Artificial intelligence will remain at the top of corporate technology agendas through 2027. Global AI spending is projected to reach $3.64 trillion next year, according to forecasts by Gartner. However, increased capital investment comes with a shift in focus: beyond exploring AI’s potential, organizations must find ways to scale usage securely, control costs, and deliver tangible business outcomes.
Among the key trends gaining momentum is the rise of agentic AI—systems capable of executing tasks and making decisions within predefined parameters—and the expansion of physical AI in robotics, drones, autonomous vehicles, and other real-world systems. Simultaneously, demand for specialized infrastructure is surging, and quantum computing is entering long-term strategic planning.
To navigate these transformations, enterprises must streamline their IT architectures and reduce operational complexity.
“AI is rewriting everything we knew, from the compute layer to the corporate organizational chart,” notes Gilson Magalhães, Vice President and General Manager for Latin America at Red Hat.
This shift requires eliminating redundancies, standardizing environments, and building a common technological foundation connecting data centers, public clouds, and the edge. The objective is to allow organizations to adopt new technologies without rebuilding their infrastructure with every evolutionary cycle.
“In the AI era, complexity becomes expensive because agents need understandable environments to make effective decisions. If every system has a different rule, every data point has a different definition, and every process requires a manual exception, AI cannot scale and will not yield the expected return,” Magalhães adds.
1. Agentic AI Becomes the Connective Fabric of the Enterprise
IDC projects a major evolution in corporate architecture as AI agents begin managing complex tasks and end-to-end processes. The challenge for CIOs lies in determining where to process AI workloads, how to govern autonomous agents, how to control inference costs, and how to integrate these agents with legacy operational systems.
“More than a new technology layer, agents are organizational catalysts. Scaling intelligent agents requires organized data with clear context, reliable architectures, and well-defined governance—alongside optimized processes and teams prepared to work with them. Without these elements, an agent becomes an operational liability rather than a strategic asset,” explains Alexandre Duarte, Vice President of Services for Latin America at Red Hat.
Gartner estimates that by 2028, at least 15% of daily corporate decisions will be made autonomously by AI systems, and the number of deployed agents will surpass 10 billion by 2030. Furthermore, agentic AI is expected to account for roughly 30% of enterprise application software revenue by 2035, representing a market exceeding $450 billion.
2. Physical AI Extends Intelligence to the Real World
Artificial intelligence is moving rapidly into applications that perceive and interact directly with the physical environment. Autonomous vehicles, industrial robots, and logistics drones are already automating tasks, enhancing safety, and generating operational insights.
The next stage involves deeper integration between these physical assets, AI agents, enterprise data platforms, and automation frameworks. This connectivity enables hardware to adapt to variable conditions, interpret contextual data, and execute actions within preset safety parameters.
Gartner predicts that by 2030, 80% of frontline workers in global enterprises will be assisted by physical AI systems. However, scaling adoption safely demands unified governance tailored to operational risk levels.
“The technology focus is evolving from disconnected tools toward a unified, governed environment where data, controls, platforms, and systems operate seamlessly across any setting,” states Thiago Araki, Senior Technology Director for Latin America at Red Hat.
For enterprise leaders, this shift highlights the necessity of an architecture capable of linking applications, machinery, and data without creating new technical silos. Integrated visibility also enables real-time performance monitoring and sets clear boundaries for autonomous decision-making.
3. Cyber Resilience: Moving Beyond Reactive Security
The expansion of AI across corporate operations escalates cybersecurity requirements—a global market Gartner projects will reach nearly $4.8 trillion by 2027. Protecting digital assets against AI-powered cyber threats presents a double challenge: safeguarding environments while continuously detecting and patching software supply chain vulnerabilities.
Modern enterprise applications rely heavily on open-source and third-party components that require ongoing monitoring and updating. With complex hybrid environments and threat actors leveraging AI to discover and exploit vulnerabilities at lower costs, remediating flaws without causing downtime is a fundamental security imperative.
“Open source is at the heart of modern software, but securing it at enterprise scale is a challenge. Identifying a vulnerability is only the first step; remediating it without disrupting critical production systems is the real key to success,” explains Andrea Cavallari, Field CTO for Latin America at Red Hat.
Solutions like Lightwell, developed by IBM and Red Hat, aim to streamline automated vulnerability remediation in production dependencies.
“Securing these systems has become the most critical gap in cybersecurity readiness, primarily because effective protection today requires transitioning from human-paced remediation to machine-speed defense,” Cavallari emphasizes.
4. Digital Sovereignty as Strategic Decision-Making Power
Rapid AI adoption is intensifying discussions around digital sovereignty. Regulatory policies, geopolitical shifts, and data privacy concerns are pushing governments and enterprise leaders to re-evaluate where workloads run, how sensitive data is managed, and which underlying models are deployed.
Investments in sovereignty encompass compute capacity, localized data centers, and AI models aligned with regional laws, languages, and cultural contexts. Gartner projects that by 2027, 35% of countries will be connected to regional AI platforms using proprietary contextual data.
“Deciding where workloads execute, how data is managed, and which technologies to deploy—even during regulatory or geopolitical shifts—will be vital for companies. However, organizations must carefully evaluate the cost and complexity arising from technological fragmentation across regions,” warns Martha Ardila, Country Manager for Central America and the Caribbean at Red Hat.
In this landscape, open-source technologies help preserve transparency in the software stack, minimize vendor lock-in, and provide the flexibility needed to swap or adapt solutions as business needs evolve.
Simplifying Infrastructure with Freedom of Choice
The intersection of open source, digital sovereignty, and enterprise AI served as the central focus of Red Hat Summit: Connect, a regional event series hosted across seven Latin American countries between October and November. The forum explored how organizations can prepare their IT foundations for AI expansion while balancing innovation, security, and architectural freedom.
“Amid all the predictions and challenges for the coming years, there is one common denominator: there is no single blueprint for every organization. What matters most is understanding what truly delivers measurable value and return to your specific business,” concludes Mike Ferris, Senior Vice President, COO, and CSO at Red Hat.
