{"id":23984,"date":"2026-03-09T15:30:28","date_gmt":"2026-03-09T08:30:28","guid":{"rendered":"https:\/\/fpt-is.com\/en\/?post_type=goc_nhin_so&p=23984"},"modified":"2026-04-06T15:58:58","modified_gmt":"2026-04-06T08:58:58","slug":"ai-development-trends-in-2026","status":"publish","type":"goc_nhin_so","link":"https:\/\/fpt-is.com\/en\/insights\/ai-development-trends-in-2026\/","title":{"rendered":"AI Development Trends in 2026"},"content":{"rendered":"

Executive Summary<\/b><\/span><\/h1>\n

The year 2025 was filled with major events in the AI industry. The DeepSeek event at the beginning of the lunar year caused significant disruption in the AI market, wiping out nearly $600 billion of Nvidia\u2019s market capitalization, while Meta had to establish a \u201cwar room\u201d to analyze the Chinese model that was reshaping the competitive landscape. This was followed by the rapid development of coding agents (agentic coding): AI models and systems with outstanding programming capabilities that are fundamentally changing the way software is developed. The year 2025 was also when Google made a strong comeback to reclaim its position as a leading giant in the AI industry with a series of new AI models, products, and services deeply integrated into its ecosystem. The vibrant development of AI throughout 2025 is an indication of the breakthroughs expected in the AI industry in 2026. In this article, we compile and select predictions about the major development trends in AI in 2026 and provide recommendations for enterprises and organizations to overcome challenges and seize opportunities brought by AI.<\/span><\/p>\n

1. Major AI trends in 2026<\/b><\/span><\/h2>\n

The AI industry is expected to continue growing at a very rapid pace in 2026. To identify the main AI trends for 2026, the author relies on academic research findings, market analysis reports, insights from leading experts in the field, as well as personal observations and experience.<\/span><\/p>\n

The most notable and influential AI trends in 2026 include the continued prominence of Agentic AI as the keyword of the year, with autonomous and multi-agent AI systems being developed and applied more widely in enterprises and organizations. Domain-specific reasoning models will be increasingly developed and adopted. Security for information systems in the era of agentic AI will receive greater attention. Scaling post-training and inference will become the dominant strategy instead of scaling pre-training. Physical AI will achieve technological breakthroughs and be applied more widely in manufacturing and everyday life. At the same time, countries will place increasing emphasis on AI sovereignty, accelerating efforts to master AI technologies and infrastructure.<\/span><\/p>\n

1.1 Agentic AI<\/b><\/span><\/h3>\n

Agentic AI represents the evolution from passive assistants that simply answer questions to autonomous systems capable of analyzing goals, planning multi-step actions, controlling tools, and adjusting their behavior with minimal human intervention. While traditional AI agents usually handle a relatively clear set of tasks, agentic AI systems demonstrate a higher degree of autonomy: they coordinate multiple agents, data sources, and tools to execute entire workflows and optimize outcomes based on context rather than following predefined scripts. One of the most prominent applications of Agentic AI is in software development, where multi-agent AI systems can autonomously plan tasks, break down problems, write code, test, fix bugs, and iterate until the objective is completed.<\/span><\/p>\n

According to a report by <\/span>McKinsey<\/span><\/a>, 62% of enterprises experimented with agentic AI in 2025. The <\/span>Protiviti AI Pulse Survey<\/span><\/a> predicts that more than 68% of enterprises will integrate autonomous or semi-autonomous AI into their operations by 2026. Meanwhile, <\/span>Gartner<\/span><\/a> forecasts that by the end of 2026, 40% of enterprise applications will integrate task-oriented AI agents, a sharp increase from less than 5% in 2025, indicating that most organizations will adopt AI agents in their application architectures. However, <\/span>Gartner<\/span><\/a> also warns about the risk of over-expectation: more than 40% of agentic AI projects could be canceled before the end of 2027 due to expectations exceeding current technological capabilities, high costs, and difficulties in controlling agent behavior.<\/span><\/span><\/p>\n

