Abstract
Many companies have adopted AI, but most projects ultimately become mere decorations: money spent, yet results are unsatisfactory. The root of the problem lies in the fact that large models can “speak” but cannot “act”; they can only generate reports and provide suggestions, but cannot truly drive process closure. What really enables AI to run effectively is the combination of large models and AI Agents: the former is responsible for understanding and judgment, while the latter is responsible for breaking down tasks and execution, forming a complete loop from input to output. Scenarios such as approval, quality inspection, and customer service have already proven that this combination can increase efficiency several times and reduce costs by half. Large models are the trend, but AI Agents are the lifesaver, determining the survival of enterprises in the next three to five years.
Origin
Recently, I have talked with many entrepreneurs about AI and found a commonality: everyone has adopted some AI, but they all feel it is not useful.
Some bosses spent hundreds of thousands on large model APIs, yet employees still manually create reports; some companies set up intelligent customer service, only to find that human intervention is still necessary; others simply hired an “AI consulting company” to build a platform, but after six months, data has not accumulated, business has not improved, and instead, they have ended up with a bunch of bugs.
In short – money spent, pitfalls encountered, and the result is zero.
Where does the problem lie?
Why Do Most Enterprise AI Projects Fail to Implement?
In recent years, AI has been very popular, with ChatGPT, Wenxin Yiyan, Claude, and various large models emerging. Business owners hear myths: they can write plans, perform analyses, and replace employees.
But when it comes to actual use, problems arise one after another:
Large models can “speak” but cannot “act”. They can write plans for you, but they cannot perform cross-system operations like approval, ordering, and scheduling. Ultimately, manual execution is still required.
Broken loops: ERP, CRM, OA, MES, various systems do not communicate with each other. AI can do some tasks within one system, but once the process crosses departments, it fails.
Insufficient accuracy: Financial approvals, manufacturing quality inspections, and medical diagnoses require nearly 100% accuracy. Large models occasionally make mistakes, and once applied in practice, it becomes a disaster.
Poor reusability: Changing a business scenario requires retraining and re-adapting, which is prohibitively expensive.
Therefore, you will see this reality: large models are powerful, but enterprises cannot truly improve efficiency relying on them.
Where is the Solution? The Key Combination is Large Models + AI Agents
Many bosses still do not realize that the secret to making AI truly operational is not the “universal theory of large models”, but rather the combination of large models and intelligent agents (AI Agents).
Large models are responsible for thinking: understanding the business, generating plans, and providing judgments.
AI Agents are responsible for doing: breaking down tasks, executing across systems, and automatically closing the loop.
Only when both are combined can we achieve true “cognition + execution” integration.
To put it metaphorically:
Large models are like a smart advisor, providing analysis and suggestions.
AI Agents are like a capable secretary, who not only can write but also can run errands and implement tasks.
With this combination, enterprises can truly have peace of mind.
Sounds Abstract? Let’s Look at Some Real Scenarios
Scenario 1: Financial Approval
In the past: A loan approval required running through several departments, manual reconciliation, and human signatures, taking at least two weeks. Now: The large model generates a risk control analysis report, and the Agent directly coordinates across departments, verifying information, automatically submitting, and reminding responsible persons to sign. Result: Approval efficiency increased by 3-5 times, and error rates decreased by an order of magnitude.
Scenario 2: Manufacturing Quality Inspection
In the past: Quality inspectors performed manual sampling, which was time-consuming and error-prone. Now: The large model analyzes historical data to predict risks, and the Agent directly interfaces with the production line, automatically scheduling inspection processes. Result: The yield rate increased by 10%-20%, and downtime was significantly reduced.
Scenario 3: E-commerce Customer Service
In the past: Customer inquiries were chaotic, and human customer service was overwhelmed, often leading to breakdowns in after-sales processes. Now: The large model writes communication scripts, and the Agent automatically handles orders, logistics inquiries, and after-sales follow-ups. Result: Response speed doubled, and labor costs halved.
Do you see it? The large model is the brain, and the Agent is the hands and feet. Having only the brain, enterprises cannot move; with hands and feet, they can truly implement in business.
What Do Entrepreneurs Really Care About?
Don’t talk technology to the boss; no one cares how big your model is or how many parameters it has. Entrepreneurs focus on three things:
Can costs be reduced? Can labor be halved? Can repetitive tasks be automated?
Can efficiency be improved? Can approvals be expedited by 3 times? Can production lines have less downtime? Can customers be responded to in seconds?
Can risks be controlled? Is the accuracy sufficient? Can compliance be guaranteed? Can domestic requirements be met?
These three points are the hard assessments for AI implementation. Everything else is superficial.
How Should Enterprises Get Started?
If you are a business leader and want AI to truly run, you can follow three steps:
Step 1: Select high-value processes. Don’t be greedy or spread too thin. Start with a cross-departmental, repetitive, and impactful process as a pilot. For example, approval, quality inspection, or customer service.
Step 2: Collaborate with large models + Agents. Large models are responsible for cognition and judgment, while Agents are responsible for breaking down and executing tasks, forming a closed loop.
Step 3: Quantify assessments. Don’t just shout slogans; directly use KPIs to verify: task completion rate, average process time, accuracy, and business output. Only when you can see numerical changes can it be considered truly implemented.
Who Will Win in the Next 3-5 Years?
In the coming years, the competition in enterprise AI will not be about “who bought the large model”, but rather “who first implements the large model + Agent”.
Companies that know how to use AI: double the human efficiency, reduce costs, and skyrocket competitiveness.
Companies that only play with concepts: money spent, employees still working overtime, and ultimately eliminated.
In short, large models are the “trend”, but AI Agents are the “implementation”. Only by effectively combining the two can enterprises find their true lifesaver.
This is not science fiction, nor is it a deception.
Financial approvals can be expedited by 3-5 times;
Manufacturing quality inspections can increase yield rates by 10%-20%;
E-commerce customer service can halve labor costs;
These are all real occurrences.
If you are an entrepreneur still hesitating whether to try AI, I just want to remind you: large models are just the starting point, AI Agents are the endgame.