The role of artificial Intelligence in modern entrepreneurship
CONTEXTUAL ANALYSIS BUSINESS INTELLECT
ENTREPRENEURIAL TASK
Jaroslav Kaplan
President of the Kaplan Research Company
Founder of the Business IQ project
ENTREPRENEURIAL TASK
Jaroslav Kaplan
President of the Kaplan Research Company
Founder of the Business IQ project
PDF scan of the article: https://drive.google.com/file/d/1ZPGZXUOgFtv7ce8RskB8B_sroQVUwlbL/view?usp=sharing
Abstract
This article explores the potential use of artificial Intelligence in business activities. The article also discusses the distortion of product perception caused by the context of the consumer's own experience and the limitation of AI’s use in business processes due to the lack of understanding of the context of their interaction with consumers.
Keywords:
Artificial Intelligence, meaning space, influence of contextual factors, context of interaction with consumers, data analysis.
This article explores the potential use of artificial Intelligence in business activities. The article also discusses the distortion of product perception caused by the context of the consumer's own experience and the limitation of AI’s use in business processes due to the lack of understanding of the context of their interaction with consumers.
Keywords:
Artificial Intelligence, meaning space, influence of contextual factors, context of interaction with consumers, data analysis.
In today's world, the influence of context on the field of entrepreneurship is immeasurably high. Everyone endows what they hear with their own meaning derived from personal experience, and this can strongly influence what the speaker actually wants to convey. When we say, "taken out of context," we are recognizing this distortion.
To be able to "interpret the data correctly", you must first place the question or task in a context, or in other words, frame it in a certain way. Ask a Scotsman what dinner is, and he will say it is a light snack before going to bed, at nine to ten o'clock. Ask an American the same question and he will say it is a delicious and nutritious multi-course meal at five to six in the evening. The word is the same, but the meaning is different.
Formulating a problem or question is always about "placing" it in context, in this or that "space of meaning." The question, "Have you still not solved your problem?" immediately places the interlocutor in a certain (albeit unpleasant) context. If the entrepreneur understands the environment in which he interacts with consumers, he can to some extent predict his future [1]. For example, from time to time intentionally planning the choice of this or that context of interaction - say, to plan a romantic meeting with your wife / husband (fiancé / bride) in a quiet cozy restaurant with dim lighting. With all originality, you are unlikely to invite your beloved on a romantic date in the factory shop, construction site or cemetery.
Choosing the right context for communication helps to give words and actions the desired meaning
In many cases, the context of communication can help determine much of the content of your message. For example, knowing that a police patrol pulled over a driver who overtook you five minutes ago at breakneck speed can easily help you anticipate the content of much of their conversation.
Why do peace talks fail on the battlefield?
The context of the battlefield does not imply peaceful negotiation, it implies the destruction of the enemy. In Rome, the mortality rate of battlefield participants during military operations was over 90% . This is why civilian mediators (rather than military generals) emerged, who organized meetings between warring parties far away from the front in order to reconcile them.
The upper limit of using AI
Understanding the environment and the impact of contextual factors on the field of activity is the upper limit of application of AI tools in entrepreneurship. In each specific case, this "ceiling" will be precisely the completeness of understanding the context of the activity. The same bottle of water will have a different value in a supermarket, an airplane, and a desert, so it would be prudent to know where that bottle of water will be sold before we design it.
If we don't have an answer to this question, or we don't know the context of future consumer interaction, then we risk selling a product designed for the desert on supermarket shelves.
Beyond this, one could postulate that there would have to be an ideal context for each product. Conversely, every context presupposes the existence of a product perfectly suited to that context. This means that it is not always necessary to change the product, one possible solution could be to change the context of interaction with consumers. For example, an "unsuccessful" bottle of water in a supermarket, let's start selling it in the desert and voila - success.
Prospects for AI development
Harvard Business School estimates that AI will add 13 trillion dollars to the global economy over the next decade [2]. However, the results of a survey of executives show that only 8% of firms are engaged in widespread adoption of AI [3], with most firms applying AI to only one business process. Why such slow progress? I conducted a separate study on this topic and came to the following conclusions:
Firstly, AI capabilities are only effectively utilized when a company has a good understanding of its environment, and consequently, the context of its interactions with consumers.
