In today’s digital wave, artificial intelligence (AI) has become a key technology for enterprise transformation and upgrading. However, a large number of AI projects in which companies have invested heavily have failed to achieve their expected goals and have even become flashy “technology shows”. This paper provides an in-depth analysis of the common reasons for the failure of enterprise AI projects, including blind follow-up, technology-led thinking, ambiguous definition of results, and ignoring landing costs, and proposes strategies such as returning to the essence of demand, starting with the end, and using minimal verification to help enterprises truly achieve value creation in AI applications and avoid falling into the “gimmick trap”.
1. Introduction: The “heat” and “confusion” of AI
In today’s era of digital waves, artificial intelligence (AI) has emerged as the brightest star in the business sky. Turning on financial news, Internet giants are rushing to release AI models, claiming to reshape the industry pattern; Walking into the traditional manufacturing workshop, pictures of robotic arms equipped with AI algorithms for precise operation frequently appear; Financial institutions use AI to achieve intelligent risk control, and retail stores use AI for precision marketing……
All walks of life are holding high the banner of AI, regarding it as a key weapon for enterprises to transform and upgrade and seize market opportunities. The capital market is also eager to see AI concepts, with countless AI startups receiving huge amounts of funding with seemingly cutting-edge technology ideas, as if they have infinite possibilities as long as they are involved with AI.
However, behind this nationwide embrace of AI, there is a worrying shadow. A large number of AI projects have gradually deviated from the track during the implementation process, becoming flashy “technology shows” or “face projects” that decorate the façade.
According to authoritative research data, more than 60% of enterprise AI projects fail to achieve the expected goals, and some projects spend millions or even tens of millions of yuan in R&D funds, but are eventually forced to be shelved because they cannot be put into practical applications. Some companies have built complex AI systems, but due to cumbersome operations and disconnection from business processes, employees are reluctant to use them, and the equipment is idle for a long time.
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A well-known home appliance manufacturing company has invested heavily in the research and development of an AI intelligent production scheduling system, trying to optimize the production process through algorithms, but due to insufficient consideration of complex variables in actual production scenarios, the scheduling scheme given by the system cannot adapt to the changeable production line, and the final project failed, and a large amount of resources were wasted.
Behind this phenomenon of “driving high and going low”, a sharp core contradiction is exposed: enterprises focus too much on the technical capabilities of AI, but seriously ignore the transformation of technology into business value. Many companies fall into the misunderstanding of “technology worship”, blindly pursuing the novelty and advancement of AI technology, believing that as long as they introduce the latest deep learning models and the most advanced algorithm frameworks, they can stand out in the fierce market competition.
Driven by this misconception, companies often forcibly develop unrealistic AI use cases without sufficient research and demonstration. For example, some small retail companies have followed the trend of developing AI virtual shopping guides, but due to the lack of sufficient user behavior data support, the shopping guide function is useless; Some local banks are eager to launch AI financial advisory services, but they ignore the complexity of financial products and the diversity of customer needs, causing a crisis of customer trust. These AI applications that are out of touch with actual needs not only fail to improve the operational efficiency of enterprises, but also cause serious waste of human, material and financial resources.
The root of these problems is that companies see AI as a goal, not a tool. The true value of AI lies not in the technology itself, but in its ability to solve practical problems, improve business efficiency, or create new business opportunities.
In the wave of digital transformation, artificial intelligence technology is experiencing a frenzy period similar to that of “Internet +”. As a product manager for more than ten years, I have seen too many companies fall into this cycle:
- Panic layout: Worried about being abandoned by the times, hastily set up an AI laboratory
- Conspicuous investment: procurement of top computing power equipment, high-paying poaching algorithm team
- Ritual landing: develop “smart kanban” and “prediction system” that are disconnected from business
- Silent unfinished: After the project is accepted, it will be put on the high shelf
Standing at the crossroads of the booming development of AI technology, enterprises have to pause and reflect: have they lost their way in the process of embracing AI? The answer is clear: AI is essentially a tool, not an end.
Only by returning to the demand itself and solving actual business problems as the starting point can AI truly exert its value and become a strong driving force for enterprise development. This is not only a test of the strategic decision-making ability of enterprises, but also a key proposition related to whether enterprises can achieve sustainable development in the digital age.
When the heat of technology hype fades, it will be those companies that adhere to valueism that will eventually remain on the beach. As a key bridge between business and technology, product managers must help companies maintain this bottom line: any AI investment must be translated into specific numbers on financial statements.
2. Misunderstanding diagnosis: Why do enterprises fall into the “AI gimmick trap”?
2.1 Following the trend: the “prisoner’s dilemma” in the era of digital transformation
At the moment of rapid technological change, the atmosphere of the times of “technology-only” is like an invisible pressure, enveloping every enterprise. There is a strong sense of anxiety among corporate decision-makers that stems from concerns about the changing competitive landscape of the industry, fearing that once they fall behind in the application of AI technology, they will be ruthlessly abandoned by the times. This psychology often causes companies to lose their rational judgment in the face of the AI boom and fall into the whirlpool of blindly following the trend.
