At the end of the year, we usually look back and try to guess what's next. Businesses wrap up their finances, experts share their views, and the market sets its hopes. We examine trends not out of curiosity, but out of pragmatic necessity: to understand what should be added to the strategy, where efforts are worth investing, and where focus should, conversely, be reduced.
2026 is not about “starting a new life on Monday.” It is about inflection points that will define the next decade of technological development.
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- AI: From a Tool to a Partner
AI is still the trend everyone's waiting for, and sometimes its race can be scary, but also the one most people get wrong. Microsoft articulates the core idea clearly: AI does not replace humans — it amplifies what people can achieve together.
It is 2026 that is expected to mark the transformation of AI from a tool into a full-fledged partner — one that not only executes tasks, but also helps shape them and find solutions.
The first waves of AI focused on automating routine processes and personalization (marketing, support, coding). The next level is context and domain understanding. For example, in healthcare, AI becomes a lab assistant; in marketing, a junior marketer; in development, a programmer.
This is shaping a new labor market reality: entry at the junior level becomes more challenging, while value shifts toward mid-level expertise, creativity, and strategic thinking.
This is not a future to be feared. It is a future in which the key skill is the ability to work with artificial intelligence, combining the strengths of humans and machines. Technology exists for people — and this principle remains unchanged.
- The Computational Reality: More Capacity, Not Less
Despite popular narratives around edge AI, the reality looks different.
By 2026, the deployment and operation of AI models will consume up to two-thirds of total AI infrastructure computing capacity. The core logic of inference will remain concentrated in data centers and on enterprise servers.
This includes:
— new data centers with a combined value approaching half a trillion dollars;
— energy-intensive AI chips with a total value exceeding $200 billion;
— specialized inference chips, which, while optimized, are not necessarily less energy-consuming.
2026 is not the year of “cheap AI.” It is the year of infrastructure concentration.
- AI Agents: From Automation to Trust
AI agents are a logical continuation of the broader trend, yet significant enough to warrant separate focus. They are evolving from tools into workplace colleagues to whom increasingly complex functions can be delegated.
However, the real challenge is not scale, but trust and security. Delegation is only possible when:
— the agent has a clear identity;
— access to systems and data is strictly limited;
— there is control over the data it generates;
— built-in protection against malicious actors is in place.
In other words, trust is the currency of innovation, and 2026 will only confirm this sentence.
Security becomes embedded, autonomous, and ambient — not “an add-on at the end.” Ironically, but inevitably, malicious actors will use AI as well — and AI agents themselves will become the primary instruments of defense.
- Confidential Computing: The Foundation of the Agent Economy
The growth of AI is impossible without the expansion of confidential computing. This refers to secure processors and trusted execution environments that enable encrypted data to be processed without being exposed.
This is a security trend that scales in direct proportion to AI adoption. The technology relies on secure processors — hardware-based trusted execution environments — to isolate sensitive data while it is being processed in encrypted form, effectively creating fully encrypted environments for both storage and computation. Cloud providers such as Microsoft, Google and Amazon are adopting confidential computing, with trust emerging as a new currency of innovation.
Without these changes, progress in agent-based systems is not possible. As AI continues to take on more functions, it introduces risks that organizations must be able to manage in real time. This transition will be gradual, because change takes time — and trust takes time as well. Not only between people, but between technologies.
These shifts will happen incrementally. Trust requires time — both between humans and between systems.
- AI in Science: An MVP Approach to Discovery
AI's been helping out in science for a while, but things are really gonna speed up around 2026. AI will come up with ideas, test them out with simulations, manage experiments, and basically work side-by-side with scientists like another member of the team. This mirrors a “wipe coding” approach, but applied to scientific research:
— the researcher defines the hypothesis;
— AI conducts initial experiments;
— the team becomes involved only once the hypothesis proves viable.
The result is faster selection of promising research directions and a significant reduction in time spent at the early stages of discovery.
- Robotics, Drones, and Humanoids: Awaiting the Inflection Point
By 2026, there could be 5.5 million industrial robots worldwide, according to Statista, but still, robot sales haven't grown since 2021. Things might really change around 2030. By then, we might see a million new robots shipped each year, which is double what it is now.
