*By Rennan Sanchez
Technology has officially entered a new cycle. If the last decade was marked by digitalization, the turn of the millennium towards 2026 inaugurates a deeper movement: the consolidation of Artificial Intelligence as a structural axis of the digital economy. According to Gartner, the trends predicted for this year are not just about technological acceleration, but about a paradigm shift. We are moving from an experimental phase to what is called "operational AI," supported by robust infrastructures, specialized models, and rigorous security and governance practices.
The explosion of advanced models, especially multimodal and specialized ones, has become possible thanks to the convergence of supercomputing platforms, intelligent hybrid architectures, and components dedicated to AI processing. This evolution redefines sectors: pharmaceutical companies simulate molecules with precision, financial institutions run risk models in minutes, agribusiness and logistics incorporate granular weather forecasts, and retail tests pricing strategies in real time. Supercomputing becomes a strategic asset – by 2026, processing will cease to be a bottleneck and become an enabler of continuous innovation.
Traditional automation relied on rigid workflows. AI-driven automation evolves into ecosystems of autonomous agents, specialized in micro-tasks or specific domains. The competition will not be between companies that use AI or not, but between organizations capable of orchestrating agents, enabling rapid decisions, bottleneck reduction, and context-adaptable processes in real time. Multi-agent systems represent the beginning of self-adjusting operations, a milestone in modern management.
The era of generalist models was essential for democratizing technology. However, the era of Domain-Specific Language Models (DSLMs) ushers in a new level of maturity: models that encompass terminology, processes, regulatory cycles, and the nuances of each industry. The effect is transformative, generating precision in decision-making, reduced regulatory risk, and the ability to customize at scale. DSLMs are the synthesis of proprietary data, operational context, and specialized intelligence.
The widespread adoption of AI expands the risk surface. Models can be manipulated, agents can perform unforeseen actions, and data can leak in prompts. Therefore, Gartner points to the emergence of AI security platforms that unify governance, auditing, usage policies, behavioral detection, and protection against attacks. If the cloud demanded new layers of security in 2015, AI will require a dynamic, contextual approach adapted to digital behavior in 2026.
Generative AI now collaborates seamlessly with human teams, boosting productivity. The consequence is structural: smaller teams deliver more, development cycles become shorter, business experts participate in building solutions, and IT gains a strategic role. Gartner projects that, by 2030, 80% companies will replace large engineering structures with lean teams augmented by AI.
With the expansion of AI workloads across multiple clouds, confidential computing becomes critical. It ensures encryption throughout the entire data lifecycle, including during processing. By 2026, privacy will cease to be a requirement and become a pillar of enterprise architecture.
AI, data, cloud, and security now form a single ecosystem. The new era demands prepared infrastructure, specialized models, and deep integration between technology and business. It's not about implementing tools, but about building organizational capabilities. Companies that seek prepared technology partners will not only survive; they will set the pace for their industry.
Rennan Sanchez is the CTO of Skyone.
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
