"Ten Thousand Words" Analysis of AI's Intelligent Transformation: How Intelligent Agents Drive Organizational Evolution

Introduction: The Rise of Intelligent Agents

With GPT-4o breaking through the technical bottleneck of cross-modal real-time interaction and the humanoid robot Figure 01 achieving end-to-end autonomous decision-making, global companies are ushering in a productivity revolution driven by AI agents.

More and more companies have keenly realized the huge value of this technological change and have accelerated the layout of intelligent applications. McKinsey (2025) research pointed out that artificial intelligence will bring about an additional productivity growth of about US$4.4 trillion to the world in the long run, and its transformative potential is comparable to the industrial revolution caused by the steam engine in the 19th century.

However, beneath the glamorous appearance of technological dividends, deeper challenges are quietly brewing:

• What short-term impact will intelligent agent technology have on corporate operations and management?

• How will intelligent entities reshape the future corporate management paradigm?

• How should companies proactively respond to the challenges brought about by these changes?

These issues are not only directly related to the current transformation practices of enterprises, but also determine the direction of future organizational evolution. Therefore, it is urgent for academia and industry to conduct in-depth discussions and exchanges.

This article will combine the latest trends in technological development and the actual needs of enterprise management to conduct an in-depth analysis of the above key issues, in order to provide useful reference for the transformation and development of enterprises in the era of intelligent bodies.

Intelligent Agents: New Quality Producers in the AI ​​Era

1. Definition and Evolution of Intelligent Agents

Intelligent agent, or AI Agent, is an important concept in the field of artificial intelligence. The classic AI textbook Artificial Intelligence: A Modern Approach (Russell and Norvig, 2020) defines it as: "An autonomous entity that can perceive the environment through sensors and act on the environment through actuators." This definition highlights the environmental adaptability and goal orientation of the intelligent agent.

Margaret Boden (2006), a cognitive scientist at the University of Cambridge, further distinguished between two types of intelligent agents in Mind as Machine: A History of Cognitive Science:

• Reactive agents: such as early rule-based chatbots, which adopt a “stimulus-response” model and lack deep reasoning capabilities.

• Cognitive agent: A complex system with internal state representation and reasoning capabilities, combining symbolic logic and machine learning to truly achieve the evolution from the “stimulus-response” to the “perception-decision-action” closed loop.

In the business context, Andrew McAfee (2017), director of the MIT Center for Digital Business, proposed in his book Machine, Platform , Crowd: Harnessing Our Digital Future : "Intelligent agents are not simple automation tools, but 'digital business partners' with situational understanding capabilities and the ability to plan actions autonomously." This view reveals the essential characteristics of intelligent agents that distinguish them from traditional IT systems - they are not only process execution tools, but also business logic analyzers and decision makers.

2. Recent Advances in Intelligent Agent Technology

The famous artificial intelligence company OpenAI (2023) pointed out in the GPT-4 Technical Report that the intelligent agent is "an autonomous system that completes complex tasks through natural language instructions based on a large language model." The white paper particularly emphasizes "tool call autonomy" and "multi-step task planning capabilities", which marks the evolution of the intelligent agent from a single functional module to a commercial entity with strategic thinking.

We believe that intelligent agents are algorithm-driven silicon-based organisms with the following five capabilities:

(1) Environmental perception: Acquire real-time data through a variety of sensors and accurately identify changes in the surrounding environment.

(2) Knowledge reasoning: combining symbolic logic and machine learning algorithms to conduct in-depth analysis and judgment of data.

(3) Memory learning: Continuously optimize the knowledge system and decision-making model based on a large amount of historical data.

(4) Autonomous decision-making: Quickly analyze various action plans, evaluate their potential benefits, and select the optimal strategy.

(5) Dynamic Interaction: Communicate naturally with humans and other systems to support information sharing and collaborative work.

3. Analysis of the core capabilities of intelligent agents

Learning ability is the core competitiveness of intelligent agents. Through machine learning algorithms, intelligent agents can extract features from structured and unstructured data, discover patterns, and continuously optimize their own knowledge system. They can quickly adapt to complex and changing business environments and meet diverse business needs.

