Latest AI Trends 2025
Technology is moving the world. They make our lives better, create new professions and industries, and open up new investment opportunities. Artificial intelligence is the undisputed leader of human thoughts, hopes and fears. Innovation is developing at a rapid pace, and many people are frightened and delighted by it at the same time. Analytics and forecasts on AI trends take the top spot in the ratings of almost all research companies.
According to Precedence Statistics, generative AI will be a $1.3 trillion market by 2032, and the industry will grow at an average annual rate of 42% over 10 years.
This trend was quickly picked up by major technology companies - Alphabet, Amazon, Apple, Meta and Microsoft - investing huge sums in development AI. In 2024, they have allocated about $400 billion from their budgets for expenses related to artificial intelligence. And talented startups and developers are raising millions of dollars by proposing new uses for neural networks. Which AI 2025 new trends are waiting for us very soon? We have selected 5 promising technologies.
The introduction of artificial intelligence into business processes is still a niche activity. Companies mostly use them instrumentally, to solve point routine tasks: working with documents, databases, and so on.
But gradually, the technology will become a real rocket fuel for growth. Experts predict that large language models will continue to develop. But at the same time, platforms will emerge with a narrow specialization. They will give companies the tools to use generative artificial intelligence without the necessity of deep internal technical expertise, restructuring of technical departments, and searching for new specialists.
Then there will be entire networks of generative platforms customized for specific business tasks, capable of forming new ecosystems in industries.
The first thing is detailed personalization. Many banks have such a service as a personal banker - a specialist who is assigned to specific clients and whom they can contact with any questions. With the expansion of Internet banking and neobanks, personal bankers are increasingly being replaced by software interfaces.
The usage of AI tools (including generative AI) will make it possible to create virtual personal assistants. Such an assistant will always remind about paying a mandatory payment, warn about a risky transaction or advise on services available at the bank. It can be either a “faceless” chatbot, AI trading bot or a generated video consultant, but in any case it will know all the necessary information about the client, will be able to help promptly and even take over some routine functions such as paying regular bills.
One more important area in fintech is risk management. For financial institutions, many decisions carry certain risks. Whether to issue a loan to a new corporate client? Should a new branch be opened? How to allocate capital? Artificial intelligence will help to find optimal solutions to these and dozens of other questions. Of course, no one guarantees that this advice will be 100% correct. However, AI's ability to analyze huge amounts of information will significantly reduce risks when making certain financial decisions.
AI also analyzes huge amounts of data quickly. This will help in the fight against fraudsters and criminals. With its help, it will be very easy to identify suspicious and atypical fund transfer and electronic payment transactions. Furthermore, the analysis of many parameters will allow us to identify criminal transactions, rather than trying to sweep all atypical payments under the same brush (for example, when a person simply makes a one-time large purchase).
Payment automation is another important AI 2025 trend. Therefore, developers are actively creating AI apps. Another popular technology - blockchain - is often used for this purpose.
Many transactions, especially in the corporate segment, are still carried out manually. For example, to issue a large loan for business development, bank employees perform many routine operations and analyze a lot of information, studying the client's solvency. The introduction of AI tools will make it possible to speed up such processes significantly and expedite customer service.
Big data is the most valuable tool of influence for the modern retailer. The winner is the one who manages to collect, analyze and make decisions based on the information received quickly. And here the capabilities of artificial intelligence are already significantly winning over humans. So experts expect further development of this AI trend in 2025.
Especially relevant will be programs that help employees of retail chains. The fact is that such employees usually do not transmit much data to the business. Chatbots for retail will help to automate data collection. Bot is a part of the wider enterprise computer system, so the details of every customer interaction can be fed directly into the database. The data collection can include a complete “report” submitted for each customer: initial greeting, tone, pace, answers to customer questions, and of course, dollar results.
Depending on the ethical stance of the firm, an AI bot can also be designed to collect not only the client's words, but also various “metadata”. For example, to analyze and create a customer profile: male or female, young or old, thin or fat, etc.
