AI Trends 2026: the end of hype and the rise of artificial intelligence as an orchestrator

The corporate market has experienced a frenetic race for artificial intelligence adoption in recent years. However, looking at the horizon of 2026, we realize that the phase of "isolated experimentation" is coming to an end.

The capital invested in artificial intelligence by the corporate market so far, which already exceeds the mark of US$30 billion globally, demands concrete return on investment (ROI) and direct impact on organizations' bottom line.

This year, artificial intelligence can truly move the business needle, but leadership must stay away from hypes that can divert strategic focus from AI usage.

2025 made it clear that experimenting is different from transforming. And, here at SQUADRA, we ended last year with real AI use cases for real strategies, whether it's reducing costs, accelerating decisions, improving experience, or sustaining growth.

In 2026, artificial intelligence enters a new phase: separating hype from applications that will truly make a difference in companies' daily lives. Check out what stops being discourse and what truly becomes a trend to sustain business impact:

Trend #1. AI Agents and Trained Models

In 2026, AI ceases to be just a question-and-answer interface to become agentic. AI agents are systems that not only suggest content but are trained to understand objectives, devise plans, and execute actions autonomously under human supervision.

The differentiator here is the transition from generic models to highly specialized models trained with proprietary data (Small Language Models and verticalized agents).

While many are still discussing the theory of agents, here at SQUADRA, we develop Genius and its turing bots, which are autonomous agents that learn from each interaction of Genius with our clients' real and complex problems and execute specific and repetitive tasks, freeing up human capital to focus on activities of higher strategic value.

Hype #1. Generic Assistants

The market has realized that "asking AI to write an email" and solving everyday problems is just the surface. The true value lies in models that, trained from a specific context, solve real business problems.

The use of generic assistants, such as ChatGPT, Gemini, or Copilot, will continue to be a reality for gaining efficiency in common activities, but using them as an end in itself is hype.

Trend #2. End-to-End Process Orchestration

In 2026, the trend is multi-agent orchestration, where different AI agents collaborate with each other and with humans to manage and execute complex end-to-end workflows, like digital assembly lines.

Success will be measured by the integration of AI into critical systems, ceasing to be a parallel tool to become part of the decision-making infrastructure.

Through Genius, we already have cases of processes where AI agents analyze, optimize, and learn, enabling scalability aligned with the organization's strategic goals, with reduced operational costs, increased predictability and control, in addition to the design of new processes in stages that allow integration between all the organization's systems.

Hype #2. Isolated POCs

According to the report GenAI Divide: State of AI in Business 2025, produced by MIT NANDA, about 95% of GenAI pilots do not show a measurable impact on companies' financial results.

According to the study, the barrier is not in the lack of infrastructure or the scarcity of available AI models, but in the difficulty of absorbing and applying the learning generated by technologies in a systemic and integrated way.

To transition from POCs (proofs of concept) to truly scalable systems that retain context and adapt to real operations to orchestrate complex end-to-end processes, hype must be set aside.

Trend #3. Deep Context in RAGs

For AI to deliver reliable results, it needs to understand the complex relationships that exist between data.

RAG (Retrieval-Augmented Generation) allows AI to perform deep searches, connecting dots that traditional databases overlook, drastically reducing hallucinations and increasing decision accuracy.

The crucial point of RAGs is an architecture that ensures security beyond scalability. After all, sensitive data is shared for AI to interpret, structure, and return results. A user-friendly front-end is also essential to democratize data access and reduce the time spent on research and analysis.

In March of last year, SQUADRA was the runner-up in the Microsoft Tech for Social Impact (TSI) Hack4Good 3.0, a global hackathon promoted by Microsoft Nonprofits, with a conversational RAG-based solution developed for EGPAF (Elizabeth Glaiser Pediatric AIDS Foundation).

In just 20 days, we developed a complete and integrated conversational RAG solution, which allowed users to ask questions in natural language and receive accurate and contextualized answers, extracted directly from the foundation's databases.

The development began with the creation of a robust data warehouse, gathering and standardizing fragmented data from 16 countries where EGPAF operates, which enabled the structuring of a unified and coherent database, essential for ensuring accurate and agile analyses.

We also built a semantic model on this consolidated base, allowing for advanced, multidimensional interpretations and the extraction of strategic insights. The solution's differentiator was the implementation of advanced AI agents capable of processing large volumes of information and generating structured, contextualized, and actionable responses.

Hype #3. Simple Keyword Search

Without a solid data foundation and a structure that organizes knowledge, AI delivers fragile information that can lead to catastrophic decisions. Believing that simply providing data access is enough for AI to function as an intelligent search engine through simple keyword searches is hype.

This Year, the Path to AI Success is Human and Strategic

The great lesson for 2026 is that technology does not solve problems alone. The success of AI adoption depends on the tripod: cutting-edge technology, clear governance, and people empowerment. Companies that insist on treating AI as an isolated IT project will lose relevance.

At SQUADRA, we help our partners and clients bridge the gap between experimentation and real transformation. The future of AI has already begun. Is your company ready to be a protagonist or just a spectator?

References: Microsoft | MIT Sloan Management Review | InfoMoney | Deloitte | IBM

To learn more about implementing these trends in your business, talk to one of our specialists.