In 2026, technological competition goes beyond AI and 5G, with advancements such as post-quantum computing and cryptography, operational digital twins, and multi-cloud strategies. In this scenario, organizations that anticipate these changes will gain efficiency, security, and innovation. In this article, we gather 7 emerging technologies that promise to transform industries throughout this year, starting with quantum computing. 

1 - Quantum readiness: organizational preparation for the quantum era

Quantum computing is not yet fully operational on a large scale, but its advancement is already creating real risks for current digital security systems. Therefore, organizations are beginning to assess their Quantum Readiness, meaning how prepared they are for a world where traditional cryptography methods may become obsolete. This preparation involves technology, governance, and a long-term vision.

Preparation
Companies are starting to map where they use vulnerable cryptography and which data needs protection for many years. The logic is simple: if sensitive information is intercepted today, it can be broken in the future by quantum computers. Thus, preparation includes testing post-quantum cryptography and planning gradual migrations without disrupting operations.

Organization
Technology is not enough; structure is needed. More advanced organizations are creating internal quantum transition frameworks, defining managers, schedules, and security policies. Models like QUASAR help assess risks, prioritize critical assets, and guide decisions on when and how to migrate systems to standards resistant to quantum computing.

Quantum era
The quantum era will not just be a technical shift, but a new paradigm of digital security. Companies that anticipate this will have a competitive advantage, greater market trust, and lower exposure to future attacks. Quantum Readiness, therefore, ceases to be optional and becomes part of the technological strategy of innovative organizations.

2 - Post-quantum cryptography: the pillar of security in the digital future 

Current digital security still relies on methods like RSA and elliptic curve cryptography, which work because certain mathematical problems are infeasible for classical computers. The problem is that quantum computing threatens this model: algorithms like Shor's will be able to break these systems in the future, creating the risk of "harvest now, decrypt later," where data is captured today to be decrypted when quantum machines are mature.
To anticipate this, the security community developed algorithms resistant to quantum attacks, based on structures such as lattices and advanced hash functions. This work resulted, in 2024, in the NIST standardization of solutions like CRYSTALS-Kyber (key exchange) and CRYSTALS-Dilithium (digital signatures).

In practice, browsers and corporate systems are already testing hybrid models that combine traditional and post-quantum cryptography, while critical sectors, such as banking, healthcare, and government, are starting to adopt these standards to protect sensitive information for long periods.

As in other technological transitions, this change will be gradual and will require adaptation of legacy systems. However, post-quantum cryptography has ceased to be futuristic and has become a strategic priority in this decade.

3 - Evolved digital twins: from simulations to complete operations

Digital twins are undergoing a shift in role: from simulation tools to true physical-world operating systems. Instead of just reproducing assets or processes in a virtual environment, today they receive continuous data from IoT sensors, cross-reference this information with analytical models, and use AI to interpret what is happening in real time to suggest or even execute corrective actions automatically.

In the industry, this advancement is already visible. Siemens uses digital twins in production lines to monitor machine performance and predict failures before they cause shutdowns, reducing maintenance costs and increasing operational reliability. General Electric applies similar logic to wind turbines and industrial engines: their digital twins combine historical data, environmental conditions, and live telemetry to optimize performance and schedule preventive maintenance with greater accuracy.

Beyond manufacturing and energy, the digital twin logic is expanding to infrastructure and logistics. Ports, for example, have been adopting digital replicas integrated with maritime traffic systems to track ships in real time, simulate risks, and improve safety and emergency response protocols. In smart cities, urban digital models already help test traffic flows, energy consumption, and extreme weather scenarios before physical interventions.

This movement marks the central transition of digital twins: they leave behind being just "digital mirrors" for experimentation and become the core of operational decision-making, connecting data, automation, and intelligence continuously between the physical and digital worlds.

4 - Resilient multi-cloud and infrastructure interoperability

The multi-cloud strategy has ceased to be just a cost or performance choice and has become a central mechanism of resilience and business continuity. Instead of relying on a single cloud provider, organizations have been distributing workloads across environments like AWS, Microsoft Azure, and Google Cloud, combined with hybrid infrastructures (cloud + on-premises data centers). This approach reduces systemic risks: if a provider experiences instability, critical applications can continue operating automatically in another environment through orchestrated failover strategies and software automation.

