bottlenecks?"
Not all tasks are of equal importance, and we can define the scope of AI work by dividing tasks into levels.
Try classifying daily tasks into the following three categories:
1) Tasks that can be objectively judged for correctness, such as answering customer inquiries.
2) Tasks that require subjective judgment scores, such as guiding customers to place orders.
3) Creativity-intensive tasks that require stronger judgment and professional capabilities, such as developing a customer support system.
Once you've sorted existing tasks, you'll likely find that a significant amount of work belongs to the first two types. These tasks are ideal entry-points for AI because it excels at handling these more rule-based and standardized tasks.
With AI helping complete the first and second types of tasks, employees can focus more on the third type. These tasks require in-depth professional knowledge, innovative thinking, and high-level decision-making processes, thereby allowing for efficient utilization of human resources.
2. Find the Right System
Once AI operation-assistance opportunities are identified, companies need to start system construction. In reality, many companies are still using one-time-use tools. These tools often cannot learn from past operations or optimize themselves.
Therefore, discovering systems that truly can self-improve and allows the company to intervene for further improvement is a significant step towards the "AI-first" working modality.
To begin, you should sort out your business processes. Let's use customer service as an example, simplified SOP (Standard Operating Procedure) would look like:
The customer asks a question.
Customer service retrieves relevant knowledge from the database.
Once knowledge is found, respond to the customer.
If the knowledge isn't found, collect more information from the customer and raise a ticket for the technical department.
You can identify which parts of this process are suitable for AI-assisted applications and thus find an entry point for AI to take over tasks. This process also helps the company define specific requirements for the AI system.
However, setting up a system is only the first step. Achieving excellent performance requires integrating company-specific knowledge into the system.
3. Integrate Professional Knowledge
The more specific and comprehensive we are in describing our desired results, the more substantial the results we can achieve with AI. The key is to tell AI exactly what you want it to do and provide the relevant material, enabling the system to continuously refine itself.
Here are a few suggestions:
Provide AI with as much knowledge as possible and supplement information to issues as they are discovered.
Use clearly structured knowledge.
Provide examples to inform AI about the expected output.
List the standards and checklists that excellent answers should meet.
4. Human Participation and Improvement
AI projects often fail due to high expectations and inadequate practical planning. Once the initial preparations are complete, we may feel like we've got everything under control–a dangerous mindset given the high expectations. Many times, like most projects, unexpected challenges will be encountered during the progress of an AI project and initial disappointments and difficulties may be commonplace.
Many prematurely declare that AI can't manage the work. In reality, we should adjust expectations to a reasonable level and realize that human intervention is necessary.
In the initial stage of AI system operation, companies should arrange for someone to monitor AI output, provide immediate feedback, and make adjustments; as the system matures, human intervention gradually reduces, but human review is still required for sensitive tasks. Throughout this process, the knowledge base continually expands, and correction cases get documented. A significant improvement in results can typically be seen after a few iterations. This is how to make maximum use of AI.
The progress of the autonomous vehicle industry is a good example. They have established five different degrees of automation, clearly demonstrating their specific expectations and considerations about AI.
No matter the application scene, the company should pinpoint its position in this process. There’s no need to feel discouraged if you can't start directly from the fifth level.
Start the shift to "AI-first"
Given the continuous innovation in technology, the focus of future work will increasingly turn to "how to effectively hand over tasks to AI." Companies, when encountering potential AI application opportunities, should consider how to maximize the potential of AI.
There's no rush to see immediate, wholesale shifts to AI transformation overnight. However, the sooner you start, the sooner you reach your goal. We can start with small-scale pilot projects and gradually expand the scope of AI applications in business as we accumulate experience and confidence. Simultaneously, cultivate team members' understanding and use of AI, making them active participants in driving the process of AI adoption.
In summary, transitioning to "AI-first" is a systematic project that requires patience and careful planning and implementation. By identifying AI opportunities, selecting appropriate systems, integrating professional knowledge, and involving human participation for improvements, we can steadily move towards a more efficient and intelligent work ecology, making AI a robust instrument for improving productivity, stimulating innovative vitality, and consolidating competitive advantages.
Information source：
AI Exchange CEO Rachel Woods - The AI & Operations Playbook: How to take AI and apply it to the operations of the business
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