Artificial Intelligence Market Trends

Global IT Market Trends

White Paper: Global IT Market Trends

2025

Top Artificial Intelligence Trends in 2025

The global artificial intelligence market continues to grow rapidly. At the same time, there is a gradual transition from generative AI (Genia) as a central direction to other technologies. Analysts Gartner in the material dated August 5, 2025 outlined four main trends in the field of AI.

1. Agent AI

This is a specialized software that can interact with the environment, collect data and based on it independently determine and perform tasks that allow you to achieve pre-set goals. No human intervention is required. AI agents can receive certain information through physical or software interfaces. An important feature of such systems is their ability to operate in the real world using digital tools, and not just provide results. AI agents are able to improve the performance of business operations by performing routine tasks. At the same time, unnecessary costs associated with inefficiency of processes, human errors and manual labor are reduced. Sophisticated intelligent agents use machine learning to collect and process vast amounts of real-time data: this helps inform informed decision-making. In addition, the quality of consumer service is improving: the use of AI agents allows companies to personalize product recommendations, respond quickly to requests and innovate to increase customer engagement, conversion and loyalty.

2. Data for AI

Arrays of information specially prepared for AI applications help improve the accuracy and efficiency of the corresponding systems. According to the recommendations of Gartner, organizations investing heavily in neural network technologies need to develop their own methods and capabilities for managing data in order to expand their application in the field of AI. This will address current and future business needs, provide trust, avoid risks and regulatory challenges, protect intellectual property, and reduce bias and hallucinations.

3. Multimodal AI

Such AI systems are able to receive, analyze and understand heterogeneous information from various sources: these can be images, text, audio and video materials, any schemes and graphs, readings from sensors, etc. Compared to traditional AI models, such neural networks understand the world around them much deeper and more accurately. Multimodal AI can detect complex connections between sets of incoming information, generating more accurate and context-sensitive results. The popularity of multimodal AI is growing rapidly, which is explained by its flexibility and effectiveness in solving complex problems. However, the implementation of such systems comes with significant financial costs, since processing heterogeneous data, including images and video materials, requires powerful hardware platforms.

4. AI Trust, Risk and Safety Management (TRiSM)

We are talking about a comprehensive strategy covering technological, organizational and other aspects of working with AI. Gartner notes that many AI problems are not related to malicious attacks, but to questions of trust in the results and previously unforeseen risks. The concept of TRiSM is focused on managing AI models, ensuring their reliability, fairness, efficiency and sustainability. Significant attention should be paid to privacy and data protection.^[1]

3 main trends in the global AI market for 2026

Artificial intelligence technologies continue to rapidly gain momentum around the world. Neural networks allow you to transform traditional ones, business processes increasing their efficiency, reducing costs and reducing the burden on employees. AI algorithms are capable of analyzing huge amounts of data from various sources at high speed, which helps in management decisions. Analysts Deloitte in the review of July 18, 2025 identified three main trends in the global market. AI

1. Agent AI

We are talking about specialized software that is able to interact with the environment, collect data and based on it independently determine and perform tasks that allow you to achieve pre-set goals. AI agents allow companies to reduce unnecessary costs associated with process inefficiencies, human errors and manual operations. Such systems use machine learning to collect and process vast amounts of real-time data. AI agents allow organizations to automate not only repetitive tasks, but also dynamic multi-step processes. These systems obtain information about the environment through physical or software interfaces and make informed decisions based on the principles inherent in them in order to optimize performance and improve results. Integrating AI agents into business processes allows companies to personalize product recommendations, respond quickly to consumer demands, and innovate to increase customer engagement, conversion, and loyalty. Agents can optimize product inventory, logistics and procurement in real time, detect fraud, and monitor compliance.

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2. Physical AI

This concept involves the use of artificial intelligence in the physical world: thanks to this, machines are able to meaningfully interact with the environment. AI algorithms can be integrated with robotics, autonomous vehicles, the Internet of Things (IoT) and digital twins. Examples are warehouse robots, smart medical devices and smart traffic lights. Physical AI can open up new possibilities in terms of improving the efficiency of various operations and improving security in sectors where automation was previously limited due to high complexity or excessive financial costs. Deloitte analysts believe that physical AI can gain significant distribution primarily in sectors with a large amount of assets and high intensity of work - in the manufacturing industry, logistics, healthcare, agriculture, etc.