This growth would be driven by two key catalysts: labor shortages in specialized industrial applications across developed economies and the exponential growth of computing power alongside the emergence of specialized AI foundation models.
Robots may expand across industries and use cases, including autonomous drones. However, if the broader ecosystem of technology, artificial intelligence, and robotics fails to address bottlenecks related to data quality, system integration, and cybersecurity, the industrial robotics market may continue to experience relatively modest annual growth.
- The Fragility of Semiconductor Supply Chains
As trade restrictions on next-generation AI chip technologies continue to expand, leaders must adapt quickly to make supply chains more resilient.
In the past, manufacturing cutting-edge chips already required navigating fragile supply chains. Today, the stakes are significantly higher.
By 2026, bottlenecks may extend beyond EUV lithography to include:
— software tools;
— materials;
— highly specialized manufacturing processes.
Dependence on a limited number of suppliers is pushing governments to impose trade barriers, further complicating the global AI ecosystem.
- Standardizing AI “IQ”
The AI industry still lacks a unified standard for evaluation. While there are individual benchmarks, they only cover limited aspects of AI performance, so comparisons are far from comprehensive. The absence of standardization across enterprise solutions remains a challenge.
MIQ (Machine Intelligence Quotient), developed in 2024, could become the first holistic approach, combining:
— reasoning;
— accuracy;
— efficiency;
— explainability;
— adaptability;
— speed;
— ethical compliance.
AI is no longer merely a “transformational technology.” In recent years, it has continuously amazed us, and this trend will persist. However, it is increasingly important to assess AI using comprehensive metrics — reasoning ability, accuracy, efficiency, explainability, adaptability, speed, and adherence to ethical standards — unified into a single evaluation framework.
Standardization will enable organizations to evaluate their own solutions and compare competing systems. Odd as it may sound, standardization does more than create order — it drives further improvement.
Standardized benchmarks will also help AI providers optimize their solutions, knowing that companies will monitor these benchmarks and use them to make informed decisions about AI procurement and deployment.
Standardization is not a brake; it is a catalyst for development, allowing solutions to be compared and informed business decisions to be made.
- Multimodal AI
Human communication is multidimensional. To be our best assistant, AI must be multimodal, integrating text, voice, images, video, and sound to understand context, not just language.
It is only a matter of time before we all use AI as a full-fledged assistant. The challenge for 2026 is ensuring that people can interact effectively with AI systems. Human–machine interaction can take many forms, including text, voice, images, video and sound. Typical single-modal AI systems are limited to one type of input. The problem with single-modal input is context: human interaction is often subtler and more complex than written words, encompassing body language, vocal intonation and facial expressions.
The outcome:
— more natural interaction;
— improved UX;
— more ethical and balanced decisions.
The multimodal AI market is expected to grow from $1.6 billion in 2024 to $27 billion by 2034. Multimodal AI represents an opportunity to enhance AI capabilities — it cannot be ignored and will remain a key trend.
- Regulation, Sovereign Clouds, and “Invisible AI”
AI regulation is increasingly a reflection of geopolitics. The EU, Canada, and other countries are moving toward sovereign control over AI infrastructure. Sovereign clouds are emerging, with the IaaS market projected to reach $169 billion by 2028.
At the same time, businesses are adopting the concept of “invisible AI,” where GenAI is deeply integrated into products and services and ceases to be a standalone object of attention.
Analyzing these trends, it becomes clear that AI in 2026 will not be about hype, but about value: standardization, full project execution, and measurable outcomes. Progress will be defined not by flashy models, but by fundamental, systemic work.
2026 is not another wave of AI excitement
It is going to be the year when promises align with reality and technology becomes not a hype, but the infrastructure of growth, efficiency, and innovation.
P.S. If you want to start 2026 with a true business transformation powered by AI and optimized processes, partner with Muteki Group as your IT experts. We help you develop AI solutions from scratch or implement the right existing tools if they are already available on the market. From strategy to execution, we guide you every step of the way.
Together, we can achieve everything — and even more.
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