What AI Trends Will Dominate in 2026, and Where Is the Technology Heading?

By 2026, AI is entering a new phase – more challenging, more pragmatic, and far more large-scale. The market has shed its illusions, money is being counted more carefully, and companies are asking a simple question: Where is the real business value here?

All the key trends converge in one spot: AI is ceasing to be a tool and is becoming infrastructure.

From LLMs to agent systems

One of the key trends already shaping the industry today is agentic AI. It is evolving from an auxiliary tool into a full-fledged enterprise solution widely used by large companies. This is the next stage after the classic LLMs used for text generation, analytics, and other standard tasks.

Historically, such technologies remained inside large corporations for a long time and were almost invisible to the general public. Companies like Google and Facebook used them long before the term LLM became common. Ten years ago, while working at an international software company, we developed and used such systems ourselves, though we called them Data Processing AI rather than LLMs.

The turning point came with the democratization of artificial intelligence. The emergence of ChatGPT, Gemini, and similar products made AI a mass-market tool, which triggered a sharp increase in interest and investment. However, the market quickly hit a limit: within a short period, nearly all obvious use cases had already been implemented.

Most startups of that era did not build their own models but created so-called wrappers – interfaces on top of existing LLMs. These solutions rapidly lost their value because the base models provided the same functionality directly, without the need for separate applications.

This era lasted about a year. Billions of dollars were invested in such products, after which it became clear that expectations had been overstated.

It was against this backdrop that the shift toward agent systems began. AI agents represent a more complex architecture in which several specialized models interact with one another, distributing tasks and coordinating actions. This approach enables the handling of complex scenarios from travel planning to managing business processes, and marks the next stage in the evolution of AI.

Market consolidation and why only the giants will survive

We are already seeing that the AI-agent market has effectively passed through a consolidation phase. A limited group of major players, roughly a dozen companies, has emerged, quickly taking dominant positions.

This process largely mirrors the history of the email services market, which eventually came under the control of Microsoft, Google, and Yahoo. A similar dynamic is unfolding in agentic AI: key solutions are being developed by companies such as Cohere, OpenAI, and Google. They will steadily displace not only new entrants but also smaller players who previously captured niche segments.

Today, major providers’ focus has shifted toward the enterprise segment. Throughout 2025, they actively deployed agent systems in large organizations, starting with applied tasks such as customer support, internal knowledge bases, employee training, and document workflow automation. A typical scenario involves analyzing corporate materials and building intelligent assistants that can answer complex questions without human specialists. For example, all technical materials of a platform like Keylabs might be processed, enabling a bot to answer any technical question without needing live experts.

Scaling is the next step on this journey. In the near future, enterprise clients will be offered increasingly comprehensive packages: from accounting and legal support to operational process management. The human role will shift toward oversight and final decision-making, while AI agents will handle routine tasks.

The same applies to other corporate functions. For instance, in large banks with thousands of employees, AI agents can take over travel organization, ticket management, and itinerary changes, replacing external services and contractors.

Once major providers begin offering the full spectrum of such services in a single integrated package, from a travel agent to financial and legal assistance, specialized startup providers will become uncompetitive.

Large players don’t need to conquer the market from scratch: they will expand horizontally, progressively covering more and more business processes inside enterprise organizations.

Which industries are most sensitive to AI and automation

When we talk about technology in general, it’s already clear that digital tools and AI are reshaping workflows in the legal sector. Many companies are seeing reduced demand for traditional legal services, primarily due to the automation of routine operations. This applies to both small organizations and large corporations, while the financial sector, particularly banks, continues to adopt new technologies more conservatively.

It is essential, however, to distinguish between legal practice and the judicial system. In court proceedings, where an attorney represents and defends a client’s interests, the human role remains essential. Despite experiments with using AI in judicial practice, humans will continue to make decisions and construct legal arguments in court for the foreseeable future, at least for the next several decades.

The situation is entirely different in corporate law. Nearly every business operation involves legal documentation from NDAs and basic contracts to project documentation. Previously, drafting and approving these contracts required significant time and multiple rounds of comments from legal teams on both sides.

Today, these processes are increasingly optimized with AI tools and LLMs. AI helps quickly identify contentious or sensitive clauses, suggest revisions, and ensure documents comply with a company’s internal requirements. As a result, the approval cycle is significantly shortened, and the lawyer’s role is shifting toward oversight, strategic risk assessment, and final decision-making.

Similar changes are taking place in the financial sector. In tasks such as tax and financial reporting, which are governed by strict rules and regulations, AI has proven especially effective. Many companies already use such solutions to automate calculations, prepare reports, and improve operational accuracy.

Ultimately, technology is not so much replacing specialists as transforming the nature of their work: routine operations are automated, while the focus shifts to analytical, managerial, and strategic tasks where human expertise remains critically important. I observed this very clearly in 2025 in Keymakr client requests: we saw a significant number of inquiries related to data solutions in the financial and legal industries.

Looking ahead to 2026, all deterministic processes will gradually transition to agentic AI systems. By deterministic, I mean tasks governed by strict rules: laws, regulations, financial procedures, and compliance. In this context, the next logical direction of development will be cybersecurity.

Cybersecurity as the reverse side of AI automation

As the volume of available data grows and circulates more actively between systems, the level of risk inevitably increases. While information is stored locally and in isolation, it is relatively protected. But once continuous data exchange begins between databases, AI models, and agents, the attack surface expands sharply.

Modern AI systems require continuous access to data. For agent systems to operate and for language models to analyze information and make decisions, data must be regularly extracted from internal repositories and transferred into external computational environments. At this point, a critical question arises: who exactly can exploit a potential vulnerability: the company itself or the third-party AI provider whose infrastructure it relies on?

If a major provider has a vulnerability, an attacker could gain access not only to its systems but also to the data of numerous client companies. Without such external dependency, this attack vector may not exist.

Thus, the adoption of AI significantly expands the perimeter of cyber risks. This creates opportunities for both targeted attacks and a broad spectrum of actors working with vulnerabilities, from malicious actors to security specialists and proactive defense teams.

All these processes are interconnected: the growth of AI automation inevitably increases cybersecurity requirements, which in turn stimulates the emergence of new solutions and companies. Already today, we are seeing a wave of startups developing tools to protect AI infrastructure, manage data access, and monitor risks.

So where are we going in 2026?

The consolidation of large AI/LLM providers, combined with increasingly accessible systems with a focus on cybersecurity and the ability to make agentic decisions, paints a picture. We’re expecting to see less hype and more practical solutions out of the industry – taking over routine tasks and automating entire sectors of corporate decision-making.

The rule is: if it’s possible to comprehend and determine strict rules and best practices, AI agents will be able to handle it. Now that we understand what this technology is actually good at, businesses are increasingly going to maximize its utility across different verticals.