When considering the capabilities and readiness of multi-agent systems, in 2026 we will likely see advances in three areas: improved foundation models for planning and reasoning across sequences of actions; increasingly standardized tooling including search, knowledge retrieval (RAG), office task execution, and enterprise API integration; and the emergence of industry standards for observability, guardrails, and evaluation of agent behavior.<\/span><\/p>\n

Regarding architecture, a common pattern is the \u201corchestrator-worker\u201d model: a lead agent acts as the orchestrator, assigning tasks to specialized agents running in parallel for activities such as searching, analysis, writing, and verification, before aggregating and self-evaluating the final output. <\/span>Anthropic<\/span><\/a> has clearly described this type of multi-agent architecture in their research system. Other common components of agentic AI systems include memory and state management, knowledge repositories using RAG, tool adapters, and policy or permission layers designed to restrict risky actions.<\/span><\/span><\/p>\n

Finally, the role of humans in agentic AI systems does not diminish but changes in nature. Humans shift from execution roles to designing goals, monitoring processes, and intervening when necessary. People define KPIs, set action boundaries, approve sensitive steps through human-in-the-loop mechanisms, and remain responsible for data governance, transparency, and compliance. If 2026 marks the expansion of agentic AI adoption in enterprises, competitive advantage will belong to organizations capable of combining the autonomy of AI agents with operational discipline and human accountability.<\/span><\/p>\n

1.2 Domain-specific small models<\/b><\/span><\/h3>\n

The year 2026 is expected to witness a clear trend: instead of relying solely on massive general-purpose foundation models, many organizations will adopt domain-specific small language models (SLMs) tailored for particular industries.<\/span><\/p>\n

Several reasons explain this shift. The first is economic. Very large models require extremely expensive computational infrastructure, high-end GPUs, and significant inference costs. In enterprise environments where AI is deployed at large scale and runs continuously, these costs become a major barrier to adoption. According to the <\/span>State of AI report by McKinsey<\/span><\/a>, optimizing costs and ensuring return on investment are top priorities when enterprises expand AI into real operations. Instead of using one large model for every task, deploying multiple specialized small models can reduce latency, save computing resources, and provide better budget control.<\/span><\/span><\/p>\n

The second reason is performance efficiency. When trained or fine-tuned on domain-specific datasets such as finance, law, or healthcare, smaller models can achieve performance comparable to large models within a narrower scope. NVIDIA\u2019s article <\/span>\u201cSmall Language Models are the Future of Agentic AI\u201d<\/span><\/a> emphasizes that SLMs deliver strong performance in specific domains, consume less energy, and are easier to deploy in enterprise environments. The <\/span>Stanford AI Index 2024<\/span><\/a> also highlights that smaller models are gradually narrowing the performance gap with large models thanks to improved training and fine-tuning techniques.<\/span><\/span><\/p>\n

In agentic AI systems, this trend becomes even more important. Instead of relying entirely on a single massive LLM, modern multi-agent architectures allow each agent to use the model best suited for its role. A legal agent may use a model trained on legal texts, a financial analysis agent may rely on a model optimized for accounting data, and a knowledge retrieval agent may use a small model combined with RAG. This \u201cright model for the right task\u201d approach helps optimize costs, improve accuracy, and reduce the risk of propagating errors across the system.<\/span><\/p>\n

\"Xu<\/a><\/p>\n

Figure 1: Multi-agent AI architecture of Anthropic’s multi-agent deep research system. Reference: https:\/\/www.anthropic.com\/engineering\/multi-agent-research-system<\/span><\/i><\/span><\/p>\n

In summary, the year 2026 marks a shift from the mindset that \u201cbigger is better\u201d toward a multi-model strategy in which domain-specific small models play a central role in building practical, cost-efficient, and sustainable AI architectures for enterprises.<\/span><\/p>\n