In terms of decision-making ability, the agent uses decision-making algorithms to reason and judge based on learned knowledge and real-time environmental data. It can weigh multiple options in a very short time, evaluate their benefits and risks, and ultimately choose the optimal or near-optimal action path. For example, in the field of financial investment, the agent can integrate market data, financial statements, and news information to quickly make accurate investment decisions.

Interaction capabilities enable intelligent agents to collaborate smoothly with human users and other systems. With the help of natural language processing technology, intelligent agents can understand semantics, context and emotions, and provide personalized services and support. At the same time, intelligent agents can share information and work together through specific communication protocols to complete complex tasks.

4. Management changes driven by intelligent agents

Herbert Simon, the founder of the decision theory school and a pioneer of artificial intelligence, proposed the classic proposition of "management is decision-making" in Administrative Behavior , laying the foundation for organizational decision-making theory. Under the traditional management paradigm, decision-makers are usually humans, who are limited by the cognitive boundaries of biological intelligence, such as information processing speed, multi-dimensional analysis capabilities, and decision-making stability.

Intelligent agents break through the constraints of these bounded rationalities through algorithmic models. They can be deeply integrated into the business links of enterprises, such as R&D design, manufacturing, marketing and customer service, to comprehensively improve the operational efficiency and competitiveness of enterprises. Intelligent agents are not only executors, but also the core driving force for the intelligent upgrade of enterprises, becoming new quality producers in the AI ​​era.

Intelligent entities reshape enterprises:

Short-term impact and long-term impact

Short-term impact

1. Positive side

(1) Reduce costs and improve efficiency

In repetitive and regular work tasks, intelligent agents demonstrate unparalleled efficiency advantages.

For example, in terms of data entry, traditional manual data entry is not only slow, but also prone to errors, and requires a lot of time and energy to review and correct. Intelligent agents can complete data entry quickly and accurately, greatly improving work efficiency.

In file organization, intelligent agents can quickly classify, retrieve and archive large amounts of files, significantly reducing manual processing time and labor costs.

In the production process, the intelligent agent plays a key role through real-time monitoring and in-depth analysis. It can comprehensively analyze the operation data of production equipment, raw material consumption data and product quality data, accurately identify bottlenecks and problems in production, and propose optimization solutions.

For example, in automobile manufacturing companies, intelligent agents optimize production scheduling and equipment maintenance plans by analyzing the time, equipment utilization and component quality of each process on the production line, which significantly improves the production line utilization rate and reduces production costs.

In addition, the intelligent agent can dynamically adjust production plans based on market demand forecasts and inventory conditions to prevent overproduction or inventory backlogs, and further optimize resource utilization efficiency.

(2) Enhance customer personalized experience

With its powerful data analysis and prediction capabilities, the intelligent agent can gain in-depth insights into customer needs and preferences. By analyzing customers’ historical purchase behaviors, browsing records, and evaluation feedback, the intelligent agent can accurately portray the personality of each customer and understand their interests, consumption habits, and potential needs.

Based on this, the intelligent agent can realize product recommendations and service customization for thousands of people, bringing customers highly personalized product and service experience.

In the field of customer service, intelligent customer service has become one of the important applications. 7×24 hours online service can answer customer questions at any time. Compared with manual customer service, intelligent customer service can respond instantly without long waiting time, and can handle a large number of inquiries at the same time, significantly improving service efficiency and coverage.

With the help of natural language processing technology, intelligent customer service can accurately understand the semantics and emotions of customer inquiries, provide solutions in a friendly and professional tone, and effectively improve customer satisfaction.

On e-commerce platforms, the intelligent agent analyzes customer browsing and purchasing behaviors in real time, accurately recommends products that meet customer preferences, and creates a personalized shopping experience. After the purchase, the intelligent agent continues to track usage feedback and provide timely after-sales support to enhance customer stickiness and loyalty.

(3) Accumulating and renewing private domain knowledge

Although the large language model (LLM) can collect public knowledge, the private knowledge of enterprises is often difficult to access. By deploying the large language model within the enterprise, the intelligent agent can continuously learn and accumulate the internal knowledge, experience and data of the enterprise, and gradually build a private knowledge system.

