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White Paper: Global IT Market Trends
The global artificial intelligence market continues to grow rapidly. Enterprises in all industries are introducing processes based on neural network algorithms. But if earlier attention was paid primarily to experimental projects and the study of opportunities, then in 2026 the stage of scaling the relevant technologies began. AI affected many areas of the IT sphere - from software Internet services to equipment and physical robotics. Analysts Deloitte in mid-April 2026 named five key IT trends in the world - all of them are somehow related to AI.
1. Physical AI
We are talking about the convergence of AI and robotics. Such systems interact with the real world by understanding the physical laws and context of the environment. Unlike "digital" AI, which only works with data, physical AI is able to interact with objects using mechanical drives and manipulators. AI-enabled robots are rapidly gaining popularity in manufacturing, logistics and other areas. The authors of the study believe that in the medium term, such devices are transformed from niche products into products for mass use.
2. Agent AI
These are special software systems that are able to interact with their environment, collect data and use it to independently perform tasks that meet predetermined goals. Deloitte notes that businesses are moving quickly towards agent AI, but many of them face obstacles. Companies are trying to automate existing processes - tasks designed by people and designed for people - without rethinking their essence. The true value, experts believe, lies in redesigning operational processes, not just introducing AI agents into existing infrastructure. That is, it is necessary to create fundamentally new platforms, introduce reliable orchestration systems and develop qualitatively different approaches to management.
3. Rethinking AI Infrastructure
As AI moves from proof-of-concept to production-scale implementation, organizations face the need to overhaul their IT infrastructure. It is necessary to solve issues related to the allocation of computing power, the sovereignty of data, the protection of intellectual property and the provision of fault tolerance. Three-tier hybrid architectures that take advantage of all available deployment options - cloud, local, and peripheral - can help solve problems.
4. Large-scale restructuring of the organization
AI is radically changing the structure and processes of enterprise management. In the future, AI is expected to evolve from an additional tool or efficiency tool into an integrated "digital employee" at every level - from decision-making to product development. The hallmark of the technological organizations of the future will be constant evolution, when changes become a key competence, and not a one-time event.
5. Ensuring security in the age of AI
Companies implementing AI on a large scale face a dilemma: new opportunities provided by neural networks create additional risks in the field. cyber security These are deepfakes, synthetic personalities, social engineering using AI, etc. On the other hand, AI offers powerful tools to counter next-generation vulnerabilities and threats.[1]
The rapid development of artificial intelligence leads to the transformation of many industries. Neural networks are capable of performing routine operations with high efficiency, reducing the burden on company employees and minimizing the number of errors associated with the human factor. AI can process huge amounts of information, revealing hidden patterns, which improves decision-making. In general, AI acts as a powerful tool that empowers humans and improves the quality of life. Gartner specialists in mid-March 2026 identified eight main AI trends in the field of analytics and data management.
1. AI skills
The authors of the study believe that by 2027, in 75% of cases, the process of hiring new employees in the field of data and analytics will include certification and testing for knowledge of artificial intelligence. Those companies that do not pay enough attention to neural network technologies risk lagging behind competitors.
2. Generative and agent AI
According to experts, these technologies will create the first serious competition in 30 years for traditional productivity tools, which will lead to large-scale changes in the market in the amount of $58 billion. In particular, AI agents are able to interact with the environment, collect data and based on them independently identify and perform tasks that allow you to achieve predetermined goals. This improves the efficiency of business operations by automating routine tasks, as well as reducing manual labor costs.
3. AI agents in the physical world
By 2029, AI agents are estimated to generate 10 times more data within real-world physical environments than all AI digital applications combined. This will open up new possibilities for studying patterns, shaping forecasts and modeling.
4. Compliance
By 2030, about half of organizations will begin using autonomous AI agents to transform control policies and technical standards into a machine-readable form. This will help automate compliance processes.
5. AI as a growth driver
By 2030, it is expected that a new wave of unicorn companies will appear (with an estimate of $1 billion or more) with an annual regular income of $2 million per employee. Startups that are initially focused on using AI will be able to demonstrate unprecedented efficiency.