When leading companies in the industry launch innovative applications such as AI customer service and intelligent recommendation systems, many small and medium-sized enterprises will quickly follow suit and can’t wait to launch similar projects. For example, in the field of e-commerce, after a leading platform significantly increased the conversion rate of user purchases with AI personalized recommendation algorithms, many small and medium-sized e-commerce companies have followed suit and invested a lot of resources to develop their own recommendation systems. However, many of these companies do not deeply analyze the behavioral characteristics of their own user groups before development, nor do they evaluate whether the existing data foundation supports algorithm operation, simply because they are afraid of being labeled as “technologically backward”.
After serving 30+ enterprises in digital transformation projects, I found a surprising pattern: 80% of AI projects are initiated based on competitor dynamics rather than business needs. This industry anxiety has given rise to “benchmarking syndrome”.
Case: A regional bank saw a large state-owned bank launch robo-advisory and hurriedly purchased the same system, and found that:
- The scale of customer assets is insufficient to support model training
- Wealth manager resistance leads to system idleness
- In the end, 3 million was invested to serve only 17 customers
This kind of blind benchmarking against industry hotspots is essentially a “technology race” with no end. Enterprises are exhausted in this competition, constantly investing human, material and financial resources, but ignoring the uniqueness and actual needs of their own business. In the end, these hastily launched projects are disconnected from the actual operation of the enterprise, not only failing to bring the expected benefits, but becoming a heavy burden, falling into the vicious circle of “AI for AI’s sake”.
2.2 Technology-led thinking: the business trap of engineer culture
Technology-led thinking is another key factor in companies falling into the “AI gimmick trap.” In many enterprises, the technical team dominates the AI project planning process, often from the perspective of “what AI can do”, and with their passion and expertise in new technologies, they come up with cool application solutions.
In AI projects led by technical teams, there is a common inversion of “finding solutions to problems”. Typical technical thinking path:
These solutions may be highly innovative at the technical level, such as using cutting-edge deep learning models, complex natural language processing techniques, etc., but they rarely think about “what the business needs” from the standpoint of the business unit.
Taking a large manufacturing company as an example, its technical team has independently developed a complex AI data analysis system to improve the digital level of the enterprise. The system uses advanced machine learning algorithms to deeply mine and analyze massive data in the production process.
However, during the development process, the technical team lacked sufficient communication with the business department and did not have an in-depth understanding of the actual needs of the production department in terms of equipment maintenance, quality control, etc. After the system is launched, although a large number of analysis reports can be generated, the content of these reports does not match the decision-making needs of the business department, and cannot provide effective support for actual business such as production process optimization and failure prediction.
In the end, this costly system can only be shelved and become a typical case of disconnect between technology and business. This way of thinking leads to AI projects becoming “self-hilary” for technical teams, and although the developed functions are technologically advanced, they cannot solve business pain points and naturally cannot generate real value.
Compare the value realization path of health:
Case library of lessons learned from blood and tears:
2.3 Blurred definition of results: Loss of value caused by failure of KPIs
Ambiguous definitions of outcomes are a common problem in enterprise AI projects and a significant reason why projects fall into the “gimmick trap.” In practice, many companies equate “launching AI capabilities” with project success, assuming that the project has achieved phased results as long as the system is functioning properly, without setting clear, measurable key performance indicators (KPIs). This vague definition of outcomes makes it difficult for companies to accurately assess the actual impact of AI projects and determine whether they are truly delivering the expected business value.
Taking the AI customer service system launched by a financial institution as an example, the institution only took the system launch as the main goal during the project planning stage, and did not set specific KPIs such as the increase in customer satisfaction and the reduction ratio of manual customer service costs. After the system was launched, although the function of automatically replying to frequently asked questions was implemented, due to the lack of clear evaluation criteria, enterprises could not determine whether the system truly improved customer service efficiency and effectively reduced operating costs. In addition, due to the lack of quantitative goals, it is difficult to conduct effective performance appraisal for the project team, resulting in a lack of motivation for the team to improve and optimize the system.
After reviewing 200+ AI projects, I have summarized three typical KPI pitfalls:
1. Vanity indicators are proliferated
- Focus on “99% model accuracy” but ignore “business decision adoption rate”
- Pursuing “100 times faster processing” but “unchanged service throughput”
2. Process indicator substitution
3. Broken chain of responsibility
- The technical team delivers the “available system” and completes the task
- The business department abandoned the use because it “won’t use/doesn’t use it”
- In the end, it became a zombie system of “three don’t care”
In the long run, AI projects have gradually lost their direction and become formalistic “face projects”. Clear KPIs are not only a measure of project success but also an important tool to guide the project towards business value creation. Without KPI constraints, enterprises can easily lose track of their goals in AI projects and effectively combine technology and business value.
2.4 Ignoring landing costs: the iceberg hidden behind the technology
The implementation of AI projects is a complex system engineering that involves not only technology development but also data quality, organizational adaptability, and employee training. However, many companies often focus only on technology development costs when planning projects, and seriously ignore these hidden challenges, which ultimately leads to project failure.