3. Sovereign AI

Such platforms ensure that data, model weights, and computing resources remain within defined national or regional boundaries. In addition to addressing regulatory, privacy and geopolitics issues, sovereign AI can help build trust among customers and partners as well as reduce reliance on foreign technology providers. Deloitte believes that in 2026, organizations will begin to more actively implement AI solutions that comply with local laws and regulations. In order to localize data and computing resources, special multi-cloud and peripheral platforms can be formed. Against the background of increased control by regulatory bodies, the development of regional and national AI centers is expected.^[2]

2024

How artificial intelligence will develop in 2025. 10 forecasts

Artificial intelligence technologies continue to evolve rapidly, with an increasing impact on various industries. At the same time, organizations are gradually moving from experiments to more meaningful implementation, as stated in the IDC review published on October 30, 2024. Analysts have formed 10 major projections that shape the future of the IT industry in the AI era.

In the changing landscape of AI, the prospects of companies depend on the ability to not only experiment, but also strategically change activities, transforming experiments into sustainable innovations. By implementing AI, priority must be given to those areas that will ensure the stability of the business in a world that is increasingly dependent on data, says Rick Villars, vice president of IDC.

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AI economics

Businesses are expected to focus on estimating the scale of AI use in 2025, moving from experimentation to monetization of these technologies. Creating a solid foundation for automated analysis and optimization of AI-enabled applications will be a prerequisite for overcoming the obstacles associated with IT modernization.

Barriers to AI implementation

The authors of the study highlight several factors that may interfere with the use of AI, including generative. These are a lack of qualified specialists, high costs, insufficient infrastructure performance and poor process coordination.

Cyberstability

An organization's failure to adapt to changing threats, including those related to AI, will negatively impact business results and competitive market position.

Cloud evolution

Companies that have successfully upgraded their cloud infrastructures will benefit from a number of benefits. These include increased productivity and efficiency, improved ROI, etc.

Data as Product

Data should be seen as a product that does the necessary work, such as improving decision-making, helping to identify fraud, or alerting an organization to change conditions in the supply chain.

Transforming Applications

We are talking about AI agents: these are autonomous intelligent systems that are able to perform certain tasks without human intervention. They use machine learning to collect and process vast amounts of information in real time.

Inference Strategy

As organizations accelerate the introduction of Genia, the need for inference will continue to increase. In such a situation, IDC experts believe, enterprises should develop a strategy of "multi-inference," that is, multiple output loads.

Unified AI Platforms

The success of AI projects will depend on the introduction of technologies throughout the organization in order to form a holistic coordinated platform. This will improve economic efficiency and productivity.

Decarbonization of AI infrastructure

As computing workloads increase, enterprises will have to pay attention to minimizing the impact of AI systems on the environment by addressing key issues such as improving energy efficiency, optimizing resources and reducing electronic waste.

The impact of AI on the labor process

The need for automation will lead to the transformation of traditional workplaces. AI technologies will help reduce the burden on employees when performing a number of routine tasks. On the other hand, some positions may be redundant, leading to their abolition.^[3]

8 trends in artificial intelligence

The global artificial intelligence market continues to develop rapidly, and the scope of application of relevant technologies is constantly expanding. At the same time, AI startups increase revenue faster than traditional software developers. This is stated in a study by Air Street Capital, the results of which were published on October 10, 2024.

There is growing concern among some investors that the hype around AI could turn into a bubble, with many businesses yet to decide how to best use such technology. However, an analysis by the fintech company Stripe indicates that successful AI startups reach an annual income of $30 million or more on average in 20 months. For comparison: for ordinary SaaS providers (software as a service), this takes an average of 65 months, that is, three times more time. And AI companies, founded in 2020 and later, reach revenue of $1 million on average in 5 months versus 15 months for startups in the SaaS segment.