1.3 Strengthening information system security in the era of agentic AI<\/b><\/span><\/h3>\n

In 2026, as autonomous agentic AI systems begin to be deployed at production scale, security becomes not merely an option but a fundamental requirement. Unlike traditional chatbots that only answer questions, agentic AI systems are granted a higher level of autonomy. They may access internal databases, read and write enterprise documents, call financial APIs, send emails, and execute operational processes through various tools. This \u201cgranting of action authority\u201d significantly expands the attack surface. Gartner predicts that more than 40% of agentic AI projects may be canceled before 2027 due to concerns about risk, cost, and security. This demonstrates that security is becoming a real barrier to the commercialization of agentic AI.<\/span><\/p>\n

The biggest challenge lies in controlling agent access rights and behavior. When an AI agent can query sensitive data or execute transactions, the risks of information leakage, privilege escalation, or exploitation through prompt injection become more serious. In multi-agent environments, risks become even more complex because agents can interact with one another and exchange information, increasing the possibility of cascading errors or coordinated attacks.<\/span><\/p>\n

For this reason, guardrails become a mandatory requirement. OpenAI\u2019s document \u201cA Practical Guide to Building Agents\u201d emphasizes the importance of strict access control mechanisms, tool sandboxing, logging, and real-time behavioral monitoring. Zero Trust models, human-in-the-loop mechanisms for sensitive actions, and regular evaluation and red-teaming are gradually becoming standard practices for deployment. At the same time, AI governance standards such as <\/span>ISO\/IEC 42001:2023<\/span><\/a> emphasize the need for risk management and accountability in AI systems.<\/span><\/span><\/p>\n

Concerns about attacks powered by AI agents became a reality in late 2025. One notable case involved an attack that used a jailbroken version of Anthropic\u2019s Claude Code to automatically identify sensitive infrastructure and extract data with minimal human intervention, achieving an autonomy level of up to 80-90%.<\/span><\/p>\n

In summary, when agentic AI systems are granted autonomy and real-world operational capabilities, security can no longer be treated as an external layer but must be built directly into the system architecture. Although strict security requirements may initially slow adoption, they will ultimately determine the sustainable scalability of agentic AI systems in enterprise environments.<\/span><\/p>\n

1.4 Scaling post-training and inference<\/b><\/span><\/h3>\n

The year 2026 will continue to witness an important shift in AI model development strategies: from scaling pre-training toward scaling post-training and inference.<\/span><\/p>\n

Pre-training refers to the process of training large language models (LLMs) on massive datasets using self-supervised learning, enabling models to learn language structures and general world knowledge. Post-training, on the other hand, includes processes such as fine-tuning, alignment, reinforcement learning from human feedback (RLHF), or additional training on specialized datasets so that the model becomes suitable for a particular domain, task, or safety requirement. If pre-training provides the model with foundational capabilities, post-training makes the model practically useful in real-world applications.<\/span><\/p>\n

This trend is reinforced by the view of AI expert Ilya Sutskever – co-founder of OpenAI, who argues that the era of \u201cinfinite pre-training scaling\u201d is gradually reaching its limits, and the AI industry must focus more on reasoning and post-training methods to achieve the next leap in capability. According to him, improvements in how models think and execute multi-step reasoning may bring greater progress than simply increasing data and parameters.<\/span><\/p>\n

This observation is particularly relevant as agentic AI systems become increasingly widespread. When a system must perform multi-step workflows such as planning, retrieving information, analyzing data, calling APIs, verifying results, and making adjustments, the ability to perform iterative reasoning and allocate more computation during inference time becomes more important than adding billions of additional parameters through pre-training. Recent studies suggest that allowing models to \u201cthink longer\u201d or break down problems into multiple reasoning steps can significantly improve output quality without increasing model size.<\/span><\/p>\n

As discussed in previous sections, the trend toward using domain-specific small models in multi-agent systems also increases the demand for post-training. Instead of relying on a single massive model, enterprises deploy multiple smaller models deeply fine-tuned on datasets from specific industries such as finance, law, or healthcare in order to optimize performance and cost. This means that more resources will be allocated to fine-tuning, alignment, and evaluation of model quality.<\/span><\/p>\n