In practice, this is only viable thanks to the maturation of standardized APIs, containers, and Kubernetes, which allow moving applications between clouds without rewriting them from scratch. Large companies in financial services and telecom already use multi-cloud orchestration platforms to replicate databases, balance traffic in real time, and redirect operations almost seamlessly to the end user. In parallel, unified observability solutions have started monitoring performance and security across multiple providers at the same time, creating an independent "control layer" above the underlying infrastructure.

Another relevant shift is the growing interoperability between clouds, with dedicated connectivity initiatives and shared identity models that facilitate governance, compliance, and unified security. In regulated sectors, such as healthcare and government, this allows separating sensitive data by environment, maintaining high availability and regulatory compliance.

The result is a more flexible, resilient, and portable architecture, in which the cloud ceases to be a single destination and becomes an integrated ecosystem of distributed services, capable of sustaining critical operations even in the face of failures, attacks, or abrupt changes in demand.

5 - Autonomous AI and web 4.0: intelligence that acts in the real world

AI is moving away from being purely assistive to act more autonomously in environments like factories, hospitals, and energy grids, making decisions in real time and coordinating systems, thus moving  from supporting humans to working jointly with them.

This transformation is deeply linked to the emergence of what has been called Web 4.0, a more intelligent, contextual, and agent-oriented internet. Unlike Web 2.0 (social) and Web 3.0 (decentralized), Web 4.0 connects data, devices, platforms, and AI models in highly interoperable ecosystems, where software can negotiate, coordinate, and act with one another. In practice, this means an AI system in a factory can automatically communicate with cloud platforms, suppliers, logistics systems, and even digital twins to optimize production without manual commands.

In the industrial sector, autonomous agents are already being used to manage supply chains, predict disruptions, and reorganize manufacturing priorities in minutes, something that previously required entire teams and hours of analysis. In smart grids, autonomous AI regulates energy distribution, detects anomalies, and isolates faults before they become widespread blackouts. In corporate environments, "AI agents" are beginning to execute chained tasks, such as approving workflows, negotiating digital contracts, or reallocating IT resources, operating as true digital coworkers.

The central point is that autonomous AI + Web 4.0 does not represent just more automation, but digital systems capable of acting in the physical and economic world with greater speed, coordination, and precision. 

6 - Privacy and digital security in the era of total connectivity

With the massive expansion of IoT, edge computing, and hyperconnected systems, organizations' attack surface has grown exponentially. In this context, the Zero Trust model, which assumes no network or user is trusted by default,  becomes essential, requiring continuous verification of identity, devices, and data.  In parallel, protecting information processed at the edge prevents sensitive data from needing to travel unnecessarily through the cloud, reducing risks and latency. The result is a more distributed, adaptive cybersecurity approach aligned with the complexity of modern connected environments.

7 - Applied quantum computing: use cases and industry impacts

Quantum computing is still in the development stage, but it has already left the purely experimental field and is starting to generate practical applications for problems that challenge classical computers. Companies like IBM, Google, and Microsoft have been advancing in different approaches, including research with topological processors, which promise greater stability and lower error rates, while organizations test quantum algorithms in hybrid cloud environments through remote-access platforms.

In the financial sector, quantum computing is expected to speed up portfolio optimization models, asset pricing, and fraud detection, allowing much more complex simulations than current ones. In logistics and supply chain, quantum algorithms can solve routing and resource allocation problems at scale, reducing costs and delivery times.

In materials science and chemistry, the quantum advantage is even more promising: simulations of molecules and reactions can lead to the development of new drugs, more efficient batteries, and sustainable materials much more quickly. Meanwhile, in healthcare, the potential includes accelerated drug discovery and more precise models of complex diseases.

Although large-scale use is still years away, pioneering organizations are already experimenting with hybrid use cases, combining classical and quantum computing, to build competitive advantage starting now and prepare for a future where this technology will be part of the technology mainstream.