SEIDOR indentifies the top 10 tech trends that will shape 2026

December 18, 2025. Technology consulting firm SEIDOR presents the ten IT trends that will shape the direction of 2026. The analysis positions artificial intelligence as a central operational core for a growing number of organizations and describes how this evolution is accelerating a cross-cutting transformation that spans infrastructure, cybersecurity, and business models alike.

According to SEIDOR, 2026 will be defined by the gradual deployment of agentic AI (AI capable of deciding and executing tasks autonomously within defined boundaries) in real-world environments; broader access to high-density computing capabilities; the consolidation of adaptive risk governance models (AI TRiSM, as a framework of policies, controls, processes, and tools to manage AI with trust while maintaining control and security); the expansion of approaches such as composable architecture (a modular architecture based on interchangeable components); and GreenOps (managing cloud environments with both economic cost and environmental impact in mind), among other developments.

1. The gradual consolidation of agentic AI

After the exploration phase of 2025, 2026 marks the inflection point toward the operational reality of Agentic AI. Artificial intelligence evolves from being a reactive tool (chatbots) to becoming systems with proactive capacity to “act” (Agentic AI). These agents will not only suggest actions, but will execute complex end-to-end workflows, interacting with databases, APIs (“standard bridges that allow different applications to connect with each other”), or even other agents through new protocols such as A2A (Agent-to-Agent, a standard that allows different AI agents to communicate and collaborate with each other).

The key nuance: autonomy will not be total or immediate. We will see gradual adoption in which agents gain independence in limited and repetitive tasks, while critical processes will maintain strict human supervision schemes (“Human-in-the-loop”) to ensure accountability and compliance.

2. Active ERP: toward assisted management

Traditional ERP (the company’s central management system, in areas such as finance, procurement, logistics, etc.), understood as a passive system of record, evolves toward an Active ERP model. Driven by Agentic AI, management software will drastically reduce the need for manual input in back-office processes (internal and repetitive administrative tasks).

At SEIDOR, in collaboration with leading ERP providers, agents are already being incorporated that are capable of detecting anomalies (such as a stock shortage) and proposing or executing corrective actions under pre-approved parameters. The objective is not a “company without humans,” but an organization where talent is freed from transactional management to focus on strategic decision-making and exception management.

3. Supercomputing and massive cloud computing services

The adoption of AI highlights the limitations of legacy infrastructure (inherited, older systems still in use) based purely on CPUs (traditional processors, not specialized for intensive AI workloads) for new workloads. The transition toward hybrid architectures with a greater role for accelerators (GPUs, NPUs, LPUs, which are chips specialized in AI calculations and much faster than traditional processors) is confirmed.

For most companies, this will not involve building their own supercomputers, but rather managing access to massive cloud computing services (cloud data centers with very high processing capacity). Infrastructure modernization (or contracting it as a service) becomes a non-negotiable requirement: without sufficient and properly sized computing capacity, planned software will not be able to run efficiently.

4. Adaptive governance and risk management (AI TRiSM)

Trust will be the absolute “gatekeeper” of AI adoption in 2026. Beyond implementation speed, success will be measured by the robustness of the TRiSM strategy (Trust, Risk and Security Management). Organizations will move from static compliance models toward dynamic governance capable of monitoring risks such as AI hallucinations or data protection in real time.

Regulatory context: in the European environment, this point is critical. A strategic tension is anticipated between innovation and regulatory compliance (AI Act, the new European regulation on artificial intelligence), which will open a necessary debate on how to make adoption frameworks more flexible so as not to compromise competitiveness vis-à-vis more deregulated blocs. In this context, it is also worth highlighting that bodies such as AESIA (Spanish Agency for the Supervision of Artificial Intelligence) have already begun publishing support guidelines to facilitate regulatory compliance and guide organizations through this transition.

5. Preventive cybersecurity: when defense also operates at machine speed

Cybersecurity accelerates its transition from a reactive paradigm to a preventive and predictive one. Faced with the “industrialization” of cyberattacks (self-healing malware, multimodal social engineering at scale), manual human response is insufficient. The trend is toward the implementation of AI-assisted defenses that can detect and neutralize threats at “machine speed.” Nevertheless, far from eliminating humans, this model elevates their role: analysts will stop chasing individual alerts to focus on supervising defense policies and managing strategic incidents, leaving immediate tactical actions to automation.

6. Federated data governance and agent ecosystems

Market fragmentation and privacy regulations drive Federated Governance, which in 2026 is shaping up as a reference model for secure data collaboration. This approach facilitates data sharing (“data sharing”) between organizations without the need to physically centralize assets, while respecting data sovereignty.

On this basis, architectures oriented toward agent orchestration emerge, which at SEIDOR we refer to as “Agentlakes”: environments where different AI agents work on distributed data in a coordinated manner. These ecosystems are designed so that multiple AI agents, regardless of their provider or foundational model, can interact and securely access enterprise data, with the strategic objective of reducing silos and mitigating the risk of technological dependency.

7. Specialized and domain-specific AI

2026 will consolidate coexistence between large general-purpose models and vertical AI based on smaller, more efficient models (SLMs: Small Language Models). Companies will massively adopt domain-specific models, refined through efficiency techniques on proprietary data and sector-specific terminology (legal, medical, engineering). This approach seeks to reduce hallucinations and improve regulatory compliance, while also helping to reduce the environmental and energy consumption impact of LLMs (Large Language Models). In turn, this specialization goes beyond the corporate sphere to industrialize R&D: vertical models designed for science will increasingly accelerate materials and drug discovery, linking research to tangible efficiency metrics.

8. Hyperautomation and the evolution toward “service as software”

The software consumption model continues its evolution. Beyond traditional SaaS (paying a subscription to use a cloud application), an approach is beginning to expand in which companies do not pay only for access to the tool, but for the automated result it generates (for example, receiving an already-prepared report or reviewed code).

This evolution can be described as a “service as software” logic, evolving from “pay per user” (for each person who uses the software) toward “pay per outcome” schemes (for each completed task or service) in high-value automatable tasks (such as document review, code generation, or triage).

9. Composable architecture as an enabler

Adopting AI does not require abandoning current systems, but evolution toward architectures with greater modularity facilitates a qualitative leap: it allows the design of an agent-oriented architecture based on Business Capabilities that collaborate with one another. By 2026, composable and modular architecture (based on Packaged Business Capabilities) gains strength, where each business capability is exposed as a well-defined functional block. In practice, this means building the system in interchangeable business modules that connect to each other like technological “LEGO pieces.” This modularity is the technical prerequisite for agent ecosystems: only if data and functions are exposed via APIs (standard interfaces that allow systems to call each other) can AI agents orchestrate them flexibly.

10. IT sustainability: the AI paradox and “GreenOps”

Sustainability is reinforced as a critical operational KPI for the CIO (key indicator to measure IT performance), driven by strict regulations (such as the European CSRD regulation, which requires detailed sustainability reporting) and by the “AI paradox” itself: the technology that optimizes corporate efficiency is, at the same time, resource-intensive. We will see mature adoption of “GreenOps” practices (financial and environmental cloud management). Companies will need to balance innovation with carbon footprint, making hardware efficiency (use of LPUs/NPUs, chips specialized in accelerating AI with lower consumption than traditional processors, and model optimization) a decision that is both financial and one of corporate responsibility.