Generative AI and machine learning: how businesses are using AI in 2024-2025.

Artificial intelligence, especially generative models like ChatGPT, Midjourney, and Stable Diffusion, have rapidly moved beyond laboratories and become part of everyday business processes. Already 56% of companies worldwide use AI/ML technologies to improve operational efficiency, automate processes, and create personalized services. And in the coming years, the pace of adoption will only increase.

In this article, we will explore how generative AI and machine learning (ML) are changing business processes, what advantages they provide, and what trends to expect in the coming years.

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Generative AI: from creativity to automation.

Generative models (LLMs, diffusion models, etc.) are capable of creating texts, images, music, and even code based on simple prompts. This opens up fundamentally new scenarios:

• Content creation: automation of article writing, creation of advertisements, social media posts, presentations, images, programming code.

• Virtual assistants: chatbots, automated consultants, generation and personalization of responses.

• Data enrichment: generation of synthetic data for training and testing, imitation of statistical properties of real data.

• Creative tasks: idea generation, logo creation, prototyping, and design.

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Machine learning: analytics and forecasting.

If generative AI creates content, then classical ML models solve data analysis tasks:

• Demand forecasting - algorithms predict sales, helping to optimize inventory and logistics.

• Fraud detection - banks and fintech companies use ML to identify suspicious transactions.

• Personalized recommendations - like on Netflix or Amazon, where AI suggests products and content based on user behavior.

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Why does business need artificial intelligence?

Organizations in various industries are investing in AI/ML to gain competitive advantages such as:

1. Automation of repetitive processes.
   Document processing, customer support, content moderation - all of this can be automated using ML models and generative agents.

2. Analysis of large volumes of data.
   Machine learning allows for identifying patterns, forecasting demand, analyzing user behavior, and making data-driven decisions.

3. Personalization of products and services.
   Recommendation systems, dynamic pricing, adaptive user experience - all of this has become possible thanks to AI.

4. Cost reduction and increased speed.
   AI allows tasks to be completed faster, with less human involvement and lower costs, especially in areas such as logistics, finance, marketing, and HR.

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Trends in the implementation and development of AI/ML in 2024-2025

Key trends shaping AI today and their impact in the coming years:

• Democratization of AI.
  Thanks to platforms like OpenAI, Google Vertex AI, Hugging Face, and Microsoft Azure AI, AI is becoming accessible even for companies without their own R&D team (Research and Development).

• AI in a box (AI-as-a-Service).
  Ready-made solutions for analytics, support, and natural language processing. Integration through API and Low-Code/No-Code interfaces.

• Growth of private models and customization.
  Companies train their own models on private data or further train public LLMs for their specific tasks.

• Integration of AI into corporate systems.
  Use of AI in CRM, ERP, BI systems. Examples: Salesforce Einstein, SAP AI, Microsoft Copilot.

• Multimodality of models.
  Neural networks are starting to work simultaneously with text, images, and voice.

• Hyperpersonalization.
  Artificial intelligence is starting to better understand individual customer preferences.

• Ethics, security, and regulation.
  Increased attention to issues of transparency, model auditing, preventing "hallucinations," and compliance with GDPR and other regulations.

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Real cases of AI/ML applications in business.

A selection of real cases of AI/ML applications in various business sectors with examples of companies and specific results:

1. Retail and E-commerce.

Case: Amazon - personalization and demand forecasting

• What they use: ML recommendation algorithms + predictive analytics.

• Result: 35% of sales are generated by the recommendation system + warehouse optimization reduced logistics costs by 20%.
  

Case: Alibaba - visual search

• What they use: Computer Vision for searching products by photo.

• Result: Increase in purchase conversion by 15%.

 

2. Finance and Banking.

Case: JPMorgan Chase - document analysis

• What they use: NLP (Natural Language Processing) in the COiN system.

• Result: Automatic analysis of 12,000 contracts per second (instead of 360,000 man-hours). 

Case: Mastercard - combating fraud

• What they use: ML models to detect anomalous transactions.

• Result: 50% reduction in fraud.
  

 

3. Healthcare.

Case: IBM Watson - cancer diagnosis

• What they use: Analysis of medical images and scientific articles.

• Result: The accuracy of oncology diagnosis increased to 93% (compared to 50% with traditional methods).
  

Case: Zebra Medical Vision - X-ray analysis

• What they use: AI to detect pathologies in the images.

• Result: A 30% reduction in the workload for radiologists.
  

 

4. Manufacturing and Logistics.

Case: Siemens - predictive maintenance

• What they use: Sensors + ML for predicting equipment failures.

• Result: Reduction of downtime by 25%.

Case: DHL - route optimization

• What they use: Delivery optimization algorithms.

• Result: Reduction in van mileage by 15%.

 

5. Marketing and Advertising.

Case: Coca-Cola - generative design

• What they use: AI (DALL-E) to create packaging variations.

• Result: Doubling the speed of product launch to market.
  

Case: Netflix - content recommendations

• What they use: ML algorithms based on user behavior.

• Result: 80% of views are initiated through recommendations.
  

 

6. Recruitment (HR-tech).

Case: Unilever - interview automation

• What they use: AI analysis of video interviews (body language, tone of voice).

• Result: Reduction of hiring time by 75%.
  

 

7. Agriculture.

Case: John Deere - autonomous tractors

• What they use: Computer Vision + IoT for precision agriculture.

• Result: Increase in yield by 10-15%.

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How can we help?

At We Can Develop IT, we:

• Analyze business goals and data.

• Determine which tasks can be effectively solved using AI/ML.

• Select suitable models and platforms (GPT, Claude, open-source, private).

• Integrate AI into existing systems or create an MVP from scratch.

• Ensure quality control, security, and scalability of solutions.
  
  

Looking for a way to use AI strategically, not just "play around"?

Contact us, and we'll show you how to implement AI/ML in your business with real benefits.

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