Finally, as enterprises increasingly focus on cost optimization, hardware optimization for inference becomes a strategic priority. Techniques such as model quantization, distillation, and KV caching help reduce operational costs while maintaining model performance. As a result, 2026 is expected to become a period of competition in post-training efficiency and inference optimization, where practical execution capability in real-world environments matters more than the sheer size of a model.<\/span><\/p>\n

1.5 Physical AI opens a new economic era<\/b><\/span><\/h3>\n

The trend of Physical AI is emerging as the next major step following the wave of agentic AI. While agentic AI allows software systems to autonomously plan and execute tasks in digital environments, Physical AI extends these capabilities into the physical world. According to Jensen Huang, the technology industry will move into the era of Physical AI after the era of agentic AI, an era in which artificial intelligence not only operates on screens but also controls robots, autonomous vehicles, and electromechanical systems in real life. He has repeatedly emphasized that the convergence of AI, physical simulation, and humanoid robotics will form the foundation of a new industrial revolution.<\/span><\/p>\n

In this stage, humanoid robots and specialized robots integrated with AI are becoming increasingly intelligent thanks to their ability to perceive environments, plan actions, and continuously learn from experience. AI does not merely allow robots to \u201csee\u201d and \u201chear\u201d; it also enables them to understand context, predict motion, and coordinate complex multi-step actions, similar to how agentic AI operates in digital environments.<\/span><\/p>\n

\"Ai<\/a><\/p>\n

Figure 2: CEO talks about the future of AI. Image source: https:\/\/www.rcrwireless.com\/20250112\/ai-ml\/nvidia-takes-on-physical-ai-for-automotive-industrial-robotics<\/span><\/i><\/span><\/p>\n

Asian countries such as China and Japan are accelerating investments in AI-integrated robotics. China considers intelligent robotics a key pillar in its strategy to upgrade industrial manufacturing, particularly in highly automated factories. Japan, facing the challenge of an aging population, focuses on developing robots that support elderly care and daily-life services. The integration of AI with robotics helps reduce dependence on manual labor while simultaneously improving productivity and service quality.<\/span><\/p>\n

One important factor driving Physical AI is the development of <\/span>world models<\/span><\/a>, which are the models that allow AI systems to simulate environments, reason spatially, and predict how physical environments behave. According to <\/span>Arm\u2019s 2026 Tech Predictions<\/span><\/a>, world models will play a central role in training and testing robots within simulated environments before deploying them in the real world. This approach shortens development cycles, reduces risks, and optimizes costs.<\/span><\/span><\/p>\n

In summary, Physical AI marks the transition from \u201cAI that can think\u201d to \u201cAI that can act in the real world.\u201d As humanoid robots, digital simulation, and AI converge, the boundary between the digital and physical worlds will increasingly blur, opening a new era for manufacturing, services, and everyday life.<\/span><\/p>\n

1.6 AI sovereignty<\/b><\/span><\/h3>\n

In the context of the global race in artificial intelligence, AI sovereignty is becoming a central strategic priority for many nations. It is not merely a technological issue but also a matter of national security, economic competitiveness, and political strategy. AI sovereignty refers to a nation\u2019s ability to control and operate the entire AI stack, from infrastructure, data, and models to operational policies, rather than depending on foreign technologies or multinational corporations. This approach aims to strengthen national decision-making capacity, protect sensitive data, and maintain competitive advantages in the digital era.<\/span><\/p>\n

AI sovereignty is reshaping investment decisions worldwide. According to a <\/span>report by IBM<\/span><\/a>, 93% of executives now consider AI sovereignty a strategic priority for their organizations. In addition, the trend of \u201cdata geopatriation\u201d, requirements for storing data within national borders, is creating new opportunities for domestic AI service providers. By 2030, 75% of enterprises in Europe and the Middle East are expected to comply with regulations related to data sovereignty.<\/span><\/span><\/p>\n

