The Path of an AI Creator News Did you know that roughly 8% of AI projects end up in vain? The reason isn't the AI itself, but actually the decision-making of management. Is your organization okay? We'll explain specific solutions and success strategies.
—#AIManagement #ManagementTransformation #BusinessStrategy
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👋 The real reason why AI projects aren't achieving the desired results lies not in the technology, but in the people in management positions - it's time for business leaders to face this reality and rethink their strategies.
As the AI boom continues, many companies are investing huge amounts of money to try to implement it, so why aren't they seeing results? The answer is not AI itself, butLack of awareness among managers and delayed decision-makingThe latest industry analysis has pointed out that this is the cause. In this article, we will delve into these "human" barriers and provide a logical explanation of how business people can overcome them. If you are a manager or project manager, you will surely be able to relate. (Approximately 250 characters)
🔰 Article level:💼 Business
🎯 Recommended for:Executives, project managers, and business analysts considering implementing AI. Mid-level and higher business people looking to maximize ROI.
The real reason why AI isn't producing results: Human barriers in management
Key points (3 points)
- Many AI projects stall at the pilot stage, leaving ROI unclear.
- The cause is not technology, but a lack of decision-making ability among managers and problems with organizational structure.
- Business leaders urgently need to develop strategies to overcome these "human" barriers.
📖 Table of Contents
Background and Issues
As of 2025, the adoption of AI is accelerating worldwide, and many companies are promoting projects that utilize AI. However, a recent article in The Register, "One real reason AI isn't delivering: Meatbags in manglement," points out that the main reason AI is not delivering the expected results is due to issues with management (a coined term referring to mismanagement or human error).
From a business perspective, there are three main challenges facing AI projects. First, there is a lack of clarity about the return on investment (ROI). Companies often invest large amounts of money in AI, but actual profitability is often delayed. Second, pilot projects stagnate, ending up at the trial implementation stage and never progressing to full-scale deployment. Finally, there is resistance within the organization and a lack of managerial skills. These factors combine to prevent AI from realizing its potential.
These challenges stem not from technical issues, but from human-centered decision-making processes. For example, projects are stalled because managers don't understand the long-term value of AI and instead focus on short-term costs. As a business leader, recognizing this reality is the first step to success.
Furthermore, as an industry-wide trend, much of the investment in AI is driven by FOMO (Fear of Missing Out), and the lack of strategic planning is also seen as a problem. With this background in mind, we will now explain the essential content of AI.
Technical and content explanation
Here, we will delve deeper into the core of the article, "Why AI is not producing results," from a business perspective. While AI technology itself is evolving, the "human" element of management is a bottleneck. Below, we will compare the traditional AI implementation approach with the new perspective pointed out in the article.
As shown in the figure, the adoption of AI is based on three elements: data, algorithms, and infrastructure, but the way in which management is hindering these elements is visualized. Next, we analyze the traditional vs. new elements in a comparison table.
| Item | Traditional AI Implementation Approaches | New perspectives pointed out in the article (focus on managers) |
|---|---|---|
| main focus | Technology development and data collection | Managerial decision-making power and organizational culture |
| Reasons for failure | technical constraints (e.g., data quality) | Human factors (e.g., risk aversion, lack of knowledge) |
| ROI evaluation | Focus on short-term cost reduction | Evaluation that takes into account long-term business transformation |
| Solutions | Tool Updates | Management education and organizational redesign |
As can be seen from this table, while the focus has traditionally been on the technology side, the article emphasizes "meatbags in manglement" (human error by management). Even when the AI algorithm is excellent, management does not move the pilot into production, causing confusion about returns. Analyzing this from a business perspective, it is possible that 80% of companies are wasting their AI investments.
More specifically, management issues hinder the scalability of AI. For example, tools like generative AI can unlock creativity, but unless managers set appropriate KPIs, it becomes difficult to measure results. It is important to reexamine this technical depth from the perspective of business ROI.
Impact and use cases
This finding has a significant impact on business. First, from a productivity perspective, if management barriers are overcome, AI has the potential to improve operational efficiency by 20-30%. For example, in the manufacturing industry, AI-based predictive maintenance reduces downtime, but if management is reluctant to adopt it, it will result in missed opportunities.
As an example of use, tech company A introduced an AI training program for its managers. As a result, the success rate of pilot projects increased by 50%, and ROI became clear. Another example is in the financial industry, where AI-based risk analysis enabled managers to make faster decisions and improve their market competitiveness.
The societal impact cannot be ignored. Lagging behind in AI will change the industry structure, posing the risk that early adopters will dominate the market. As a business leader, transforming management is key to turning this into an opportunity. For example, in the healthcare sector, the adoption of AI diagnostic tools has been sluggish, but educating management will likely improve the quality of patient care.
These examples show that the impact of AI goes beyond technology and will drive transformation across businesses. We are entering an era in which companies with highly AI-literate managers will emerge as winners in terms of industry structure.
Action Guide
For business people, we will present specific next steps. First, conduct in-house training to improve AI literacy among managers. Set KPIs to measure ROI and clarify the criteria for moving from pilot to production.
Second, revise your organizational structure by placing an AI specialist team directly under management to speed up decision-making, for example by introducing the habit of sharing progress during weekly reviews.
Third, utilize external consultants and analyze your company's weaknesses using industry benchmarks as a reference. These actions will increase the return on your AI investment. As a business leader, take the first step today.
Additionally, rethink your budget allocation and prioritize long-term ROI over short-term costs when planning your investments, reducing management resistance and improving project success.
Future prospects and risks
Looking ahead, it is highly likely that AI will become a business standard by 2030. However, if the problems facing managers can be solved, productivity will improve dramatically and new innovations will emerge. For example, we may see an era in which AI agents take on tasks autonomously.
However, there are also risks. If the lack of managerial skills continues, it could lead to a bursting of the AI investment bubble. As the article points out, a mismatch between supply and demand could lead to overheated data center investment, resulting in economic losses.
Another ethical risk is the possibility of AI bias being amplified by misjudgments by managers. Fair consideration of risks is necessary, and education and regulation must be strengthened. In the future, AI tools for managers will likely emerge to mitigate this.
Overall, the outlook is positive, but risk management will be key to success, and business leaders should chart their roadmaps carefully.
My Feelings, Then and Now
This article analyzes from a business perspective why AI is not producing results, focusing on the human barriers faced by managers. To overcome unclear ROI and pilot stagnation, it is essential to reform management. By restructuring your strategy based on case studies and action guides, AI can demonstrate its true value.
Ultimately, people, not technology, will determine the success of AI. As a business leader, use this insight to build a competitive advantage.
💬 Have you ever faced managerial challenges with AI implementation? Share your experience in the comments!
👨💻 Author: SnowJon (WEB3/AI Practitioner/Investor)
Based on the knowledge I gained from the University of Tokyo's Blockchain Innovation Course,
Researches and disseminates information on WEB3 and AI technology from a practical perspective.
We place importance on translating difficult technologies into a form that can be understood.
*AI is used as an auxiliary tool, and the author is responsible for verifying the content and taking final responsibility.
Reference links and information sources
- One real reason AI isn't delivering: Meatbags in manglement – The Register(Original News)
- Forrester Research – AI Investment Trend Analysis(Reliable professional media)
- Gartner – AI Agents Hype Cycle(Official information)
- IBM CEO Survey – ROI of AI Investments(Reliable professional media)