The intelligent agent can integrate internal technical documents, market research reports, customer cases and employee experience sharing to form a structured knowledge base. This system can not only promote knowledge inheritance and sharing, prevent knowledge loss due to personnel turnover, but also stimulate employees' innovative thinking.

For knowledge-intensive enterprises, the accumulation of private domain knowledge is particularly critical. For example, professional consulting companies learn industry cases and project experience through intelligent agents to form highly specialized knowledge assets, provide strong support for consulting projects, and form market competitive advantages. At the same time, intelligent agents can also conduct in-depth mining of this knowledge, discover potential value and innovation points, and provide forward-looking support for corporate strategic decision-making.

2. Negative aspects

(1) Short-term costs are rising instead of falling

In the early stages of introducing intelligent entities, companies often face tremendous financial pressure.

• Technology R&D: Although existing solutions are mature, companies usually need to customize their development based on specific needs, which involves investment in artificial intelligence talent and technology R&D costs.

• Equipment procurement: The operation of intelligent entities requires hardware such as high-performance servers and graphics processors, and the cost of purchasing equipment is high.

• Employee training: To ensure the implementation of the technology, companies need to provide operational training and collaborative work guidance to their employees, which requires a large investment of manpower and financial resources.

In addition, it often takes a certain period of time for the intelligent body to reduce costs and increase efficiency. In the initial stage, the performance of the intelligent body has not been fully optimized, and there is inevitably uncertainty in the coordination and running-in with human beings, so it is difficult to achieve significant efficiency improvement in the short term.

A McKinsey (2025) survey showed that 92% of companies plan to increase AI investment in the next three years, but only 1% of leaders believe that their company has reached a mature stage in AI deployment.

(2) Inducing organizational turmoil

After the introduction of intelligent entities, the traditional human-centered organizational structure will face profound reshaping.

In some departments where repetitive work is replaced, the job settings and division of responsibilities will be greatly adjusted, and some employees may face career uncertainty. This change is likely to cause psychological pressure on employees, leading to a decline in work enthusiasm and frustration of team morale.

For example, after the finance department introduces intelligent report processing, some employees may feel confused about their career development or even develop resistance and be unwilling to cooperate with the new way of working.

(3) Increased data security risks

When intelligent agents are deeply involved in data interaction, their technical complexity becomes a data security risk.

For example, the 2023 Black Hat conference revealed that attackers induced intelligent customer service to leak core algorithm parameters by embedding "reverse engineering instructions". This attack method exploited the context learning vulnerability, bypassed content filtering through complex prompt words, and forced the intelligent agent to inadvertently disclose technical details.

In generative AI, such risks are particularly prominent. An early version of GPT-4 was exposed to a “principle disclosure vulnerability” that allowed users to obtain training data. Although this vulnerability has been fixed, the evolving attack methods warn us that the stronger the interactive capabilities of an intelligent agent, the greater the risk of data leakage.

2. Far-reaching impact

Agent-driven organization: from concept to reality

Faced with the impact of intelligent agent technology, different companies naturally have different strategies due to differences in resource endowments and development conditions. However, in the long run, it has become a general trend to fully integrate intelligent agents into corporate operations. In the future, companies will inevitably evolve into intelligent agent-driven organizations (ACOs).

Compared to traditional human-driven organizations (HCOs), ACOs will undergo two fundamental changes:

(1) Management subject level: shift from the “single human decision-making” model to the “human-machine collaborative decision-making” architecture.

(2) Management object dimension: Expanded from “pure carbon-based individuals” to “carbon-based and silicon-based collaborative communities”.

This means that humans and intelligent agents are no longer limited to the single roles of subject and object. Both can participate in decision-making as management subjects and accept commands as management objects. This dual shift is not a simple conceptual extension, but will trigger a systematic innovation and reconstruction of the organizational management system. Its profound changes are reflected in the following six key dimensions.

1. Reconstruction of governance structure: the rise of one-person companies

Economist Ronald Coase believed that the boundaries of enterprises are determined by the balance between management costs and transaction costs. However, the rise of intelligent agent technology is breaking this balance and reshaping enterprise boundaries.