No activity happens by itself, but within a specific space in which such activity "unfolds". If, within this space there are conditions in which such an action can be potentially performed, then there is a probability that this or that action will be performed.
If the conditions existing in this space do not allow such an action to occur, then the zero result of such an action is obvious. In order for an activity in any area to become possible, some degree of coordination of this activity with the environment is required. Thus, you cannot build a foundation for a house in water or air - such an activity is incompatible with these types of environment. For example, you cannot ride a sled on water or float on it in the air, the conditions of the physical universe will not allow you to do so. Such limiting conditions exist in every area of the entrepreneur's activity, whether he or she is aware of them or not.
Thus, the most important task of data analysis is to understand some boundaries within which certain events or phenomena can exist. And then, when these boundaries are known, it is possible to structure the data within these boundaries, establish their interrelationships and build a knowledge system. Understanding the context of consumer interactions is a prerequisite for analyzing any data. A person's understanding of the context itself can be defined as their ability to interpret data or events within some known boundaries, some specific context.
Planting a Christmas tree in your own backyard is unlikely to be difficult for most people, but would it be as easy if the same tree had to be planted on the Moon? It would be a very difficult task, as it would require a new system of knowledge.
Secondly, the difficulty in introducing AI into organizations at the highest level reflects the inability to reconfigure the organization. Oftentimes, AI initiatives face huge cultural and organizational barriers [4]. One of the most powerful organizational barriers to the adoption of AI in business is the idea that AI will replace humans. In my opinion, this is a misguided and counterproductive position.
The very emergence of artificial Intelligence has raised the question of its correlation with natural Intellect. It will lead to a new division of labor: artificial Intelligence will deal with computation, while natural Intellect will focus on understanding, in other words, on placing data in a certain context. What we have here today is yet another form of division of labor. Previously, the division of labor was only in the realm of physical labor: the emergence of manufactories, factories, and program-controlled machines. However, now there is a division of labor related to intellectual tasks.
Natural Intelligence is concerned with developing goals, has the ability to discern multiple "shades" of meaning, the ability to set and solve problems, it is able to control its environment, to understand, feel, perceive and know. It possesses an intrinsic level of ethics, strives for survival and creation.
Ethics is an issue related to human survival. AI does not survive in our usual sense of the word. Therefore, AI can have morals, rules and laws, decision-making algorithms, but it cannot make judgments about human survival because it does not know about what life is. It cannot understand the feeling of having a child, communication with friends, meeting a simple long separation, death of a loved one, etc. AI is incapable of sympathy, anger, admiration, or other emotions. It can only imitate them. In terms of computation, it is an indispensable human tool, like scissors for a tailor. However, when the scissors begin to command the tailor, we have a problem with the tailor, with his state of mind.
Artificial Intelligence is primarily concerned with creating algorithms that can perform tasks that normally require human Intelligence. This includes problem solving, learning, planning, language understanding, perception, and robotics. To put this in historical perspective, in the 1970s and 1970s, with the advent of computers and automated control systems, there was another shift in the division of labor, which resulted in an increased role for office workers. At that time, many people said: "man is no longer needed. All the work will now be done by computers." This did not happen then and will not happen now.
Today we are standing on the threshold of a new division of labor among these specialists: some will understand the activity and set tasks for artificial Intelligence, the second - who cannot understand their activity, will perform the work planned by AI.
In the first case, the activity will be under human control, in the second case, the activity will be under AI’s control. Here, we must still define the limits in which natural Intelligence is ready to delegate tasks to artificial Intelligence.
In any case, the introduction of new methods complicates control systems. In the very near future, this will allow us to do things that were previously inaccessible and abandon obsolete methods. On this basis, we can assume that this division of labor - within natural Intelligence (understanding) and artificial Intelligence (computing) will lead to a rapid change in the entrepreneurial landscape. Many forms of business life will disappear, but a significantly larger number of new forms of business life are likely to emerge.