Through fault tree analysis, it is found that the main reason for the failure of AI projects is rarely the technology itself:
As the “fuel” of AI, the quality of data directly affects the operation of AI models. If the data is missing, erroneous, or incomplete, the AI model cannot operate accurately or even draw incorrect conclusions. For example, a healthcare company plans to use AI technology to assist in disease diagnosis, hastily developing diagnostic models without sufficient cleaning and annotation of historical case data. As a result, due to the low quality of the data, there were a large number of misdiagnoses in the testing stage, and the project had to suspend rectification, resulting in a double waste of time and money.
The implementation of AI projects also requires the coordination of the organizational structure and business processes of the enterprise. If the organization is not adaptable, it is difficult to move forward smoothly. For example, a retail company introduced an AI intelligent warehouse management system, which needed to optimize the original warehousing process and require collaboration between various departments. However, due to the complex internal organizational structure of the enterprise, poor communication between departments, and problems such as delayed data transmission and unclear division of responsibilities during the implementation of the system, resulting in the system not being able to operate normally, and finally the project was forced to be shelved.
Additionally, employee acceptance and ability to use new technologies are crucial. Without effective training, employees may not be able to fully utilize AI tools. A manufacturing company blindly launched a full-process AI quality inspection project without adequate training for its employees. Due to the employees being unfamiliar with the operation process and technical principles of the new system, frequent operational errors occur in actual work, resulting in the quality inspection efficiency not rising but decreasing, and the project cannot achieve the expected results, and finally has to give up, resulting in a huge waste of resources. These cases show that ignoring the hidden challenges in the implementation process is one of the important reasons why enterprises fall into the “AI gimmick trap”. Enterprises must comprehensively consider the costs and challenges of implementing AI projects to ensure their successful implementation.
Case: The real cost composition of an AI quality inspection project in a manufacturing enterprise
3. Back to the essence: How to make AI truly create value?
3.1 Principle 1: Demand first, technology later
In the wave of AI technology applications, if enterprises want AI to truly create value, they must first follow the principle of “demand first, technology second”. This principle requires that when launching an AI project, enterprises must take their own business needs as the starting point, dig deep into business pain points, accurately locate problems, and then find suitable AI technologies to solve problems, rather than chasing technology hotspots before the horse.
The successful practice of a retail enterprise provides a vivid annotation of this principle. In the process of operation, the company faced the problem of serious inventory backlog and high unsalable rate, and a large amount of funds were occupied by unsalable goods, which seriously affected the cash flow and profitability of the enterprise.
When introducing AI technology, enterprises do not blindly follow the popular concept of “intelligent forecasting” in the market, but clearly put forward the specific goal of “reducing the unsalable rate” based on their own core needs. The business team works closely with data analysts to conduct a comprehensive and in-depth analysis of historical sales data, market trends, consumer purchasing preferences, and more.
Inventory forecasting models are built through AI algorithms, combined with sales cycles and changes in market demand, to achieve accurate replenishment. For example, when analyzing the sales data of a certain seasonal clothing, the AI model not only considers the sales of the same period in previous years, but also adjusts the procurement plan in advance by combining external factors such as fashion trends and weather forecasts of the year, effectively reducing the slow-selling rate and saving a lot of costs for the company.
Starting from the business pain points, in more than ten years of product management practice, I have summarized the “demand pyramid” model, combined with the “5Why analysis method” to continuously ask “why”, layer by layer, penetrating the surface of the problem, and finding the root cause hidden in the depths, so as to accurately distinguish between real and pseudo-demand, and help enterprises screen real needs layer by layer.
Taking a service-oriented enterprise as an example, it found that the customer churn rate showed an upward trend, and it was preliminarily judged that the customer service response speed was slow and caused customer dissatisfaction.
Use the “5Why analysis” to ask: Why is the customer service response speed slow? Because the number of customer service staff is insufficient; Why is there not enough customer service staff? The number of inquiries has increased significantly due to business growth; Why didn’t business growth replenish customer service staff in time? Because there is a lack of effective personnel planning mechanisms.
After continuous questioning, the company finally realized that the key to solving the problem of customer churn lies in establishing a scientific personnel planning mechanism, rather than simply improving the response speed of customer service. If enterprises do not adopt this method and blindly invest resources to upgrade the customer service system, it will not only fail to fundamentally solve the problem, but also cause waste of resources. The “demand pyramid” model, combined with the “5Why analysis method”, enables enterprises to accurately locate business pain points and lay a solid foundation for the planning and implementation of subsequent AI projects.
3.2 Principle 2: Start with the end and define measurable outcomes
When working on AI projects, “starting with the end and defining measurable outcomes” is key to ensuring project success. Many companies have misunderstandings in setting AI project goals, often focusing on technical realization, such as “deploying an AI customer service system” and “developing AI recommendation algorithms”, but ignoring the actual impact of the project on the business.
The right approach should be to set goals around business value and translate them into clear, quantifiable outcome metrics that drive project implementation. The evolution path of AI project target design is shown in the figure below.