The rise of superindividuals: from teams to individuals

Clusters of intelligent entities enable super-individuals to possess full-factor capabilities comparable to those of traditional enterprises.

For example, independent developers can use AI code generation tools to efficiently complete full-stack software development, and their productivity can even rival that of a team of 100 people. This phenomenon poses a subversive challenge to the governance model of large companies based on equity aggregation, and promotes the rapid rise of the "one-person company" model.

The essence of this change is that the business model is transforming from platform capitalism to individual empowerment.

From Equity Control to Capability Symbiosis

Blockchain smart contract technology enables intelligent entities to participate in value distribution, such as automatically executing intellectual property rights revenue sharing. The decentralized autonomous organization (DAO) model realizes the self-consistency of cross-regional intelligent entities.

These technological advances are driving the transformation of corporate governance from the traditional "equity control" model to the "capability symbiosis" model.

This shift calls for a new governance framework that includes three main aspects:

(1) Human creativity rights confirmation: clarifying people’s creative contributions in intelligent decision-making.

(2) Definition of the rights and responsibilities of the intelligent agent: clarify the authority and responsibilities of the intelligent agent in task execution and decision-making.

(3) Distributed profit distribution: Automated and fair profit distribution is achieved through smart contracts.

This framework will provide a more flexible and efficient institutional foundation for future business activities while ensuring the balance and protection of the interests of all parties.

One-person company: the prototype of future enterprises

In an intelligent-driven organization, a one-person company is not just a symbol of individual entrepreneurship, but also a new evolution of organizational form. Super individuals can achieve high-efficiency output at extremely low management costs through the collaboration of intelligent clusters, bringing huge institutional impact and innovation opportunities to future businesses.

2. Transformation of organizational form: from pyramidal bureaucracy to dynamic network collaboration

The information attenuation and decision-making lag of the traditional bureaucracy can no longer adapt to the high-frequency, multimodal collaboration between intelligent agents and human employees.

New organizations are showing the network characteristics of "decentralized nodes + intelligent links":

• Front-end business intelligence: respond to customer needs in real time, such as intelligent customer service generating personalized solutions in seconds.

• Mid-range process agents: dynamically allocate resources, such as supply chain agents matching the optimal logistics path.

• Back-end strategic agent: Provides trend predictions, such as the market insight agent to develop a three-year technology roadmap.

• Human managers: act as “value anchors” to control strategic direction, such as establishing product ethical boundaries.

This structure enables the organization to have agility similar to that of an organism, forming a symbiotic evolutionary system between efficient machine execution and human innovative breakthroughs.

3. Decision-making mechanism upgrade: intelligent revolution driven by dual systems

Nobel Prize winner Daniel Kahneman proposed the "System 1/System 2" theory, which reveals the human decision-making mechanism:

• System 1: intuitive, quick decision making;

• System 2: Rationalization, slow thinking.

In an Agent-Driven Organization (ACO):

• Machine decision-making (system 1): The intelligent agent cluster responds quickly to market changes and makes regular decisions in real time.

• Human Decision Making (System 2): Managers engage in strategic thinking and counter-consensus judgment, dealing with long-term decisions in complex environments.

This dual-wheel drive model of "machine fast response + human deep thinking" combines the advantages of machine data processing and human strategic judgment capabilities, greatly improving the intelligence level of the organization.

4. Optimizing the incentive system: Building a value cycle of human-machine symbiosis

In ACO, human-machine collaboration is the core model of value creation.

• Human employees: As intelligent agents collaborate to improve productivity, the way of working will transform to creativity. Incentive mechanisms should strengthen autonomy and long-term incentives, such as diversified compensation and welfare incentives.

• Agent: Set incentive functions based on algorithmic logic, such as task completion accuracy and resource utilization efficiency, to ensure that its computing strategy is consistent with organizational goals.

By building a hybrid incentive model of "human creativity incentive-machine computing power optimization", we can not only unleash the unique value of humans in solving complex problems, but also maximize the data processing efficiency of intelligent agents.

5. Evolution of knowledge management model: from experience accumulation to metacognition construction

Professor Rui Mingjie pointed out in "Growth and Innovation of Knowledge-based Enterprises": In the era of knowledge economy, knowledge has leapt from a traditional production factor to a core strategic resource of enterprises. Knowledge-based enterprises are mainly driven by knowledge innovation.