To be able to "interpret the data correctly", you must first place the question or task in a context, or in other words, frame it in a certain way. Ask a Scotsman what dinner is, and he will say it is a light snack before going to bed, at nine to ten o'clock. Ask an American the same question and he will say it is a delicious and nutritious multi-course meal at five to six in the evening. The word is the same, but the meaning is different.
Formulating a problem or question is always about "placing" it in context, in this or that "space of meaning." The question, "Have you still not solved your problem?" immediately places the interlocutor in a certain (albeit unpleasant) context. If the entrepreneur understands the environment in which he interacts with consumers, he can to some extent predict his future [1]. For example, from time to time intentionally planning the choice of this or that context of interaction - say, to plan a romantic meeting with your wife / husband (fiancé / bride) in a quiet cozy restaurant with dim lighting. With all originality, you are unlikely to invite your beloved on a romantic date in the factory shop, construction site or cemetery.
Choosing the right context for communication helps to give words and actions the desired meaning
In many cases, the context of communication can help determine much of the content of your message. For example, knowing that a police patrol pulled over a driver who overtook you five minutes ago at breakneck speed can easily help you anticipate the content of much of their conversation.
Why do peace talks fail on the battlefield?
The context of the battlefield does not imply peaceful negotiation, it implies the destruction of the enemy. In Rome, the mortality rate of battlefield participants during military operations was over 90% . This is why civilian mediators (rather than military generals) emerged, who organized meetings between warring parties far away from the front in order to reconcile them.
The upper limit of using AI
Understanding the environment and the impact of contextual factors on the field of activity is the upper limit of application of AI tools in entrepreneurship. In each specific case, this "ceiling" will be precisely the completeness of understanding the context of the activity. The same bottle of water will have a different value in a supermarket, an airplane, and a desert, so it would be prudent to know where that bottle of water will be sold before we design it.
If we don't have an answer to this question, or we don't know the context of future consumer interaction, then we risk selling a product designed for the desert on supermarket shelves.
Beyond this, one could postulate that there would have to be an ideal context for each product. Conversely, every context presupposes the existence of a product perfectly suited to that context. This means that it is not always necessary to change the product, one possible solution could be to change the context of interaction with consumers. For example, an "unsuccessful" bottle of water in a supermarket, let's start selling it in the desert and voila - success.
Prospects for AI development
Harvard Business School estimates that AI will add 13 trillion dollars to the global economy over the next decade [2]. However, the results of a survey of executives show that only 8% of firms are engaged in widespread adoption of AI [3], with most firms applying AI to only one business process. Why such slow progress? I conducted a separate study on this topic and came to the following conclusions:
Firstly, AI capabilities are only effectively utilized when a company has a good understanding of its environment, and consequently, the context of its interactions with consumers.
No activity happens by itself, but within a specific space in which such activity "unfolds". If, within this space there are conditions in which such an action can be potentially performed, then there is a probability that this or that action will be performed.
If the conditions existing in this space do not allow such an action to occur, then the zero result of such an action is obvious. In order for an activity in any area to become possible, some degree of coordination of this activity with the environment is required. Thus, you cannot build a foundation for a house in water or air - such an activity is incompatible with these types of environment. For example, you cannot ride a sled on water or float on it in the air, the conditions of the physical universe will not allow you to do so. Such limiting conditions exist in every area of the entrepreneur's activity, whether he or she is aware of them or not.
Thus, the most important task of data analysis is to understand some boundaries within which certain events or phenomena can exist. And then, when these boundaries are known, it is possible to structure the data within these boundaries, establish their interrelationships and build a knowledge system. Understanding the context of consumer interactions is a prerequisite for analyzing any data. A person's understanding of the context itself can be defined as their ability to interpret data or events within some known boundaries, some specific context.
Planting a Christmas tree in your own backyard is unlikely to be difficult for most people, but would it be as easy if the same tree had to be planted on the Moon? It would be a very difficult task, as it would require a new system of knowledge.
Secondly, the difficulty in introducing AI into organizations at the highest level reflects the inability to reconfigure the organization. Oftentimes, AI initiatives face huge cultural and organizational barriers [4]. One of the most powerful organizational barriers to the adoption of AI in business is the idea that AI will replace humans. In my opinion, this is a misguided and counterproductive position.