Value indicator system design template:
Taking AI customer service systems as an example, the wrong goal setting only focuses on the launch of the system and does not consider the specific contribution of the system to the business after it goes live. Correct goal setting will clarify quantitative metrics, such as “reducing labor costs by 30% through AI customer service without decreasing customer satisfaction.”
Such goal setting not only clarifies the requirements of the project for cost control, but also takes into account the quality of service, and points out a clear direction for the implementation of the project. At the same time, specific quantitative indicators also facilitate enterprises to monitor and evaluate in real time during the project promotion process, detect deviations in a timely manner and take adjustment measures.
To understand this principle more intuitively, use the following table to compare mistakes with correct goal-setting:
Identifying measurable outcome metrics not only helps organizations assess the value of AI projects, but also motivates project teams to work around core goals, avoid deviating from the project during implementation, and ensure that every input translates into tangible business benefits.
3.3 Principle 3: Minimize validation (MVP thinking)
Adopting the Minimum Viable Product (MVP) mindset is an effective strategy for enterprises to reduce the risk of AI projects and increase the success rate. The core of MVP thinking lies in the four steps of small-scale pilot, verification of effect, iterative optimization, and large-scale promotion, to quickly verify the feasibility of AI solutions with minimal cost and resource investment, and reduce losses caused by project failure.
MVP selection criteria:
- High business value density
- Data is highly available
- The amount of process transformation is small
- The cost of failure is controllable
Analysis of successful cases: a smart ordering project of a catering enterprise
In the small-scale pilot phase, enterprises choose a relatively small business scenario or department and apply AI solutions for experimentation. For example, a catering company plans to introduce an AI smart ordering system, and in order to reduce risks, the company will first pilot it in one of its stores. During the pilot process, enterprises pay close attention to the operation of the system and collect data such as customer feedback, waiter operation experience, and the impact of the system on ordering efficiency and order accuracy. Through the pilot, the company found that there were logical loopholes in the system’s dish recommendation function, resulting in a mismatch between the recommended dishes and customer needs, and some elderly customers found it difficult to operate the system.
Based on the problems found in the pilot, the enterprise entered the stage of verification effect and iterative optimization. In response to the logic problem of dish recommendation, the technical team readjusted the algorithm and made personalized recommendations based on customer historical ordering data and current popular dishes. For the problem of inconvenient operation, the design team simplified the interface and added a voice ordering function to facilitate the use of elderly customers. After multiple iterations and optimizations, the system has significantly improved its effectiveness in pilot stores, increased ordering efficiency by 40%, and increased customer satisfaction.
When the verification effect meets expectations, the company enters the large-scale promotion stage and promotes the optimized AI intelligent ordering system to other stores. This step-by-step approach avoids the risks that enterprises may face when investing resources on a large scale at one time.
Analysis of failure cases: An AI quality inspection project in a manufacturing industry
The company blindly launched a full-process AI quality inspection project without sufficient pilot and data preparation. Due to the lack of sufficient quality inspection data for model training, the accuracy of AI quality inspection systems is extremely low and cannot meet actual production needs. In the end, the project had to be shelved, resulting in a huge waste of resources. If the company adopts an MVP mindset, conducting a small-scale pilot on a production line first, optimizing the plan based on the pilot results, and then gradually promoting it, such failures may be avoided.
The comparison of positive and negative cases fully proves that MVP thinking can help enterprises avoid detours in AI projects, improve resource utilization efficiency, and achieve a smooth transition from successful pilot to full-scale application.
3.4 Principle 4: Technological adaptability > technological advancement
When choosing AI technology solutions, enterprises must abandon the concept of “technologically advanced theory” and prioritize technology adaptability. There are significant differences in suitable AI technology solutions for enterprises of different sizes and different business characteristics. For small and medium-sized enterprises, although complex large models are technologically advanced, they may face problems such as insufficient data, high computing power costs, and difficult maintenance.
In contrast, the combination of “rules engine + RPA (robotic process automation)” may be better suited to its practical needs. Although this technology combination does not have the “cool” appearance of large models, it can quickly automate business processes, solve the repetitive and regular work problems of enterprises, and has relatively low requirements for data and computing power, and the implementation cost and risk are smaller. The decision tree of technology selection is shown in the figure below.
For example, when a small e-commerce company handles order review, logistics information tracking, and other businesses, it uses RPA technology to automatically capture order data, check information, and update logistics status, combined with a simple rule engine to handle common exceptions, which greatly improves work efficiency and reduces manual errors. If the company blindly pursues large model technology, it not only needs to invest a lot of resources in building data centers and computing power facilities, but also needs to set up a professional AI team for model training and maintenance, which is undoubtedly an unbearable burden for small and medium-sized enterprises with limited resources.
Cost-benefit comparison table:
When evaluating technical solutions, enterprises can solve problems by asking “Can problems be solved without AI?” This is a key issue to avoid technical over-design. If traditional methods can effectively solve problems, enterprises should prioritize traditional solutions to reduce costs and risks. Only when traditional methods cannot meet business needs should AI technology be considered, and when choosing AI technology, it is necessary to fully evaluate the adaptability of the technology to enterprise business processes, data foundation, personnel capabilities, etc.