The traditional knowledge management model relies on systems and incentives, showing the characteristics of "command learning-passive sedimentation". With the breakthrough of intelligent agent technology, knowledge management is moving from "human experience accumulation" to "intelligence agent autonomous learning". However, although intelligent agents can automatically acquire and update knowledge, the processing and refinement of massive data has become a new bottleneck - how to transform unstructured information into effective knowledge and inject development momentum has become a key challenge for enterprises.

The rise of meta-knowledge management is a response to this problem. It focuses on "knowledge of knowledge" and systematically manages knowledge systems, data logic and cognitive frameworks; it not only improves knowledge processing efficiency, but also builds an innovative ecological closed loop from data to wisdom. Meta-knowledge management helps companies break through traditional boundaries, integrates fragmented information accumulated by intelligent entities into dynamic knowledge graphs, and provides in-depth support for decision-making.

In the future, building a complete meta-knowledge management system and strengthening the meta-cognitive ability of the organization will become the core competitiveness of intelligent-driven organizations. This cognitive foundation will help companies lock in their development direction amid uncertainty.

6. Collaborative Reshaping of Business Ecosystem: Emergence and Collaboration of Cross-Domain Intelligent Bodies

In the early days of intelligent agent applications, collaboration was mainly limited to agents within the same enterprise. As technology penetration deepens, more and more market players are beginning to deploy intelligent agents, and the traditional cross-organizational collaboration model that relies on interpersonal division of labor is being subverted.

The collaboration paradigm is shifting from a model that is limited by corporate boundaries and has a rigid structure to a dynamic network with deep collaboration across enterprises and multiple agents.

The A2A (Agent-to-Agent) protocol released by Google in 2025 laid the basic framework for cross-enterprise multi-agent collaboration.

Unified intelligent communication standards break down organizational barriers, expand the scope of knowledge sharing, greatly improve business collaboration efficiency, and accelerate the intelligent transformation and innovative development of the business ecosystem.

Figure 1 Schematic diagram of ACO management paradigm shift

The path to change: the era of intelligent agents

Enterprise response strategy

1. Senior management strengthens AI learning and bridges the gap in reverse cognition with employees

In the future, the intelligence level of agents will continue to improve, and their professional capabilities will continue to improve. A large number of agents (clusters) will be deeply embedded in enterprises, which will give rise to organizational intelligence and significantly enhance competitiveness. Agent-driven organizations (ACOs) are bound to become the mainstream. However, there are not many executives who can truly understand this trend. Due to their age and the nature of their work, many managers do not have a deep understanding of AI tools and use them less frequently, so they tend to underestimate the impact of AI.

A study by McKinsey (2025) shows that C-level leaders’ estimates of employee use of generative AI are far lower than the actual level. Executives believe that only 4% of employees use generative AI for at least 30% of tasks in their daily work, while the actual proportion is three times higher than the estimate, reaching 13%. This cognitive deficit between executives and employees is hindering the intelligent transformation of enterprises.

The solution is to establish a regular AI learning system, especially to strengthen the learning and practical experience of senior management. Senior management needs to actively think about how intelligent bodies can reshape business, innovate organizational structure, and even subvert existing models. Enterprises can:

• Invite industry experts to hold technical seminars;

• Organize management to participate in cutting-edge summits;

• Regularly publish white papers on intelligent agent applications;

• Conduct cross-departmental discussions and internal case sharing.

The above measures can break down cognitive barriers, promote a unified consensus on the strategic value of intelligent entities, eliminate fear and resistance, and lay a solid ideological foundation for intelligent transformation.

2. Carry out empowerment-oriented organizational reform to stimulate employees’ innovative vitality

Many employees have taken the initiative to use external agents to improve efficiency and have a strong desire to learn. However, once an enterprise plans to formally introduce agents, employees often worry that their jobs will be replaced. This worry may become a major obstacle to promoting the application of agents.

John Kotter, an authority on organizational change, points out that successful change is inseparable from a clear vision that benefits employees. Therefore, when companies turn to an intelligent-driven model, they must make their employees clearly feel the "three core values":

• Increased competitiveness: Human-machine collaboration will significantly enhance organizational strength.