In 2024, the United States accounted for the largest number of published works in the field of AI - approximately 29.5% of all articles. China is in second place with a share of about 23.6%. Development in the field of AI is actively underway in all regions of the world. Against this background, analysts at the London investment company Air Street Capital highlight eight main trends in the relevant area.

1. Leading AI developers are developing at a similar pace, and their models become comparable in performance and capabilities to each other. As a consequence, the gap between GPT-4 and other solutions is narrowing.

2. The next generation of large language models (LLMs) are prioritized by reasoning and planning functions. In order to create new AI agents, companies are exploring opportunities to combine LLM with approaches such as reinforcement training, evolutionary algorithms and self-improvement.

3. Basic AI models go beyond speech and text processing. They are able to work with different data formats in fields such as mathematics, biology, genomics, physical sciences and neurotechnology.

4. US sanctions have limited impact on the ability of Chinese companies to develop effective AI models. This is due to the availability of stocks of high-performance chips, smuggling and access to cloud resources. At the same time, analysts emphasize, it is not clear whether the desired result will bring the efforts of the PRC to create its own semiconductor industry.

5. The total value of AI companies as of 2024 reached $9 trillion. Investment in private AI enterprises has also risen, but not so significantly, despite the success of a number of generative AI projects in the American market.

6. Some AI companies are starting to get serious revenue. We are talking about developers of basic models and startups, whose tools are designed to generate audio and video materials.

7. Individual companies are struggling to find a viable business model in a highly competitive AI market. Due to lack of funds, they are forced to use the pseudo-acquisition mechanism of another organization.

8. Discussions on the existential risks of AI have subsided - especially after the failed coup at OpenAI. However, researchers continue to explore potential model vulnerabilities and ways to abuse them, offering security solutions and methods.^[4]

Named the most important AI technologies in the world

Investment in artificial intelligence applications and systems continues to increase, and organizations around the world are actively implementing generative services. The focus is increasingly on risk management and data security. Analytical company Gartner on June 17, 2024 named the most important AI technologies on a global scale.

Autonomous systems

These platforms provide a level of adaptability, flexibility, and agility that cannot be achieved with traditional AI techniques alone. The flexibility of autonomous systems is especially important in situations where the operating environment is unpredictable and real-time monitoring and control is impractical.

Quantum AI

We are talking about the nascent field at the junction of quantum technologies and neural networks. Research in this area in the future can lead to the emergence of qualitatively new AI algorithms designed to work in quantum systems.

Multi-Agent Systems

AI platforms of this type consist of several independent agents, each of which is able to perceive the environment and perform certain functions. Agents can be AI models, programs, robots and other elements. Several agents are able to work towards a common goal beyond the capabilities of each of the agents individually.

Neuro-symbolic AI

This is a type of artificial intelligence that combines machine learning methods and symbolic systems, for example, knowledge graphs. This approach makes it possible to create more reliable and trustworthy AI models. Neurosymbolic AI eliminates limitations in traditional AI systems, such as incorrect inferences and failure to explain the steps that led to a specific outcome.

Composite AI

This type of system provides for a combination of several artificial intelligence methods for deeper interpretation of data and solving a wide range of business problems. The goal is to create AI solutions that require less data and energy to learn. Composite AI provides greater opportunities for those organizations that do not have access to large amounts of historical or labeled data.

General AI

We are talking about creating software with intelligence similar to human. Such systems will be able to self-learn and perform tasks for which they were not originally intended. General AI without human intervention will be able to solve various problems, including those that arise in humans. In general, the concept provides for the development of AI systems with autonomous self-control, a sufficient degree of self-awareness and the ability to master new skills.

Sovereign AI

This is an attempt by various states to independently develop and implement AI with less dependence on the commercial market. The approach embodies political and cultural differences to achieve sovereign goals. Sovereign AI aims to maximize the value of technology while reducing risks.