For example, a company initially considered introducing AI image recognition and natural language processing technology when handling document classification, but after evaluation, it found that it could meet current business needs by establishing simple keyword matching rules and manual-assisted review, so it abandoned the AI solution and saved unnecessary investment. Only by adhering to the principle of prioritizing technology adaptability can enterprises choose AI technologies that truly suit them and maximize the value of AI.
Value realization checklist:
- Whether a mapping relationship between the business problem and the technical solution has been established
- Whether the value indicator is quantifiable and trackable
- Whether the MVP path is clear and feasible
- Whether the technical solution matches the current situation of the enterprise
The essence of AI value realization is to establish a golden triangle of “business-technology-data”. Product managers need to be like architects, drawing big blueprints and ensuring that every structural component can withstand the load.
4. Practical framework for enterprises to implement AI
4.1 Top-level design: Build a symbiotic relationship between AI and strategy
Incorporate AI into your strategy, but make it clear that it’s a “tool” rather than a “goal.”
As the wave of digitalization swept the world, corporate strategic planning can no longer avoid the deep involvement of AI technology. However, many companies fall into the misunderstanding of AI application, regarding AI itself as the ultimate goal of enterprise development, blindly pursuing the advancement of technology while ignoring its adaptability to core business.
Businesses must be soberly aware that AI is inherently a powerful tool for achieving business goals, not an end in itself. When formulating strategies, it is necessary to start from the long-term development of the enterprise and the construction of core competitiveness, and accurately locate the application areas and key projects of AI.
In the process of serving the digital transformation of many enterprises, I have summarized the top-level design framework of the “strategy-business-technology” trinity:
In the strategic planning layer, enterprises should avoid falling into the trap of “technology first”. For example, a retail company not only applies AI technology to customer demand forecasting and inventory optimization to improve supply chain efficiency in its strategic planning, but also increases investment in brand building and customer service. This fully shows that AI can only truly exert its value and promote continuous progress if it develops in tandem with other key elements of the enterprise.
Cross-analyze your own business segments and AI technology capabilities in the business layer. Sort out the core business processes of the enterprise, such as R&D, production, sales, service, etc. Continuing to take a retail company as an example, by creating a unique brand culture and high-quality offline shopping experience, it has formed a comprehensive competitiveness that combines online AI-driven precision operation and offline personalized services, so that the company can stand out in the fierce market competition.
In the technical layer, sort out AI technical capabilities, including machine learning, natural language processing, computer vision, etc. By evaluating the suitability, potential value, and difficulty of implementation of AI technology in each business segment, the highest priority AI application scenarios are identified. For example, a manufacturing company used this matrix analysis to identify AI technology as a key item in its AI strategy when it found that applying AI technology to predictive maintenance of production equipment could leverage existing equipment data while significantly reducing downtime.
4.2 Organizational adaptation: Break down the invisible barriers to AI implementation
(1) Set up the role of “Business – AI Translator”
In the process of advancing enterprise AI projects, there is often a huge communication gap between technical teams and business departments. Technicians focus on algorithm optimization and model building, using professional technical terms; Business personnel pay more attention to business goals and actual results, and lack understanding of technical details. This communication barrier has led to many AI projects not being able to accurately meet business needs and ultimately being difficult to implement. The role of “Business – AI Translator” was created to break down this barrier and build a bridge between technology and business.
Refined from more than 20 successful cases, the capability model of “Business-AI Translator” is shown in the figure below.
“Business – AI Translator” needs to be dual-functional. On the one hand, it is necessary to deeply understand the business logic, processes and pain points of the enterprise, and be familiar with market demand and customer expectations; On the other hand, it is necessary to master the basic principles, application scenarios and implementation methods of AI technology. In the early stage of an AI project, the “Business – AI Translator” must communicate deeply with the business department to fully understand the business needs through interviews, seminars, etc. Taking a company’s AI precision marketing project as an example, the “Business – AI Translator” communicated with the sales department multiple times and learned that the sales team wanted to use AI to analyze customer historical purchase data, browsing behavior, and other information, predict customer purchase intentions, and achieve accurate push.
The Business – AI Translator then translates these business requirements into technical requirements documents that the technical team can understand, detailing data sources, analysis objectives, output results, and engaging in technical solution discussions. During the technical solution development process, the “Business-AI Translator” makes recommendations from a business perspective to ensure that the technical solution is not only technically feasible but also can effectively solve business problems. During the project implementation process, the “Business – AI Translator” continued to follow up, timely feedback on new needs and problems of the business department, and coordinated the technical team to adjust and optimize to ensure the smooth progress of the project and achieve the expected results.
The typical workflow for Business – AI Translator is shown in the following image.
