How to apply artificial intelligence and machine learning in business? | Sailet

In the previous article, we dissected what AI is and why it is important for businesses. Today, we will determine in which business areas AI and ML can be successfully applied.


Automation of production lines.

Machine learning systems can monitor and manage a multitude of operations, reducing the risks of human error and optimizing work processes. This can lead to:

  • Increased productivity:

Automatic process control allows for improved efficiency and reduced labor costs.

  • Reduction of defects:

AI can detect defects at early stages of production, helping to reduce the number of defective products.

Equipment failure forecasting

Machine learning enables the prediction of when equipment may fail, which offers the following advantages:

  • Reduced downtime:

Predicting failures allows for planned maintenance and prevents equipment downtime.

  • Resource savings:

Resource usage (such as electricity) is optimized, as equipment operates more efficiently.

Public institutions

In this niche, AI helps improve the efficiency of administrative processes by:

  • Reducing bureaucratic costs:

Automating routine tasks can cut expenses on administrative processes and personnel.

  • Improving citizen services:

Through electronic systems, citizens can receive services faster and more conveniently.

Data analysis

In the field of public administration, AI can be used for more effective analysis of things like:

  • Public opinion monitoring:

Collecting information from social media and news, which helps better understand the public opinion of citizens and prevent potential crises.

  • Budget optimization:

Analyzing budget data and providing recommendations for optimizing expenses.


Routing and logistics management

In logistics, artificial intelligence can be applied in:

  • Route optimization:

Based on the analysis of maps, calculating more optimal routes for freight vehicles and delivery services.

  • Inventory management:

Monitoring and optimizing inventory levels and product orders according to current demand.

Demand forecasting

AI can also analyze data on consumer demand:

  • Demand prediction:

More accurate forecasting of future demanded goods in the market.

  • Production planning:

Assisting in determining when and how much to produce.


Smart agriculture

In the agro-industrial sector, AI and ML contribute to increasing yields and reducing costs:

  • Soil monitoring:

Analyzing data about soil composition and providing recommendations for optimal cultivation methods.

  • Irrigation optimization:

Automatic management of the irrigation system, based on data on humidity and weather conditions.

Soil and plant growth analysis

Additionally, it can assist in the analysis of soils and plant growth:

  • Choosing optimal crops:

Collecting and analyzing data and recommending which crops to plant under specific conditions.

  • Yield forecasting:

Predictions of yield and the ability of agricultural enterprises to plan the season more effectively.


Medical diagnostics

In healthcare, AI can play a significant role in medical diagnostics:

  • Automation of medical image analysis:

Analyzing images, X-rays, and possibly making diagnoses.

  • Disease diagnostics:

Accurate diagnosis of various diseases based on symptoms.

Medical information management

AI provides efficient management of medical information:

  • Electronic medical records:

Assisting in the collection, storage, and processing of medical data, which can improve patient care.

  • Drug research and development:

Machine learning can be used to analyze data on clinical trials and the development of new drugs.

Financial sector

In the financial sector, AI is used to detect fraud and ensure the security of transactions:

  • Transaction analysis:

Identifying anomalies and suspicious transactions to prevent fraud.

  • Biometric authentication:

Using biometric data to confirm the identity of clients.

Investment portfolio management

In investment portfolio management, AI plays a crucial role:

  • Market monitoring:

Analyzing market data and providing investment recommendations.

  • Portfolio management:

Optimizing the asset portfolio, considering the goals and risks of investors.

Retail and E-commerce

Personalization of offers

In retail and e-commerce, AI is used for personalizing offers:

  • Product recommendations:

Analyzing purchase history and offering users the most suitable products.

  • Personal discounts and promotions:

Adapting discounts and promotions to customer preferences.

Consumer behavior analysis

AI analyzes data on consumer behavior:

  • Understanding customer preferences:

Identifying preferences and consumer trends.

  • Demand forecasting:

The ability to predict demand for various goods and plan inventories.

It is important to note that as technologies continue to develop, new applications emerge, creating vast opportunities for businesses to optimize their processes, improve service quality, and achieve competitive advantages.

Statistics on AI usage in Kazakhstan

According to research provided by the «Center for Development of Labor Resources» for 2023, it is clear that the trend towards the implementation of artificial intelligence in various sectors of Kazakhstan is gaining momentum.

This indicates that in the near future, AI will play an increasingly significant role in the field of business and labor relations.

Let’s examine this in more detail:

  1. Growth of automation:

Data from 2023 shows that initially about 75.1% of the workforce occupies positions that remain resistant to automation. However, this is just the beginning.

  1. First steps:

Approximately 17% of employees (1.5 million people) already have the likelihood that their job duties could be transferred to production robots or artificial intelligence. This is the first step in the process of implementing AI in various fields.

  1. Gradual replacement:

Additionally, according to the data, 7.9% of workers (686,000 people) have already been partially replaced by technologies. This demonstrates that the process of replacing human work functions is ongoing.

  1. Focus on key sectors:

The information and communication (53.3%) and finance and insurance (52.1%) sectors are where AI has the greatest potential for automation. These industries are becoming important growth points.

  1. Strengthening the trend:

The average level of automation potential in Kazakhstan’s economy is estimated at 24.9%. This indicates that more and more industries are starting to implement artificial intelligence, and this trend will continue.

Thus, we see that the data reflects a gradual expansion of the use of artificial intelligence in various sectors of the Kazakhstan economy, which is an important step in developing our country in the digital era.

How to apply artificial intelligence in business?

Implementing artificial intelligence (AI) and machine learning (ML) in business requires a comprehensive approach and adherence to key stages. Below, we will elaborate on each of them:

  1. Choosing the right tools and technologies for AI in business:

Before beginning implementation, it’s necessary to determine the tools and technologies that are most suitable for solving specific business tasks.

It is important to consider the following points:

  • Choosing platforms and frameworks:

Determine which platforms and frameworks best suit your tasks.

If you cannot define your stack, or do not understand how to describe your technical task, you can leave us a request for the development of the terms of reference.

  • Hiring qualified specialists:

Attract qualified AI and ML specialists who have extensive experience in this niche and can help you. Also, you can familiarize yourself with our AI development service.

  1. Collecting and processing data for training machine learning models:

Data is the foundation for building successful ML models. Proper collection and processing of data is an important stage in AI implementation:

  • Data collection:

Determine what information is needed for training. This can be structured data from databases, textual data, images, or time series. Ensure reliable data sources.

  • Data preparation:

Data often requires preliminary processing, including cleaning from outliers, normalization, filling missing values, and encoding categorical features.

  1. Training models and integrating AI into business processes:

At this stage, you create and configure the ML model and integrate it into business processes:

  • Model training:

Use the prepared data to train models. This process includes selecting algorithms, tuning parameters, and evaluating model performance.

  • Integration into business:

After training, the model is integrated into the process. This could mean automating routine tasks, predicting demand, optimizing resources, and other applied scenarios.

Thus, proper implementation of AI in business requires careful preparation, selection of appropriate tools, and continuous updating and optimization of models to achieve maximum efficiency and competitive advantages in the modern corporate environment.


In conclusion, it is important to note that artificial intelligence is becoming an indispensable assistant in business development, capable of significantly unloading your team and providing answers to strategic questions in the shortest possible time.