Beyond the hype: the AI ​​trends that will define business growth in 2026

As 2025 comes to a close, the technological landscape has moved beyond the initial fascination with Generative Artificial Intelligence. We are no longer in a phase of discovery, but in a critical stage of maturity. For business leaders, the question has changed: it is no longer “what can AI do?”, but rather “how do we scale AI to generate a tangible impact on the P&L?”.

Looking ahead to 2026, business growth will not come from isolated experiments, but from the deep integration of autonomous systems and the redefinition of human talent. Below, we analyze the strategic trends that will make the difference between organizations that simply adopt technology and those that lead the market.

1. Agentic AI: From human execution to strategic orchestration

If 2025 was the year of exploring generative AI, 2026 will be the year of Agentic AI operating at scale. We are no longer talking only about chatbots that answer questions, but about systems with agency: software that can reason, plan, and execute complex tasks end to end with minimal human intervention.

Currently, 62% of organizations are already testing AI agents, and the trend points to a fundamental shift in the role of workers: moving from task executors to orchestrators of digital agents.

The operational implications are profound. Imagine a finance department where an autonomous agent not only detects a discrepancy in an invoice, but also investigates the cause, contacts the supplier, and proposes a reconciliation in the ERP, waiting only for final approval from the manager.

To dive deeper into this paradigm shift, McKinsey analyzes how agents are redefining skill partnerships, while technology leaders such as Wipro’s CTO point out that the agent-driven autonomous enterprise will be a tangible reality by 2026.

2. The future of work: A partnership between humans, agents, and robots

Fear of total replacement is giving way to a more nuanced and collaborative vision. Work in 2026 will be structured as a tripartite partnership between people, software agents, and physical robots.

Although theoretically up to 57% of current working hours could be automated, the reality is that more than 70% of human skills will remain critical. The difference lies in how they are applied. The fastest-growing skill will not be programming per se, but AI fluency: the ability to direct, audit, and collaborate with intelligent systems.

Organizations will need to design their workflows based on different collaboration archetypes, from “people-centered” models to “agent-centered” models, depending on the nature of the task. To better understand these archetypes and prepare for this transition, we recommend reviewing McKinsey’s detailed report on the future of skills.

3. Governance, security, and trust: The condition for scaling

The mass adoption of autonomous agents brings with it a critical challenge: security. An agent that has permission to execute transactions or send emails on behalf of the company represents a significant risk vector if it is not properly governed.

By 2026, the focus will shift from “rapid adoption” to responsible implementation. Companies must treat AI agents almost like new employees: they need clear boundaries, performance metrics, constant oversight, and audit protocols. The traceability of decisions made by AI will be mandatory not only for regulatory reasons, but for corporate survival.

Experts warn that innovation in AI agents requires a robust security “chaperone” to prevent autonomy from turning into vulnerability.

4. Mental models and agent design

One of the most common mistakes when implementing AI is the lack of conceptual definition. What is the agent for your organization? Is it a passive tool (like an advanced calculator), an automated process, or a “digital employee” with a certain level of autonomy?

Defining the right mental model is crucial for interaction design and expectation management. If employees see the agent as a tool but it acts with unexpected autonomy, mistrust is generated. If it is treated like an employee but lacks context, it will fail in execution.

By 2026, success will depend on aligning these cognitive operating models with organizational culture and business objectives.

5. The evolution of generative AI: From assistance to decision-making

So far, we have used generative AI mainly to create content or summarize information. The next frontier is assistance in complex decision-making. However, the concept of Human-in-the-loop will remain a non-negotiable layer for high-impact decisions.

By 2026, it is projected that between 15% and 20% of routine processes will run autonomously, allowing human talent to focus on exceptions and strategy. This has a direct impact on technology teams: developers will spend less time writing repetitive code and more time on system design, quality control, and product thinking.

6. Sector impact: Concrete trends for 2026

The application of these technologies varies by industry, but the pattern of intelligent automation is transversal:

Retail: Operational efficiency will be key. We will see widespread adoption of AI-driven demand forecasting, real-time dynamic pricing, and hyper-personalization that goes beyond product recommendations, automating complex inventory decisions. Retail Tech Innovation Hub highlights these technological trends as defining for the sector.

Insurance: The insurance sector will be transformed through claims orchestration and embedded insurance. The ability to process data in real time will enable much more dynamic pricing models aligned with actual risk. Economic Times explores how AI and personalization will transform insurance.

Telecommunications and Technology: The priority will be observability. Using AI to predict and prevent costly infrastructure outages will become an industry standard, moving from reactive to predictive and prescriptive maintenance.

Preparing the ground for the future

The year 2026 will not simply be about “using artificial intelligence,” but about designing organizations capable of thinking, learning, and orchestrating alongside it. Competitive advantage will no longer lie in access to models (which are becoming commodities), but in the quality of proprietary data, integration architecture, and above all, the preparedness of human talent.

At Rootstack, we understand that technology is only part of the equation. Companies that start building solid foundations today— in terms of software architecture, data governance, and a culture of innovation— will be the ones that lead their market tomorrow.

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