(2) Cultivate AI literacy in business departments
In enterprise AI applications, if only the technical team is led, it is easy to “work behind closed doors”, resulting in AI projects being disconnected from the actual business and unable to generate real value. Therefore, building AI literacy in business units is crucial. By improving business personnel’s understanding and application capabilities of AI, they can be prompted to more accurately put forward requirements, actively participate in project planning and evaluation, and form efficient collaboration with technical teams.
Enterprises can build a multi-level and systematic AI training system. First, basic theoretical training is carried out, and experts in the field of AI are invited to explain the basic concepts, technical principles, and development trends of AI to business personnel, helping them establish a correct understanding of AI. Secondly, organize industry application case sharing sessions, select successful AI application cases related to enterprise business for in-depth analysis, demonstrate specific methods and significant effects of AI in solving real business problems, and stimulate business personnel’s interest and inspiration in AI applications.
For example, a company invited representatives of enterprises in the same industry to use AI to automate customer service and significantly reduce customer service costs, so that business personnel can intuitively feel the value of AI.
In addition to theoretical training and case sharing, companies should also encourage business personnel to participate in the practice of AI projects. By setting up an internal AI practice project, business personnel and technical teams form cross-departmental teams to participate in project requirements analysis, solution design, and testing and verification.
In the practical process, business personnel can personally experience the application process of AI technology, deepen their understanding of AI, and provide valuable suggestions for projects from a business perspective, improving the practicality and effectiveness of the project. In addition, enterprises can also establish internal learning communities to facilitate business personnel to exchange AI learning experiences and application experiences at any time, creating a good learning atmosphere.
4.3 Resource Allocation: Follow the golden rules of AI investment
Many enterprises have serious cognitive biases in investing resources in AI projects, focusing a lot of resources on model development while ignoring data governance and process transformation. In fact, data is the “fuel” of AI models, and without high-quality data, no good model can function.
At the same time, the application of AI technology often requires optimizing and reshaping the existing business processes of enterprises to adapt to the changes brought about by new technologies. Therefore, enterprises should invest 80% of their resources in data governance and process transformation to lay a solid foundation for the effective operation of AI models. The data governance implementation framework is shown in the following figure.
Data governance is a systematic project that covers data standard formulation, data quality control, data security protection and other aspects. Enterprises must first formulate unified data standards, including data formats, coding rules, metadata definitions, etc., to ensure that data between different departments and systems can be compatible and shared with each other.
Secondly, establish a data quality monitoring mechanism to find and correct errors, omissions, and inconsistencies in data in a timely manner through data cleaning, data verification, and other means, so as to ensure the accuracy, integrity, and consistency of data.
For example, a financial company invested a lot of resources to clean and integrate customer transaction data, credit data, etc., improving data quality by 30%, providing reliable data support for subsequent risk assessment and precision marketing models.
In terms of process transformation, enterprises need to comprehensively sort out existing business processes and analyze which links can be optimized and automated through AI technology. The key points of process transformation are shown in the figure below.
Taking a logistics company as an example, when it introduced AI technology to optimize transportation routes, it not only developed an intelligent route planning model, but also redesigned the entire logistics scheduling process. By integrating the data of the order management system, vehicle monitoring system and warehouse management system, the whole process from order receipt, vehicle scheduling to goods distribution is automated, which greatly improves logistics efficiency and reduces operating costs. In the process of process transformation, enterprises should pay attention to communication and training with employees to ensure that employees can understand and adapt to the new workflow and ensure the smooth implementation of process transformation.
4.4 Cultural Shaping: Cultivate the soil of AI valueism
The culture of a company can have a profound impact on the success of AI applications. If companies blindly pursue the cool effects of AI technology, encourage demonstrations of “showing off their skills with AI”, and ignore the creation of actual business value, then employees will focus on pursuing the superficial luxury of technology rather than actually solving business problems.
Therefore, enterprises should actively create a cultural atmosphere conducive to AI applications, guide employees to establish correct AI application concepts, and return the value of AI to creating practical benefits for enterprises.
Enterprises can recognize and reward teams and individuals who truly use AI technology to solve business problems and improve work efficiency by establishing a comprehensive incentive mechanism. The design principle of the incentive mechanism is shown in the figure below.
For example, the “AI Innovation Application Award” has been established to give material rewards to project teams that have achieved remarkable results such as business growth, cost reduction, and service quality improvement through AI technology, such as bonuses and promotion opportunities, and at the same time award honorary certificates to publicize and commend within the company.
After an Internet company established the award, employees actively explored the application of AI in product recommendation, user growth, etc., and several successful cases emerged, including one team using AI to optimize product recommendation algorithms, increasing product click-through rates by 25%, bringing considerable benefits to the company.
In addition to material rewards, companies should also pay attention to spiritual motivation, publicize successful AI application cases and the experience of excellent teams through internal meetings, internal magazines, social media, and other channels, set an example, and inspire other employees to be motivated and creative in AI applications. The roadmap for cultural transformation is shown in the figure below.