For commerce, 'touchpoints' - moments of contact with the customer where they can influence the customer's perception and decision - are critical. Professionals have been specially selected or trained to provide effective touchpoints for this purpose in the past. But humans are not really able to model millions of previous customer interactions or compare them to the customer standing in front of them. On the other hand, AI bots can.
What does it mean? Using gigabytes of previous data, retail bots can profile the current customer and adjust their behavior accordingly, interact with the customer, and then feed back the data created to make a better sale next time. And that next time could be two seconds later at an identical retail outlet on the other side of the country with a similar customer.
These are some of the main areas and things that developers can work with. The first task is to optimize time. In a week, health care workers spend an average of 14 hours on documentation. This is 25% more than 7 years ago. Generative AI helps automate these tasks, significantly speeding up the completion of reports and record keeping.
Chatbots based on large language models also handle about 80% of simple queries, collecting data for a preliminary examination, analyzing the medical history and highlighting critical data for the doctor. This allows doctors to focus on more complex cases, leaving routine tasks to AI.
The second area is diagnostics and medical imaging. Diagnostics accounts for more than 10% of healthcare expenditures. Neural network optimizes time and resources at the initial stage of treatment, improving the accuracy and efficiency of medical imaging (MRI, CT, ultrasound). AI also enables early detection of diseases, providing detailed information for individualized treatment plans.
Personalized medicine is another challenge. According to statistics, 734 million people in the world do not have access to basic medical services, and in half of the countries there is less than one doctor per 1,000 people. Generative AI makes medical services more accessible and easier to obtain. Generative AI-based systems keep individual records and documentation for each patient, generate a schedule of medications and procedures, and fully control the treatment process.
However, the most anticipated technologies on the part of humanity are all about life extension and the early disease detection.
That's why the best minds are developing programs to help identify cancer cells in their early stages. For example, researchers at the Massachusetts Institute of Technology trained an AI model on six-year lung scans of patients from the United States and Taiwan. The tool predicts the lung cancer that will occur within one year, but is also able to predict the disease up to six years in advance.
Companies are also actively developing personalized programs that help to improve the quality of life. Neural networks make a schedule of training, sleep and rest for a person, remind about important diagnostics or trips to the doctor, and warn about changes in the weather. Such technologies will be very popular.
Another part of the workflow you can assign to AI is testing the scalability of the system. A company can create scenarios that simulate different loads on the system, from small to large amounts of data and transactions. AI can run these scenarios and analyze the system's response, which can identify potential performance and scalability issues. Artificial intelligence can also create its own scenarios and tests to evaluate system performance, Chat GPT can be used for this, but now there are more interesting cases.
It is also appropriate to use AI to generate malicious scripts to be used during testing. Algorithms analyze large amounts of data about known attacks, including vulnerability types, exploit method, and malicious scripts used during the attack. New scripts are generated on the basis of this to simulate a potential attack, making testing for attacks more efficient.
The list of successful AI startups from the US included the following firms:
- Hebbia - $130 million.
- Skild AI - $300 mln.
- Bright Machines - $106 million.
- Etched.ai - $120 million.
In general, AI companies raise more money at funding rounds than startups from other fields. Experts predict that this trend will continue next year.
The first aspect is the great competition. Technology is actively developing now, and thousands of startups are appearing. It is hard to say which of these companies will become the new Google or Apple. The second aspect is time. It takes time for most new technologies to find their niche in the economy.
Assuming that big tech's AI revenues grow at an average of 20% per year, as experts predict, investors will receive almost all of their AI profits after 2032. But there is another scenario in which the growth will be very fast and sharp. Then we should expect a sharp rise in the share prices of developers, providers and major users of AI technologies.
Only time will tell which of these scenarios will be realized in the future. In any case, 2025 will be an interesting year, with new AI trends, technological breakthroughs and revolutionary developments coming our way.
