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The concept of AI middle platform is hot, but can it really bring changes to enterprises?
Core content:
1. The rise of AI middle platform and the decline of data middle platform
2. The definition, architecture and functions of AI middle platform
3. The challenges and difficulties faced by the implementation of AI middle platform
Yang Fangxian
Founder of 53A/Most Valuable Expert of Tencent Cloud (TVP)
Recently, a reader sent me an article explaining the "AI middle station" and asked me what I thought about the AI middle station?I answered directly: I have no idea, but this year many companies have launched AI middle platforms, and the popularity is continuing to rise.I wasn't just answering him perfunctorily, but giving him a true answer.I have expressed this many times in my previous articles: almost all innovative and monopolistic companies in the world are pursuing AI without any prior agreement.Even traditional third-party software outsourcing companies have begun to talk about AI.I understand. After all, it is human nature to pursue profit in order to survive.But I really don’t understand what results or achievements these companies can achieve with AI.Back to the AI middle platform, I naturally pay attention to this "old concept", which is not a new concept.In the past, when the development of data middle platforms was in full swing, some companies engaged in traditional machine learning algorithm training and management claimed that their companies were engaged in AI middle platforms.But at that time it was obviously just to take advantage of the popularity of the data middle platform.In just a few years, things really change!Nowadays, AI has become a global hot topic, and AI middleware has naturally come into everyone's attention, and even become the client's number one project.However, the data middle platform has been gradually downgraded by enterprises due to problems such as high cost, difficult maintenance, long value output chain and difficulty in measurement.After all, everyone believes that if we don’t seize this wave of AI dividends, it may be gone.In today’s article, I want to express another point of view: purchasing a data middle platform does not mean that the enterprise has undergone digital transformation. Similarly, purchasing an AI middle platform is still a long way from an enterprise realizing AI applications and organizational changes.Also, the AI middle platform will be more difficult to implement than the data middle platform.Because enterprises can’t use it.Most companies don’t even have a data analyst, let alone an algorithm engineer. To put it bluntly, companies don’t have professionals.Only then can we use the AI middle platform.If it cannot be used, then the market size cannot continue to grow, and this will not be a fertile "land".What is AI Middle Platform
The AI middle platform is the enterprise-level artificial intelligence capability center. Its core goal is to solve the problem of duplicate construction in AI model development and implementation through a standardized and reusable technical architecture, and lower the application threshold. (Does this definition sound familiar? It is almost a replica of the concept of data middle platform.) It is specifically manifested as follows:Capability accumulation and sharing : Encapsulate general AI capabilities such as speech recognition, natural language processing, computer vision, and industry-specific models (such as financial risk control and medical image analysis) into reusable components to form an "AI model market."Full life cycle management : covers model design, training, deployment, monitoring and other links, provides unified data labeling, feature engineering, computing power scheduling and other support, and reduces the time spent by algorithm engineers on engineering. (Note that this does not include big data development and governance)Agile business response : Through microservice architecture and standardized interfaces, AI capabilities can be quickly combined to meet the personalized needs of front-end business and realize the collaborative model of "small front-end, large middle-end".With the development of technology, many components will be provided to the outside world in the form of MCP (mainly for large model applications) rather than traditional AI model components.Let’s take a look at the key architecture and functions of the AI middle platform.
AI middle station is usually divided into three layers of architecture:
Technical service layer : Provides general AI capabilities (such as computer vision, Natural Language Processing) and industry-specific services to support plug-and-play.
R&D platform layer : includes data services (cleaning, labeling) and model development tools (automatic machine learning/AutoML), which lower the threshold for model development.
Management and operation layer : responsible for resource scheduling (computing power, storage), permission control, and version management and sharing of model assets (algorithms, samples).
The essence of the AI middle platform is the "central kitchen" model. Enterprises do not need to build AI infrastructure from scratch, but instead quickly "cook" intelligent applications that meet business needs through standardized processes.
This model is particularly suitable for medium and large enterprises that need to apply AI on a large scale, but attention should be paid to deep integration with existing data middle platforms and business systems.