AI on the periphery

The concept involves the use of AI in IoT devices, gateways, edge servers, etc. AI algorithms can be integrated into mobile gadgets, vehicles, medical diagnostic equipment, streaming video analytics systems, etc.

Generative AI

These technologies have a significant impact on business operations. GeneAI is able to stimulate innovation, automate creative tasks and provide personalized interaction with customers. Many companies see Genia as a powerful tool for creating content and solving complex problems.^[5]

2023

How artificial intelligence is changing business. 10 trends

Artificial intelligence is changing the IT industry and business operations practices. Worldwide, there has been an explosive increase in interest in generative AI (GenAI) technologies, which allow the creation of text, images and diverse content based on data used to train models. In 2027, spending on various AI solutions is expected to exceed $500 billion, according to an IDC study published on October 26, 2023. Analysts call 10 trends that will change the global business ecosystem.

1. Transforming the IT Industry

IDC expects that the rapid shift in IT spending towards artificial intelligence will affect virtually all industries and applications. By 2025, the world's 2,000 largest companies (G2000) will direct more than 40% of their core IT spending to AI-related initiatives.

AI is changing the IT industry and how business operations are conducted

2. Tipping Point in the IT Industry

The IT industry will feel the influence of AI more than any other area, as almost every company seeks to present products and services based on neural networks, machine learning and large language models. In addition, organizations are actively helping their customers implement AI.

3. Infrastructure turbulence

The level of AI spending for many enterprises will be limited until 2025 due to the transformation of business processes and the redistribution of workloads in enterprise systems and cloud platforms. The current macroeconomic situation will have an impact on the industry.

4. Data arrays

Information is a critical asset in the AI world. Model learning efficiency and application functionality depend on datasets. Developers realize this, and therefore increase investment in data collection platforms in order to gain a competitive advantage.

5. IT Skills Mismatch

A shortage of qualified specialists in artificial intelligence, cloud, data processing and security will negatively affect the attempts of enterprises to succeed in the relevant segments of the IT market.

6. Service Industry Transformation

Generative AI will provoke change in many areas. IDC believes that by 2025, 40% of services, such as risk assessment and IT operations, will use GenAI tools in one form or another. These tools enable the creation of virtual assistants that generate humanoid responses, develop video games with dynamic and evolving content, and even generate synthetic data to train other AI models.

Worldwide, there is an explosive increase in interest in generative AI technologies

7. Unified control

One of the most challenging challenges for IT teams is the development of management platforms, which should cover many different areas: infrastructure operations, business applications and processes, AI systems and data

8. Convergent AI

Analysts believe that organizations should plan, test and implement fully convergent AI solutions that will allow them to develop new services to meet customer needs while saving costs.

9. Peripheral Computing

The introduction of generative AI will allow companies to improve peripheral computing platforms by aligning the results with customer expectations. And this will help to increase the effectiveness of business operations.

10. Satellite systems

Satellite internet constellations will provide broadband everywhere, helping to bridge the digital divide and opening up many new opportunities and business models. By 2028, 80% of enterprises will begin to use satellite communications, creating a single structure of digital services and services.^[6]

Named 6 main AI trends in education

In August 2023, the team of the Intersectoral Center for Technology Transfer and the Research Center in the Field of IIUnnivalence Innopolis prepared an open patent and marketing report "Application of artificial intelligence in priority sectors of the economy." In particular, experts have identified several AI trends in the educational sector.

Help with routine tasks and processing documents/tasks/forms

Here we are talking about the automation of processes: from checking tasks and processing forms to issuing ratings, including based on the results of written work.

Experts highlighted several AI trends in the educational sector

Evaluation of works

AI has been used for years to support student learning and assessment. Using works that are first evaluated by faculty, AI grading systems examine how grading criteria are applied and then apply these criteria at scale. According to experts, only 15 of the noted works can lead to the fact that the AI system will be able to accurately estimate 10,000 works. AI systems can do this for various forms of evaluations, from solving mathematical problems to short or long essays and multiple choices. These tools can also evaluate the same task in different languages without considering external factors such as handwriting, culture, or the language the student chose.