The very emergence of artificial Intelligence has raised the question of its correlation with natural Intellect. It will lead to a new division of labor: artificial Intelligence will deal with computation, while natural Intellect will focus on understanding, in other words, on placing data in a certain context. What we have here today is yet another form of division of labor. Previously, the division of labor was only in the realm of physical labor: the emergence of manufactories, factories, and program-controlled machines. However, now there is a division of labor related to intellectual tasks.
Natural Intelligence is concerned with developing goals, has the ability to discern multiple "shades" of meaning, the ability to set and solve problems, it is able to control its environment, to understand, feel, perceive and know. It possesses an intrinsic level of ethics, strives for survival and creation.
Ethics is an issue related to human survival. AI does not survive in our usual sense of the word. Therefore, AI can have morals, rules and laws, decision-making algorithms, but it cannot make judgments about human survival because it does not know about what life is. It cannot understand the feeling of having a child, communication with friends, meeting a simple long separation, death of a loved one, etc. AI is incapable of sympathy, anger, admiration, or other emotions. It can only imitate them. In terms of computation, it is an indispensable human tool, like scissors for a tailor. However, when the scissors begin to command the tailor, we have a problem with the tailor, with his state of mind.
Artificial Intelligence is primarily concerned with creating algorithms that can perform tasks that normally require human Intelligence. This includes problem solving, learning, planning, language understanding, perception, and robotics. To put this in historical perspective, in the 1970s and 1970s, with the advent of computers and automated control systems, there was another shift in the division of labor, which resulted in an increased role for office workers. At that time, many people said: "man is no longer needed. All the work will now be done by computers." This did not happen then and will not happen now.
Today we are standing on the threshold of a new division of labor among these specialists: some will understand the activity and set tasks for artificial Intelligence, the second - who cannot understand their activity, will perform the work planned by AI.
In the first case, the activity will be under human control, in the second case, the activity will be under AI’s control. Here, we must still define the limits in which natural Intelligence is ready to delegate tasks to artificial Intelligence.
In any case, the introduction of new methods complicates control systems. In the very near future, this will allow us to do things that were previously inaccessible and abandon obsolete methods. On this basis, we can assume that this division of labor - within natural Intelligence (understanding) and artificial Intelligence (computing) will lead to a rapid change in the entrepreneurial landscape. Many forms of business life will disappear, but a significantly larger number of new forms of business life are likely to emerge.
List of References:
1. Kaplan J., Business incognita: how to expand the boundaries of entrepreneurial thinking. Moscow: Alpina Publishers. 2022. 256 p.
2. Fountaine T., McCarthy B., Saleh T., Building the AI - Powered Organization // Harvard Business Review. 2019. URL: https://hbr.org/2019/07/building-the-ai-powered–organization (date of reference: 04.12.2023).
3. Bisson P., Hall B., McCarthy B., Rifai K., Breaking away: The secrets to scaling analytics // QuantumBlack, AI by McKinsey. 2018. https://www.mckinsey.com/capabilities/quantumblack/our-insights/breaking-away-the-secrets-to-scaling–analytics (accessed on: 04.12.2023).
4. Kaplan J., Communication in entrepreneurial activity: the use of artificial Intellect // Communicology. 2023. Vol. 11. No. 2. P. 139 - 147.
1. Kaplan J., Business incognita: how to expand the boundaries of entrepreneurial thinking. Moscow: Alpina Publishers. 2022. 256 p.
2. Fountaine T., McCarthy B., Saleh T., Building the AI - Powered Organization // Harvard Business Review. 2019. URL: https://hbr.org/2019/07/building-the-ai-powered–organization (date of reference: 04.12.2023).
3. Bisson P., Hall B., McCarthy B., Rifai K., Breaking away: The secrets to scaling analytics // QuantumBlack, AI by McKinsey. 2018. https://www.mckinsey.com/capabilities/quantumblack/our-insights/breaking-away-the-secrets-to-scaling–analytics (accessed on: 04.12.2023).
4. Kaplan J., Communication in entrepreneurial activity: the use of artificial Intellect // Communicology. 2023. Vol. 11. No. 2. P. 139 - 147.