6. Changes to the manual
By 2030, according to Gartner, about 60% of organizations successfully achieving AI differentiation will be led by executives who prioritize improving interpersonal skills.
7. Semantic layers
These are business intelligence systems that provide a simplified and understandable representation of complex data. That is, an intermediate level is formed between technical information and users, on which complex structures (tables, SQL queries) are transformed into understandable business terms. By 2030, according to the authors of the study, universal semantic layers will be considered as a critical infrastructure along with data platforms and cybersecurity.
8. AI risks
Risk mitigation functions are increasingly being integrated into AI and software development and data analysis processes. By 2028, half of the content risk managers will move from the legal and cybersecurity unit to the AI development unit, helping to address the challenges inherent in disparate quality assurance processes.[2]
Artificial intelligence, as it develops, has an increasingly powerful impact on the work of companies in various industries. Neural networks help optimize business processes, reduce the burden on employees, speed up decision-making, increase the efficiency of data use, etc. At the same time, one of the most important areas is agent AI, as stated in the IDC study, the results of which were published at the end of October 2025.
AI agents are specialized software that is able to interact with the environment, collect data and based on it independently identify and perform tasks that allow you to achieve predetermined goals. These goals are set by humans, whereas AI agents perform optimal actions to achieve them without human intervention.
Agent AI provides a number of important benefits. In particular, such systems give companies the opportunity to reduce the costs associated with inefficiency of operations, human errors and manual processes. Sophisticated AI agents use machine learning to collect and process vast amounts of information in real time, helping organizations make better decisions. In addition, the quality of customer service is improving: AI agents allow companies to personalize product recommendations, respond more quickly to requests, and innovate to increase consumer engagement, conversion, and loyalty. All this provides competitive advantages and helps to adapt to market changes.
| Today, organizations operate in conditions of economic and geopolitical uncertainty. Against this background, agent AI turns into a strategically important tool. Our research shows that this new class of AI doesn't just accelerate innovation. It changes the approach to work, transforms people's work and redefines the further development of industries, says Meredith Whalen, director of products and research at IDC. |
Overall, IDC predicts that 45% of organizations will use enterprise-wide agent AI by 2030, incorporating it into all business functions. Analysts highlight six key trends in the use of such AI solutions in the corporate sector.
The development of generative artificial intelligence (GENI) is entering a new phase - from scaling models to increasing their autonomy, independence and mobility. The observed changes affect the architecture of AI systems, ways to integrate them with corporate platforms and data. The development of open ecosystems, standardization of information exchange protocols and increased computing capabilities of user devices have a significant impact on the industry. Experts from the consulting company Yakov & Partners and Yandex in early December 2025 identified key technological trends that determine the further evolution of Genia in Russia.
Multimodal models
Such systems are able to simultaneously perceive and process different types of data - text, images, audio, video and other formats. A key trend in the development of multimodality is the transition to end-to-end models that can work with any data type in a single view space.
Reasoning LLM
AI of this type uses more computations to think through the solution step by step and check its correctness before giving an answer. Instead of increasing the size of the model, quality growth is achieved through a deeper process of reflection. This approach gives a noticeable increase in accuracy in writing code, solving mathematical problems and performing logical reasoning.
Developing and Increasing the Importance of Open-Source Models
The use of open models accelerates the launch of products to market, reduces barriers to business and promotes mass adoption of AI. This is especially important in conditions of limited access to computing resources and sanctions pressure.
Development of agency systems
We are talking about autonomous AI, capable of performing tasks without constant human intervention. Unlike traditional question-answer models, such agents are able to plan actions, use external tools and interact with business systems. This significantly expands the range of practical scenarios - from automating internal processes to creating new client services.
AI Agent Development Platforms
Special tools for adapting models to specific business tasks help you configure the generation of responses across the Internet and knowledge bases, create a voice AI agent, or configure the logic of interaction between the AI agent and external systems.
GeneAI on devices
Thanks to the increase in the power of neuroprocessors (NPUs), the launch of AI models has become possible directly on laptops, smartphones and other user devices, which reduces latency and helps to process data that requires privacy.