Experts believe this shift stems from growing concerns about cybersecurity, data residency laws, and geopolitical tensions. By owning a sovereign AI ecosystem, countries can ensure that sensitive data belonging to citizens and businesses is not transferred to platforms located outside national borders.<\/span><\/p>\n

In summary, the trend of AI sovereignty reflects a shared recognition that AI is not merely a technical tool but a strategic national asset. Investing in sovereign AI capabilities allows countries to reduce dependency on foreign partners, protect sensitive data, and build a foundation for sustainable growth in the 21st century.<\/span><\/p>\n

2. Preparing for AI trends in 2026<\/b><\/span><\/h2>\n

2.1 What enterprises need to prepare to embrace AI trends in 2026<\/b><\/span><\/h3>\n

Entering 2026, AI is no longer an experimental technology but is becoming core infrastructure for both enterprises and nations. To prepare for and stay ahead of AI trends, organizations need a strategic approach to building sustainable internal capabilities.<\/span><\/p>\n

First, enterprises must shift from fragmented AI applications toward a comprehensive AI architecture. Agentic AI, domain-specific small models, and inference optimization are transforming how AI solutions are designed. The key is to clearly define business problems and select the right models rather than simply pursuing the largest models available. Investment in post-training, domain-specific datasets, and system integration capabilities will deliver more practical value than focusing solely on pre-training.<\/span><\/p>\n

Second, data and infrastructure are becoming strategic foundations. AI sovereignty, multi-layer security, and inference cost optimization are essential requirements when AI is deployed at a large operational scale. Enterprises must standardize data governance, implement access control mechanisms, and design observability systems for agentic AI architectures.<\/span><\/p>\n

Third, the human factor remains central. Training teams to effectively collaborate with AI, building a culture of controlled experimentation, and establishing human-in-the-loop mechanisms for critical decisions are essential for reducing risks. AI does not replace humans; rather, it enhances decision-making capability and accelerates the execution of tasks.<\/span><\/p>\n

Finally, preparing for AI trends in 2026 is not simply a technological race but a competition in integration capability, cost optimization, and risk management. Organizations that build strong foundational capabilities will be the ones leading the era of agentic AI and physical AI.<\/span><\/p>\n

2.2 FPT supporting enterprises and organizations in AI transformation<\/b><\/span><\/h3>\n

FPT accompanies enterprises and organizations throughout their AI transformation journey, from building strategic vision to real-world deployment and operation. Beyond providing technology solutions, FPT acts as a strategic advisor, helping leadership identify the right priority problems, assess readiness in terms of data, infrastructure, and workforce, and design AI transformation roadmaps tailored to each industry. With experience in sectors such as finance and banking, telecommunications, manufacturing, healthcare, and the public sector, FPT supports the design of modern AI architectures integrating agentic AI, industry-specific models, and intelligent data platforms.<\/span><\/p>\n

In addition, FPT places strong emphasis on security, data sovereignty, and operational cost optimization, key challenges when AI systems are deployed at production scale. Enterprises are supported in building AI transformation capabilities through training programs, technology transfer, standardized AI governance processes, and transparent monitoring mechanisms. FPT AI Factory possesses powerful NVIDIA GPU infrastructure, enabling AI systems to be deployed on domestic infrastructure while ensuring AI sovereignty and data sovereignty without requiring enterprise data to be sent to foreign platforms outside national borders. As a long-term strategic partner, FPT is committed to helping organizations implement AI transformation in a practical, secure, and value-driven manner.<\/span><\/p>\n

 <\/p>\n\n\n\n
Exclusive article by a Technology Expert from FPT IS, FPT Corporation<\/b><\/span><\/p>\n


\n<\/b>Pham Quang Nhat Minh<\/b><\/em><\/span><\/p>\n

Director of the Artificial Intelligence Research and Development Center (FPT IS AI R&D Center)<\/b><\/span><\/em><\/p>\n