Coaching and Teacher Professional Development

Artificial intelligence can provide feedback on a lesson's success, track a student's progress and warn when performance issues may arise, and identify areas where a teacher can improve teaching.

AI has been used for many years to support learning and assessment of students

Digital assistants

Experts pointed to the possibilities of automating processes using digital assistants. The latter, according to experts, will not completely replace the teacher, but will do what the teacher does not have enough time for. Thanks to digital tools, the evidence-based capabilities of pedagogy increase many times over. First of all, this is a constant included measurement of the student - an operational assessment of the work that he performs, which is very important to increase his motivation. After all, the teacher does not have time to check all the notebooks, because often this is a boring, routine activity, and because of this, the student loses interest in studying, because he does not feel attention to himself or sees that he was assessed according to a template.

Another feature of the digital platforms with which schools begin to work is the generalization of feedback data for the teacher. For example, they show exactly where there are laggards, what topics they did not understand, and the teacher has a different information base for assessment.

Individual training

Artificial intelligence helps to create adaptive training programs. According to the researchers, AI will achieve a level of personalization that is impossible for teachers managing 30 students in the classroom by 2023.

Chat boats

With the chatbot, the teacher can collect statistical information about the student. There is no need to personally write to a student and ask his parents' phone number - all this information is already accumulated in the artificial intelligence system. The chatbot works as a teaching tool. He can offer the student additional tasks, links to the necessary resources. For example, the foreign language learning service Duolingo was one of the first in its field to use chatbots in its application. They understand spoken language and support audio message format in multiple languages. If at some point in the conversation the user has problems expressing his thought, the bot will give him several possible phrases to choose from.

The use of artificial intelligence in priority sectors of the economy

2022: McKinsey names 2 top trends in artificial intelligence

On August 25, 2022, a study was published, according to McKinsey which the application artificial intelligence and implementation of machine learning are the two most significant technological trends in the AI market.

Implementation of application AI

Applied AI, which McKinsey believes is based on proven and developed technologies, has a viable application in more industries and is closer to a state of mass adoption than other trends.

McKinsey named 2 top trends in artificial intelligence

In McKinsey's global survey on the state of AI conducted in 2021, 56% of respondents said their organizations had implemented AI, up from 50% in the 2020 survey. Technology industries are leading the way in implementing AI, according to a 2022 report, and product development and service operations are the business features that have benefited the most from using AI.

We see that everything is moving away from advanced analytics... using machine learning to work with large data sets to solve complex problems in a new way.

This trend is reflected in the rapid growth of AI publications, and not only because AI scientists publish more work, but also because people in a wide variety of fields use AI in their research and advance the application of AI into the future, he explained.

Indeed, there is a shift from science to technical development and scaling, "he said. We see that development in the field of AI is moving quite quickly along this path, and I am very pleased with the fact that more and more things are moving from science to large-scale application.

However, McKinsey's report also highlights a number of key uncertainties that could impact the future of applied AI, including the availability of talent and funding, cybersecurity concerns and stakeholder questions about the responsible and reliable use of AI.

Machine Learning Implementation

According to the McKinsey report, the introduction of machine learning (ML) "implies the creation of a functionally compatible stack of technical tools to automate ML and expand its use so that organizations can fully realize its potential." McKinsey expects ML adoption to spread as more companies look to use AI for a growing number of applications, the report noted.

McKinsey named 2 top trends in artificial intelligence

More broadly, ML includes a view of a technology stack that promotes scaling, which can go as far as innovating at the level of changing microprocessor structures, Roberts said. You see many new possibilities in silicon that support the acceleration of certain types of AI tasks, and these innovations will be used more widely, providing faster and more efficient scaling both in terms of computing resources and in terms of their stability.

The trend also includes integrated hardware and heterogeneous computing used in ML workflows.

We will begin to see more and more venture capital activity and corporate investment as we build a tool system for this new class of software and new class products in the form of specialized services, "he explained.[7]

2018: How artificial intelligence is improving - top trends and obstacles

Main article: How artificial intelligence is improving - the main trends and obstacles

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