Origin of the content
A new standard that automatically adds origin labels to the generated materials, fixing where and how the content was created or changed. This helps combat counterfeiting and comply with legal requirements.
Optimizing the Cost of Inference
Companies focus on reducing the cost of infection, improving algorithms and increasing the energy efficiency of models. This makes it possible to implement Genia faster and cheaper on a mass scale, expanding its application in various sectors.
Mixture of Experts (MoE)
An architectural approach in which, instead of developing one "universal" model, a pool of specialized experts is formed - small neural network subnets (mini-models), each of which is trained on a narrow type of data or a specific subtask.
The transition of AI into the physical world
AI integration into robots and wearable devices has been observed. This allows systems to perceive the environment, make decisions and perform actions in reality.[4]
Artificial intelligence is increasingly used in the field of sales. Neural networks open up new opportunities for companies, but at the same time create additional challenges. Experts believe that in such an environment, the heads of commercial departments will have to rethink their strategies taking into account the introduction of new technologies and change the methods of interaction with customers. At the end of November 2025, Gartner analysts identified three key sales AI trends that will help organizations strengthen their competitive position and optimize operations. Read more here
From smart chatbots and big data platforms to automating all sorts of operations and enabling cybersecurity, artificial intelligence is increasingly being used. Against this background, companies are looking for the most effective ways to use neural networks in their activities. The FinTech Association highlighted seven key trends in the Russian AI market, which TAdviser got acquainted with in early December 2025.
1. Generative AI (Genia) for everything
Genia technologies, as noted by the authors of the study, give impetus to the development of other digital directions. These include development tools (low- and no-code), recommendation systems, immersive platforms, design, etc. At the same time, generative models become more complex and perfect, and also move to multimodality.
| A huge number of options for using generative AI make it a universal technology, which will soon be included in the spheres of life so much that the AI itself as the basis will cease to be something special - it will simply make the usual functions more convenient, analysts say. |
2. AI avatars
We are talking about digital characters designed to imitate the properties of an object or a person. Such avatars are considered as the next stage in the development of "digital twins," since they can combine AI and technologies for simulating a certain system or some process. AI avatars can become part of a company's marketing strategy, such as playing the role of a virtual brand representative.
3. Emotional AI
As natural language processing and generation tools improve, the need to analyze the emotions of interaction participants - customers, employees, managers, etc. Emotion processing can rely on text, audiovisual information, as well as data from portable devices such as smartwatches. The development of emotional AI is expected to help improve understanding of requests and improve client experience. In addition, "emotional" decisions can increase confidence in AI technologies in general.
4. Agent AI
These are specialized systems that can interact with the environment, collect data and based on it independently determine and perform tasks that allow you to achieve predetermined goals. Agents can be AI models, programs, robots and other computing objects. More than a third of enterprise software is expected to include AI agents by 2028. This will make it possible to make at least 15% of decisions autonomously and reduce human participation in complex processes.
5. AI control platforms
Such solutions aim to ensure the reliability, transparency and accountability of AI, as well as compliance with the necessary safety and ethics standards. Experts believe that by 2028, companies using AI management platforms will be able to increase customer confidence by 30%.
6. II-plus
This is a model in which AI becomes a central technology in business. With this approach, investments in the point application of AI are replaced by investments in the full-scale implementation of the corresponding systems.
7. AI at the heart of cybersecurity
The development of neural networks leads to the transformation of the information security sector. AI is able to improve many functions - from detecting anomalies and protecting against fraud to monitoring the perimeter and managing identity and access. Against this background, there is a transition to proactive management of cyber threats, including the introduction of AI tools into all key cybersecurity services.[5]
Against the background of a shortage of accountants, 1C is actively developing AI in its products and services designed to perform accounting functions. Boris Nuraliev, founder and director of the company, spoke about this at the annual 1C business forum in November 2025. Read more here.
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.[6]
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.
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.[7]
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. |
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.[8]
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.[9]
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.[10]
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.
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.
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.
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.[11]
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.
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.
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
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.
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.
| 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.[12] |
Main article: How artificial intelligence is improving - the main trends and obstacles
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