PhD in Information Science and a specialist in Natural Language Processing (NLP), with 18 years of research and development experience in both academic and industrial environments. He is the author and co-author of numerous scientific publications in the field of natural language processing. His current research focuses on large language models and their applications.<\/span><\/em><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

References<\/b><\/span><\/h2>\n
    \n
  1. 15 AI Trends That Will Reshape Enterprises in 2026, siggan blog. Link: <\/span>https:\/\/sisgain.com\/blogs\/ai-trends<\/span><\/a><\/span><\/li>\n
  2. The trends that will shape AI and tech in 2026. IBM Think. Link: <\/span>https:\/\/www.ibm.com\/think\/news\/ai-tech-trends-predictions-2026<\/span><\/a><\/span><\/li>\n
  3. How we built our multi-agent research system. Anthropic Blog. Link: <\/span>https:\/\/www.anthropic.com\/engineering\/multi-agent-research-system<\/span><\/a><\/span><\/li>\n
  4. A Practical Guide to Building Agents. OpenAI. Link: <\/span>https:\/\/cdn.openai.com\/business-guides-and-resources\/a-practical-guide-to-building-agents.pdf<\/span><\/a><\/span><\/li>\n
  5. OpenAI cofounder Ilya Sutskever says the way AI is built is about to change. The Verge. Link: <\/span>https:\/\/www.theverge.com\/2024\/12\/13\/24320811\/what-ilya-sutskever-sees-openai-model-data-training<\/span><\/a><\/span><\/li>\n
  6. Is China Leading the Robotics Revolution? Link: <\/span>https:\/\/chinapower.csis.org\/china-industrial-robots<\/span><\/a><\/span><\/li>\n
  7. ARTIFICIAL INTELLIGENCE BASIC PLAN. Link: <\/span>https:\/\/www8.cao.go.jp\/cstp\/ai\/ai_plan\/aiplan_eng_20260116.pdf<\/span><\/a><\/span><\/li>\n
  8. Disrupting the first reported AI-orchestrated cyber espionage campaign. Link: <\/span>https:\/\/www.anthropic.com\/news\/disrupting-AI-espionage<\/span><\/a><\/span><\/li>\n
  9. Stanford AI Experts Predict What Will Happen in 2026. Link: <\/span>https:\/\/hai.stanford.edu\/news\/stanford-ai-experts-predict-what-will-happen-in-2026<\/span><\/a><\/span><\/li>\n
  10. What\u2019s next in AI: 7 trends to watch in 2026. Link: <\/span>https:\/\/hai.stanford.edu\/news\/stanford-ai-experts-predict-what-will-happen-in-2026<\/span><\/a><\/span><\/li>\n<\/ol>\n","protected":false},"author":21,"featured_media":23990,"parent":0,"template":"","nang_luc":[],"danh_muc_goc_nhin_so":[],"dich_vu":[],"linh_vuc":[],"platform":[],"san_pham":[],"the_goc_nhin_so":[],"class_list":["post-23984","goc_nhin_so","type-goc_nhin_so","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/goc_nhin_so\/23984","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/goc_nhin_so"}],"about":[{"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/types\/goc_nhin_so"}],"author":[{"embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/users\/21"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/media\/23990"}],"wp:attachment":[{"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/media?parent=23984"}],"wp:term":[{"taxonomy":"nang_luc","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/nang_luc?post=23984"},{"taxonomy":"danh_muc_goc_nhin_so","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/danh_muc_goc_nhin_so?post=23984"},{"taxonomy":"dich_vu","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/dich_vu?post=23984"},{"taxonomy":"linh_vuc","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/linh_vuc?post=23984"},{"taxonomy":"platform","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/platform?post=23984"},{"taxonomy":"san_pham","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/san_pham?post=23984"},{"taxonomy":"the_goc_nhin_so","embeddable":true,"href":"https:\/\/fpt-is.com\/en\/wp-json\/wp\/v2\/the_goc_nhin_so?post=23984"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}