Key interventions:
- Leadership monthly AI value review meeting
- Set up a “red and black list” for AI applications
- Establish a cross-departmental AI community
- Regularly hold “AI Value Discovery” workshops
At the same time, enterprise leaders should lead by example, emphasize the practical value of AI applications in their daily work, and guide employees to closely integrate AI technology with business needs, forming a good cultural atmosphere of “using AI to improve efficiency”. In addition, enterprises can also organize AI application experience sharing meetings, innovation competitions and other activities to provide employees with a platform for communication and display, and further promote the widespread application and in-depth development of AI within enterprises.
5. Reflection: The Law of Survival in the AI Era
5.1 In-depth interpretation of the ultimate proposition
After serving more than 100 enterprises in digital transformation, I have deeply realized that the more advanced the technology, the more critical it is to grasp the essence of the problem. This cognition can be embodied through the “problem-technology” matrix.
At a time when AI technology is rapidly advancing, enterprises are like being in a grand wave of technological change. From intelligent customer service to autonomous driving, from personalized recommendations to intelligent manufacturing, the application scenarios of AI technology seem to be infinitely expanding, however, many companies are gradually losing their way in the process of chasing AI hotspots.
At this point, we must return to the essence and confront that crucial ultimate proposition: “AI is the answer, but what’s the problem?” This question directly points to the core logic of enterprises applying AI technology, and also reveals the survival rules of enterprises in the AI era.
AI technology undoubtedly has great potential, and its deep learning algorithms can process massive amounts of data and uncover laws that are difficult for humans to detect; natural language processing technology makes human-computer interaction smoother and more natural; Computer vision technology has shown amazing capabilities in the fields of image recognition, video analysis, etc. However, if these technical capabilities cannot be combined with actual business scenarios and cannot solve specific problems in enterprise operations, then no matter how advanced the technology is, it is only a castle in the air.
Taking an educational technology company as an example, it spent a lot of money to develop an AI intelligent teaching system, although it technically realized the intelligent recommendation of course content and automatic tracking of learning progress, but due to the failure to accurately grasp the personalized learning needs of students and ignore the leading role of teachers in the teaching process, the system experience was poor, and finally failed to be recognized by the market. This fully shows that AI applications that are detached from practical problems, no matter how advanced the technology, are difficult to create value.
5.2 Rediscovery of value anchors
The essence of enterprise survival, no matter what era it is in, is always to solve user problems and create sustainable value. In the industrial age, enterprises reduce costs through mass production to meet consumer demand for standardized products; In the Internet era, enterprises use digital technology to break down information barriers and provide convenient online services. In the era of AI, this essence has not changed, but only a tool and method of solving problems.
Businesses need to be aware that AI technology is just a means, not an end. Taking the retail industry as an example, enterprises apply AI technology not simply to show the coolness of the technology, but to analyze consumer purchasing behavior data, accurately predict demand, optimize inventory management, reduce operating costs, and provide consumers with a more personalized shopping experience, thereby improving user satisfaction and loyalty, and ultimately achieving profit growth for enterprises. Only by closely following this essence can enterprises find a real development direction with the blessing of AI technology. The comparative analysis of typical cases is shown in the figure below.
5.3 Practical wisdom to dispel myths
At present, many companies have fallen into the misunderstanding of the “AI myth”. Some companies blindly follow the trend when they see their peers introduce AI technology, and hastily carry out AI projects without in-depth analysis of their own business needs. These companies often focus too much on the advanced nature of the technology itself, keen to showcase various AI concepts and models, but ignore the practical application effect.
For example, in order to demonstrate their “sense of science and technology”, some traditional manufacturing enterprises have invested a lot of resources in the research and development of AI quality inspection systems before the production process has been basically digitized. This “AI gimmick” not only fails to bring practical benefits to enterprises, but may also distract their energy and resources, hindering their normal development.
To overcome the misconception of “AI myth”, companies must maintain a rational and pragmatic attitude. In the process of guiding enterprises to implement AI projects, I summarized the “five no’s principles”.
Before launching an AI project, enterprises should conduct sufficient market research and internal assessments, deeply analyze their business pain points and needs, and identify the specific problems that AI technology can solve.
For example, when a catering company was considering introducing AI technology, it was found through research that customers were waiting in line for too long, which was a key issue affecting the consumer experience. Therefore, based on this demand, the company has developed an AI intelligent queuing and calling system, and combined it with data analysis to optimize the dish serving process, effectively shortening customer waiting time and improving customer satisfaction and turnover rate. At the same time, enterprises should pay attention to step-by-step progress in the process of promoting AI projects and avoid being greedy for perfection. You can first choose some scenarios with relatively simple business processes and good data foundation for piloting, and then gradually promote them after accumulating experience.
5.4 Do-it-based framework of action
Enterprises should also establish a scientific evaluation system to continuously monitor and evaluate the input-output ratio of AI projects. By setting clear quantitative indicators, such as cost reduction rates, efficiency improvements, and sales growth ratios, the effectiveness of AI projects is regularly evaluated, and problems are identified and strategies are adjusted in a timely manner. The issue authenticity evaluation matrix is shown in the table below.
Taking an e-commerce company as an example, after applying AI technology to optimize the product recommendation system, it continuously adjusted the algorithm model by monitoring user click-through rates, conversion rates and other indicators, and finally increased the conversion rate of the recommendation system by 40%, bringing significant economic benefits to the enterprise.
