How to apply artificial intelligence and machine learning in business?

In the last article, we broke down what AI is and why it's important for businesses.
Today, we'll look at what areas of business can successfully apply artificial intelligence (AI) and machine learning (ML).

Production

Automation of production lines

Machine learning systems can monitor and manage multiple operations, which reduces the risks of human error and optimizes workflows. This can lead to:

• Increased productivity:

Automated process control improves efficiency and reduces labor costs.

• Reducing scrap:

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

• Predicting equipment failures

Machine learning can predict when equipment is likely to fail. This provides the following benefits:

• Reduced downtime:

By predicting failures, preventive maintenance can be scheduled and downtime can be prevented.

• Resource Conservation:

The use of resources (e.g. electricity) is optimized because the equipment operates more efficiently.

Public institutions

In this niche, AI is helping to improve the efficiency of administrative processes, thanks to:

• Reducing bureaucratic costs:

Automating routine tasks can reduce administrative process and personnel costs.

• Improving services to citizens:

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

Data analysis

In public administration, AI can be used to better analyze things like:

• Public Opinion Monitoring:

Gather information from social media and news to better understand citizens' public opinion and warn of potential crises.

• Budget Optimization:

Analyzing budget data and providing recommendations for cost optimization.

Logistics

Routing and logistics management

In logistics, artificial intelligence can be applied to:

• Route Optimization:

Based on map analysis, calculating better routes for trucks and delivery services.

• Inventory Management:

Control and optimize inventory levels and ordering of goods according to current demand.

Demand forecasting

AI can also analyze consumer demand data:

• Demand Prediction:

More accurate forecasting, about future demanded products in the market.

• Production Planning:

Help in determining when and how much goods will need to be produced.

Agro-industry

Smart agriculture

In the agro-industry, AI and ML are helping to increase yields and reduce costs:

• Soil Monitoring:

Analyzing soil composition data and providing recommendations on optimal cultivation practices.

• Optimizing irrigation:

Automatic control of the irrigation system based on moisture and weather conditions.

Soil and plant growth analysis

In addition to everything can help in analyzing soils and plant growth:

• Selection of optimal crops:

Collect and analyze data and recommend which crops should be planted under specific conditions.

• Yield Prediction:

Predicting yields and allowing farms to plan the season more effectively.

Health care

Medical diagnostics

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

• Automating the analysis of medical images:

Analyzing scans, x-rays, and being able to conclude them.

• Disease Diagnosis:

Accurate diagnosis of various diseases based on symptoms.

Health information management

AI enables efficient management of health information:

• Electronic medical histories:

Assist in the collection, storage and processing of medical data that can improve patient care.

• Drug research and development:

Machine learning can be used to analyze clinical trial data and develop new drugs.

Financial sector

In the financial sector, AI is used to detect fraud and secure transactions:

• Transaction Analysis:

Identify anomalies and suspicious transactions, preventing fraud.

• Biometric authentication:

Use of biometrics to confirm the identity of customers.

Investment portfolio management

In the management of investment portfolios, AI plays a crucial role:

• Market Monitoring:

Analyzing market data and providing investment recommendations.

• Portfolio Management:

Optimizing the portfolio of assets, taking into account the goals and risks of investors.

Retail and E-commerce

Personalization of offers

In retail and e-commerce, AI is being applied to personalize offers:

• Product Recommendations:

Analyze purchase history and provide users with the most appropriate products.

• Personalized discounts and promotions:

Adapting discounts and promotions to customer preferences.

Analysis of consumer behavior

• Understanding customer preferences:

Identifying consumer preferences and trends.

• Demand Forecasting:

Ability to predict demand for various products and inventory planning.

It is important to note that every year technology continues to evolve and new applications emerge, creating huge opportunities for businesses to optimize their processes, improve services and achieve competitive advantage.

Statistics on the use of AI in Kazakhstan

Based on the research provided by the "Center for Workforce Development" JSC for 2023, we can see that the trend of introducing 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 business and labor relations.

Now let's look at this in more detail:

1. The rise of automation:

The data from 2023 shows that initially about 75.1% workers are in positions that remain resistant to automation. However, this is just the beginning.

2. First Steps:

About 17% of the employed (1.5 million people) already have the possibility that their job duties could be transferred to production automatons or artificial intelligence. This is the first step in the process of introducing AI into various fields.

3. Gradual replacement:

Furthermore, according to the data, 7.9% of workers (686 thousand people) have already been partially replaced by technology. This demonstrates that the process of replacing human work functions is on its way.

4. Focus on key industries:

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

5. Reinforcing 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 beginning to implement artificial intelligence, and this trend will continue.

Thus, we see that the data reflect the gradual expansion of the use of artificial intelligence in various sectors of Kazakhstan's economy, which is an important step in the development of 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 following key steps. Below we detail each of them:

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

Before you begin implementation, you need to decide on the tools and technologies that are best suited for your specific business challenges.

It is important to consider the following points:

• Selection of platforms and frameworks:

Determine which platforms and frameworks best fit your objectives.

• Selection of qualified professionals:

Engage qualified AI and ML professionals who have a wealth of experience in this niche and will be able to help you.

2. Data collection and processing for training machine learning models:

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

• Data collection:

Determine what information is needed for training. This could be structured data from databases, text data, images or time series. Provide reliable sources of data.

• Data preparation:

Data often require preprocessing, including outlier cleaning, normalization, filling in missing values, and coding categorical attributes.

3. training models and integrating AI into business processes:

In this phase, you create and customize the ML model and integrate it into business processes:

• Model Training:

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

• Business Integration:

Once trained, the model is integrated into the process. Which can mean automation of routine tasks, demand prediction, resource optimization and other application scenarios.

So, properly implementing AI in business requires careful preparation, selection of appropriate tools, and constant updating and optimization of models to maximize efficiency and competitive advantage in today's corporate environment.

Conclusion

To summarize, it is important to note that artificial intelligence is becoming an indispensable assistant in business development, able to significantly relieve your team and provide answers to strategic questions in the shortest possible time.

If you are interested in developing your own AI model, leave a request with us and we will be happy to advise you!