• Experience optimization: Intelligent agents take on repetitive tasks, making employees’ work easier and more fulfilling.

• Capacity leap: Employees will be upgraded from “executors” to managers and leaders of intelligent entities.

This "empowerment"-oriented transformation can alleviate employee anxiety and stimulate innovation.

Microsoft predicts in its report "2025: The Year of the Birth of Frontier Enterprises" that in the future, every employee is expected to become an "intelligent agent supervisor". In this regard, companies should:

• System training: teaching agent management skills such as task allocation, process optimization, and performance evaluation.

• Knowledge sharing: Establish incentive mechanisms to encourage employees to share professional experience, so that personal knowledge becomes a key resource for building intelligent entities.

• Collaborative atmosphere: Create an open environment and encourage employees to explore new business models and innovative solutions with the help of intelligent agents.

Through the above measures, companies can fully stimulate the enthusiasm and creativity of employees, pool innovative efforts, and ensure continuous evolution and leadership in the intelligent era.

3. Innovate knowledge management models and consolidate the foundation for intelligent transformation

Knowledge is power.

Private domain knowledge includes key information such as business processes, customer insights and technology accumulation, and is the source of a company’s core competitiveness. As early as the embryonic stage of knowledge management theory, management scholars have already seen its strategic value - Peter Drucker proposed "knowledge workers" and Ikujiro Nonaka proposed "knowledge-creating enterprises", both of which are typical examples of early exploration.

Traditional Dilemma

• “Storage is dormancy”: Massive documents, reports and manuals lack intelligent indexing and scenario association, making them difficult to be accurately activated at the front line of business, forming a paradox of “rich data and poor knowledge”.

• High explicit cost: The process from experience extraction to structured output is highly dependent on manual labor, time-consuming, with large quality fluctuations, and an unbalanced input-output ratio.

Innovation Path AI technologies such as large language models provide revolutionary tools for solving difficult problems. Instead of blindly pursuing short-term cool but easily depreciated AI applications, it is better to focus on knowledge management scenarios with longer payback cycles and high compatibility with existing technology routes.

• Systematic sorting: Relying on semantic understanding and logical reasoning capabilities, the knowledge fragments scattered across various departments and systems are unified and standardized.

• Automatic capture and sedimentation: With the help of intelligent agents, implicit experience in business processes can be captured in real time and automatically sedimented into structured assets.

• Scenario-based push: Intelligent matching and push based on specific business scenarios, allowing the right knowledge to reach the right position at the right time.

Only by reshaping the foundation of knowledge management can enterprises move forward steadily on the road of intelligent transformation and truly transform "data" into "wisdom".

4. Integrate internal and external intelligent resources to drive high-quality strategic transformation

Enterprise transformation affects the entire enterprise. Whether the direction is accurate and the process is smooth is the core challenge that all enterprises must face. In the past, enterprises relied on external consulting companies. However, this model is costly, inefficient, and often out of touch with the actual situation of the enterprise, making it difficult to implement strategic reports. In the AI ​​era, traditional strategic planning and execution models have been difficult to adapt to the new business environment, and change is imminent.

Enterprises should fully unleash the power of AI. First, build a professional data collection system to capture multi-dimensional information such as market trends, competition dynamics, and policy changes in real time. Then, use advanced algorithms to deeply mine these data and build the company's "super brain" - strategic analysis agent. This agent can quickly generate accurate industry insight reports and warn of potential risks and opportunities in real time.

In addition to building their own "super brains", companies can also flexibly purchase intelligent agent services from external consulting agencies. McKinsey has launched its internal AI assistant Lilli, and Xavier AI, the world's first artificial intelligence strategic consulting company, has also emerged. It is foreseeable that delivery agents will become the standard in the consulting industry. These external agents are professionally trained and have deep domain knowledge, and can provide companies with strategic advice from multiple perspectives; they are always online and responsive at any time, making up for the time constraints of traditional consultants.