In the AI era, the survival rule of enterprises lies in returning to the essence of value creation, maintaining a rational and pragmatic attitude, and deeply integrating AI technology with actual business needs. Only in this way can enterprises gain a firm foothold in the fierce market competition, achieve sustainable development, truly grasp the opportunities brought by the AI era, and transform technological potential into tangible business value.
The practical guidelines for continuous improvement are shown in the table below.
7. Summary: Return to the essence and let AI empower enterprises to develop with high quality
After observing the whole process of AI applications from fanaticism to rationality, we have finally reached the end of this thinking journey. Looking back on the past, those corporate cases that have risen and fallen in the wave of digital transformation all confirm a simple truth: the aura of technology will eventually fade, and only value creation will last forever.
The classic assertion of management master Peter Drucker that “efficiency is to do things right, and efficiency is to do things right” has pointed out the direction for the development of enterprises in the AI era. At a time when AI technology is booming, many companies blindly pursue the advancement of technology, falling into the misunderstanding of “AI for AI’s sake”, but ignore the essence of technology application – creating real benefits.
In fact, the value of AI is not reflected in the complexity of the technology itself, but in whether it can actually help enterprises “do the right thing” to meet market demand and achieve sustainable development. “doing things right” at the efficiency level corresponds to the correct implementation of AI technology; The “doing the right thing” at the benefit level directly refers to the value creation of the essence of business. The combination of the two is exactly the “value-oriented AI application” that this article has always emphasized.
From a strategic planning perspective, organizations must view AI as a tool to achieve business goals, not as an ultimate. In actual operations, some companies have deeply integrated AI applications with enterprise strategies and achieved remarkable results.
For example, a large manufacturing company fully considers its own shortcomings in supply chain management when formulating its strategy, and accurately positions AI technology as a means to optimize the supply chain. Intelligent planning of raw material procurement, production scheduling, logistics and distribution through AI algorithms not only reduces inventory costs, but also improves order delivery efficiency, truly doing the right thing. This confirms that only by starting from the core business needs of enterprises and clarifying the application positioning of AI can we avoid wasting resources and maximize the value of AI.
Resource allocation is a critical part of the success or failure of an AI project. Enterprises need to abandon the misconception of “emphasizing model development over data governance and process transformation” and invest 80% of their resources in data governance and process optimization. For example, when developing an AI credit risk assessment model, the institution did not rush to build a complex algorithm model, but first invested a lot of manpower and funds to clean, integrate, and label customer data, and reconstructed the credit approval process. The optimized data provides a solid foundation for model training, and the new approval process is also a perfect fit for the AI system, ultimately achieving the goal of improving the accuracy of risk assessment and doubling the approval efficiency. This fully proves that only by consolidating the data foundation and optimizing business processes can AI models exert their due effectiveness and create real benefits for enterprises.
Organizational adaptation is an important support to ensure the smooth implementation of AI projects. The establishment of the “Business – AI Translator” role and the cultivation of AI literacy in business departments effectively solve the problem of disconnection between technology and business. In an AI recommendation system project of an e-commerce company, “Business – AI Translator” accurately transformed the business department’s needs to improve user conversion rates into development tasks that the technical team could perform, and at the same time, through AI knowledge training for business personnel, they were able to participate in the project’s requirements review and effect evaluation. The two sides worked closely together to create a recommendation system that significantly increased the click-through rate of products and significantly increased user retention. This organizational optimization allows AI technology to truly serve the business and realize the two-way empowerment of technology and business.
Cultural shaping creates a good ecological environment for AI applications. Enterprises guide employees to establish a correct concept of AI application by rewarding cases of “improving efficiency with AI”. After an Internet company set up the “AI Innovation Application Award”, employees no longer pursued the flashy display of technology, but focused on solving practical business problems. Among them, the customer service department used the AI intelligent customer service system to successfully improve the efficiency of solving common problems by 60%, which not only reduced labor costs, but also improved user satisfaction, and the project was also commended by the enterprise. The formation of this cultural atmosphere allows AI applications to return to the essence of value creation, stimulating the enthusiasm and creativity of all employees in enterprises to apply AI.
In the era of AI, if enterprises want to achieve high-quality development, they must return to the essence of demand and get out of the “gimmick trap”. Through scientific top-level design, reasonable resource allocation, effective organizational adaptation, and positive culture shaping, AI technology is deeply integrated with enterprise business, making AI truly a powerful tool to enhance enterprise competitiveness.
In this era of rapid technological change, let us remember: no matter how powerful AI is, it is only a tool, and the meaning of enterprises is always to create real value for mankind. This is both the starting point of business and the destination of all technology applications. Only in this way can enterprises gain a firm foothold in the wave of digitalization, stand out in the fierce market competition, achieve a leap from efficiency improvement to efficiency growth, and write a new chapter of their own development.
May every manager become a master of technology, not a follower of trends, and illuminate the road to the realization of AI value with the light of wisdom.