Can AI middle platform replace data middle platform?
It seems that the AI middle platform is very powerful.Especially with the continued support of the AI craze, AI middleware has ushered in a wave of "trend dividends".So, is the AI middle platform an upgraded version of the data middle platform?After an enterprise purchases an AI middle platform, does it also need a data middle platform?The answer is obvious, no.Let us analyze it from three dimensions: platform positioning, functional substitutability, and whether it can be integrated.First, let’s look at the positioning of the two:It can be clearly seen that the core goal of the data middle platform is to complete the aggregation and processing of data assets, while the AI middle platform is to provide standardized AI services.The two solve different problems and target different groups of people, so they are not essentially a replacement relationship.However, although the two are not in a replacement relationship, the boundaries are merging.For example, the AI middle platform also relies on the data capabilities output by the data middle platform, especially feature engineering and training data, which rely on the data middle platform to provide high-quality, clearly structured, and traceable data assets.As the data center's ability to support AI scenarios increases (such as providing feature platforms, model labels, training samples, etc.), the two tend to merge.Some companies have already implemented the "integrated middle platform" or "intelligent data middle platform" practice, combining the data middle platform with AI capabilities to form a broader intelligent middle platform.The relationship between the two can be described in one word: "parallel collaboration."Different companies are at different stages of digitalization and AI transformation, but overall, they will go through three stages:Will AI middleware repeat the same mistakes?
A few years ago, the data middle platform became the "standard configuration" for corporate digital transformation and was highly anticipated.But soon, we saw it slip from the limelight, gradually cool down, and even be sidelined or marginalized in some companies.The reasons are: unclear positioning, insufficient closed-loop capabilities, and too long value delivery cycle.So, will the AI middle platform repeat the same mistakes and become another " middle platform illusion "?My judgment is that it may not be replicated, but we must be vigilant.AI is indeed still in its explosive growth phase. Especially with the global capital push and the accelerated implementation of large models, AI will maintain the dual-wheel drive of technological evolution and application expansion for a long time.But we must also be clear: the popularity of AI does not mean there is a strong demand for AI middleware.For enterprises, whether to build an AI platform depends on two key issues:If the enterprise itself is algorithm-driven (such as Internet advertising, intelligent risk control, and AI application service providers), the AI middle platform can help consolidate model assets and improve iteration efficiency.On the contrary, for traditional data-centric enterprises, AI is just a capability caller and its construction of a middle platform has limited significance.Can the AI middle platform be replaced by combining existing components?With the rapid development of big models, many companies have explored a lighter and more agile path: data middle platform + big model + enterprise knowledge base (RAG).This type of combination, to a certain extent, skips the traditional AI middle platform and can meet the needs of most intelligent scenarios, with faster launch and lower cost.Things are changing too fast. At least as of now, the AI middle platform is still not at the core and necessary path of enterprise AI transformation.If you want to repeat the same mistake, it will not be too late to discuss this issue after surpassing the popularity and recognition of the data middle platform.
With the development of AI, enterprises have become more anxious. This is because people in the market keep emphasizing that the window period for AI is very short and enterprises must "go all in" immediately, otherwise they will miss the dividend and be eliminated by the times.This story is too classic and too familiar. With every wave of technology and every surge of capital, someone will express the same sense of crisis. It is like a circular script, with actors changing roles in different scenes.Buffett once said: "I focus on the good things that happen, not the bad things. Life can be wonderful - but it can also be terrible."This is his advice to young people when he is about to retire. In fact, this applies not only to individuals, but also to every entrepreneur and manager.So what are “good things”?In the AI era, the best things are not blindly following trends, hastily launching projects, or forcing an AI story just for KPIs. Instead, they are:- Enable organizations to truly understand AI capabilities and build long-term digital literacy;
- Make data more credible, make decisions smarter, and make products closer to users;
- Let enterprises find their own rhythm in the technological wave instead of being swept away by the wave;
- Let technology serve value rather than become a new form of involution.
Seizing the dividends of the times has never been a race of "chasing the wind", but a practice of "laying the foundation".Good things are worth taking your time.
