Внедрение технологий ИИ в цифровые платформы
What is meant by AI in the context of digital platforms
In the context of digital platforms, artificial intelligence is not a "neural network for the sake of a neural network" or a separate smart function, but a layer of intelligent data processing and decision—making embedded in the product architecture. This layer works continuously, uses the accumulated platform data and affects key user and business processes.
AI in the platform sense is a set of models, algorithms, data, and infrastructure that enable a system to:
- analyze user behavior and processes;
- adapt to the changes;
- automate decision-making;
- scale without linear cost growth.
It's important that it's not just about content generation or chatbots. In the platform, AI can be hidden from the user, but at the same time determine the logic of recommendations, priorities for processing applications, price dynamics, task routing, and much more.
Why are companies starting to implement AI at the platform level, rather than individual functions?
Early attempts at implementing AI were often limited to point-to-point solutions: smart search, support chat, and automatic classification. These approaches have a local effect, but they quickly hit the ceiling of scaling.
The platform level is becoming a priority for several reasons.
First, the data. The main value of AI lies in data, namely, the platform accumulates a complete picture of user actions, transactions and processes. A separate function does not see the entire system.
Secondly, the effect of scale. When AI is implemented at the platform level, the same model or agent can serve dozens of scenarios, gradually learning and improving.
Thirdly, controllability. Platform-based AI is easier to control, update, measure its impact on business metrics, and integrate with existing architecture.
As a result, companies are moving from "smart buttons" to "smart systems," where AI becomes an infrastructure component.
Key classes of AI technologies: ML, LLM, NLP, CV, recommendation systems
Digital platforms use not one type of AI, but a combination of technologies, each of which solves its own class of tasks.
Machine learning (ML) is used to predict, classify, identify anomalies, and optimize processes. This is the basis for analytical and management scenarios.
Large Language Models (LLM) are used to work with text, dialogues, instructions, content generation, and interpretation of user queries. They become a universal interface between a person and a platform.
Natural language processing (NLP) underlies the search, feedback analysis, and automatic categorization of requests and documents.
Computer vision (CV) is used in platforms working with images and videos: quality control, object recognition, automation of visual checks.
Recommendation systems create a personalized user experience: from content and products to action scenarios within the platform.
In mature systems, these technologies work together to form a coherent intelligent circuit.
The main scenarios for the introduction of AI into digital platforms
In practice, AI in platforms is implemented around specific scenarios rather than abstract possibilities.
Intelligent user support is often the first step: automating responses, routing requests, and reducing the burden on operators.
The next level is the optimization of internal processes: task allocation, load forecasting, and bottleneck detection.
In marketing and sales, AI is used to personalize offers, score leads, and predict conversions.
In analytics, AI helps to move from reporting to recommendations and predictive models.
Gradually, the platform may come to semi-autonomous or autonomous scenarios, where the AI initiates actions within the framework of the specified rules.
Architectural approaches: embedding, microservices, external APIs
There are three main approaches to AI architecture in platforms.
Embedding involves implementing AI logic inside the main application. This provides high control, but requires serious expertise and makes scaling more difficult.
The microservice approach puts AI into separate services that interact with the platform via the API. This option provides flexibility, error isolation, and independent model development.
Using external APIs allows you to quickly launch AI functions, but it creates dependence on third-party vendors, limits customization, and can be risky strategically.
In practice, a hybrid approach is often used: critical components are developed internally, while auxiliary components are connected externally.
Economic effect: cost reduction, revenue growth, scalability
The economic impact of AI is evident in three key areas.
Cost reduction is achieved by automating routine operations, reducing manual labor, and improving decision accuracy.
Revenue growth is associated with personalization, increased conversions, user retention, and the introduction of new AI-based products.
Scalability allows the platform to grow without proportionally increasing the team and operating costs.
It is important that the effect is rarely instantaneous. AI is an investment that unfolds as data accumulates and processes mature.
Risks and limitations of AI implementation
Despite the potential, the introduction of AI comes with risks.
The key risk is data quality. Bad data leads to bad decisions, regardless of the complexity of the models.
The second risk is high expectations. AI does not replace strategy and management, it only enhances them.
The third is the difficulty of support. The models require monitoring, retraining, and quality control.
It is also important to consider legal, ethical, and regulatory aspects, especially in sensitive industries.
Stages of AI implementation in the platform: from pilot to industrial operation
The mature implementation of AI goes through several stages.
First, a business task is formulated and the feasibility of automation is assessed.
Then a pilot or MVP is created, which tests the hypothesis on a limited amount of data.
After confirming the effectiveness, the AI is integrated into the platform architecture and scaled.
The final stage is industrial operation with monitoring, metrics and a development plan.
Skipping stages almost always leads to frustration and loss of trust in technology.
How AI platforms will evolve in the next 2-3 years
In the coming years, AI in platforms will shift from auxiliary functions to the role of an active participant in processes.
Agent-based approaches are expected to grow, where AI will be able to plan actions, interact with tools, and coordinate processes.
Platforms will become more adaptive, context-sensitive, and personalized.
AI will increasingly be used not only for optimization, but also for designing new business models.
Conclusions for business and development teams
AI in digital platforms is not a module or a trend, but an architectural solution with long—term consequences.
For businesses, this is a way to increase sustainability, efficiency, and competitiveness.
For development teams, it's a transition from feature development to intelligent system design.
Companies that start considering AI as a platform layer today will gain a strategic advantage in the coming years, when intelligent systems will become the standard, not the exception.