During the strategic planning phase, companies can hold regular workshops to engage internal and external agents in collaboration with human experts:

• Human experts contribute industry experience and innovative thinking, and propose visionary strategic ideas;

• The agent quickly simulates multiple strategic paths and provides quantitative analysis results.

During the implementation phase, enterprises should continue to interact with intelligent entities and dynamically adjust plans based on real-time data to ensure efficient implementation of strategies. Through deep human-machine collaboration, enterprises can not only successfully complete high-quality strategic transformation, but also take the lead in moving towards intelligent entity-driven organizations and gain an advantage in the wave of intelligence.

(V) Formulate a differentiated transformation path to match the development characteristics of the enterprise

There is no universal solution for intelligent transformation. Industry attributes, business models, market environments and technology scenarios are different; enterprise scale, technology reserves, brand influence, organizational structure and culture also vary greatly. When starting the transformation, enterprises must examine the characteristics of the industry and their own reality, weigh the pros and cons, and choose a path that matches the pace of development, rather than blindly pursuing the theoretical "optimal solution". Incumbent companies and start-ups face completely different situations, so each should adopt its own strategy.

1. Incumbent companies should adopt smart transformation: proceed step by step to reduce the risk of disruption

Incumbent companies have large and complex organizational structures and business processes, and are vested interests in the industry. For such companies, prudentness is more important than radicalism. The introduction of intelligent agent technology should start with local pilot projects:

• Small-scale implementation: Select non-core processes or single projects for trial, such as human resources recruitment, financial reimbursement automation, etc.

• Accumulate experience: Understand the effects and difficulties of intelligent agents in actual scenarios through pilot projects.

• Gradual expansion: After the pilot is successful, the application of intelligent agents will be extended to more links to steadily establish a human-machine collaboration model.

During the implementation process, employee training and process optimization need to be promoted simultaneously:

• Develop customized training plans for different positions to enhance collaborative skills.

• Streamline business processes, eliminate the mismatch between agents and legacy processes, and ensure smooth integration of technology.

Take traditional manufacturing as an example: first introduce intelligent agents to optimize scheduling in a certain link of the production line; after success, expand to areas such as quality inspection and equipment predictive maintenance, and adjust processes and train personnel. This step-by-step approach can avoid operational risks and employee resistance caused by large-scale disruptions, and ultimately achieve full intelligent agent drive.

2. Start-ups’ breakthrough: accurate track selection and single-point breakthrough

Startups are lightly equipped, have flexible organizations, innovative thinking, and less historical baggage, making it easier for them to quickly apply intelligent technology. The key is to accurately identify high-potential tracks and concentrate resources on creating explosive products or services:

• Focus on disruptive scenarios: such as intelligent education tutoring system, intelligent copywriting generation, intelligent design assistance, etc.

• Rapid iteration: Closely track user feedback and continuously optimize functions and performance.

• Create a differentiated brand: Establish barriers in niche areas and complement or cooperate with large companies.

By "selecting the right track + single point breakthrough", start-ups can quickly stand out in the wave of intelligent entities, seize market opportunities and form unique influence.

Conclusion: Creating a better future together

The future is here, and change is coming.

Intelligent technology is sweeping in at an unprecedented rate, bringing both opportunities and challenges to enterprises.

(1) Short-term perspective:

Advantages: Reduce costs and increase efficiency, optimize customer experience, and promote knowledge accumulation.

Risks: rising costs, organizational fluctuations, and data security risks.

(2) Long-term perspective: Intelligent entities will deeply penetrate the entire business chain of an enterprise, completely reshaping the management paradigm and competitive landscape.

Management practitioners should be sensitive to the development trends of intelligent-driven organizations, actively embrace change, and reshape management systems to match the new demands of the AI ​​era.

Academic researchers should break down disciplinary barriers, promote the deep integration of artificial intelligence with social sciences and ethical philosophy, and create a theoretical framework for the application of intelligent bodies that is both forward-looking and humanistic.

When technological dividends and humanistic care resonate at the same frequency, and when corporate practice and academic research form a virtuous circle, the new era of human-machine collaboration will truly arrive.

Let us use rationality as a lamp to illuminate the journey of change; use innovation as a key to open the door to transformation, and work together to write a new chapter in the evolution of enterprises and human development in the intelligent era.