Key trends in technology and digital business

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By 2026, technology will cease to be a shiny toy and will become a critical infrastructure that is measured in terms of performance, cost, and riskMany of the promises of the latest wave of innovation—especially in artificial intelligence, cloud computing, and connectivity—are finally leaving the experimental phase and becoming part of the daily lives of businesses, industry, and homes, as shown by... AI as a cross-cutting and strategic lever.

Organizations will be forced to make much more mature decisions: what AI to deploy, where to run workloads, how to protect data, how much energy to consume, and Who assumes responsibility when an autonomous system makes a bad decision?2026 is not going to be "just another year of hype," but the moment when agentic AI, hyperautomation, predictive cybersecurity, strict regulation, and new infrastructures (from WiFi 7 to direct satellite connectivity) meet at the same point.

Agentic AI and AI agents: from talking to machines to delegating work to them

The relationship with AI is fundamentally changing: we are leaving behind the "ask a chatbot" model to move on to assigning complete tasks to autonomous agents who plan and actThese systems are no longer limited to generating text or images, but connect with calendars, CRMs, ERPs, payment gateways, or logistics systems to execute end-to-end workflows, an evolution analyzed in the Differences between Claude, ChatGPT, and Gemini.

In the corporate sphere, we will see specialized agents who manage inventory, monitor energy consumption, coordinate marketing campaigns, or adjust logistics routes. Their autonomy will be limited by business rules, granular permissions, and human oversight schemes. (human-in-the-loop) systems that limit risk in critical processes but allow them to move freely in repetitive or low-impact tasks. For seamless integration, they connect with calendars, CRMs, ERPs, and other systems. digital systems.

On a personal level, the agents will act as assistants who make decisions for us: from booking a trip to reorganizing the schedule when an unforeseen event arises, or negotiating appointment changes with other agents. The user will no longer "chat" with the AI ​​to set goals and grant permissions on apps and data.a change similar to customize ChatGPT for specific tasksThis opens up a huge window of productivity, but also new operational, legal, and privacy risks.

The key in 2026 will be the normalization of this agentic AI in very specific domains: finance, support, logistics, energy consumption or maintenanceWe will not yet see general agents capable of managing the entire business, but rather a constellation of expert agents coordinated with each other through emerging agent-to-agent (A2A) communication standards and specific architectures already known as "Agentlakes".

These architectures rely on federated data governance: data is not concentrated in a single centralized lake, but rather They are shared in a controlled manner across multiple domains and organizations, without losing sovereignty over the data.In this way, several agents—from different vendors and models—can work in a coordinated manner on distributed information, reducing silos and avoiding excessive dependence on a single manufacturer. This approach and its challenges are outlined in reports on the world of software.

Active ERP and intelligent operations: from passive record to decision engine

Traditional enterprise resource planning (ERP) systems are no longer "black boxes" where only transactions are recorded. The old, passive ERP is transforming into a Active ERP or intelligent operations system, powered by AI agents integrated into key processesIt's no longer about entering data to obtain reports, but about the system itself detecting deviations and taking action.

By 2026, these active ERPs will be able to identify stockouts, billing anomalies, supply chain delays, or cost discrepancies and, based on predefined rules, propose or automatically implement corrective actionsGenerating orders, rescheduling deliveries, adjusting prices, or escalating incidents. Human staff then focus on exception management and strategy, not on repetitive tasks.

This change aligns with the rise of "smart operations" described by major consulting firms: processes, not applications, become the center of the system. AI agents are embedded in the end-to-end value chain —finance, HR, customer service, logistics— to monitor, optimize, and orchestrate entire workflows. Automation is no longer a local patch but a cross-functional layer that evolves continuously. More context and analysis can be found in professional technology news.

The organizational consequence is profound: software development is also changing. AI is “taking over” the software lifecycle, automating everything from code generation to testing, deployment, and maintenance. Developers are moving from writing line by line to expressing requirements and monitoring what the models produce., with a growing focus on architecture, stakeholder coordination, governance and quality.

Supercomputing, Cloud 3.0 and edge: computing power everywhere

The expansion of AI is pushing traditional CPU-based infrastructure to its limits. Organizations are discovering that their legacy systems cannot support increasingly complex AI models, so The transition to hybrid architectures with GPUs, NPUs, and specialized chips is accelerating. in artificial intelligence workloads.

Very few companies will build their own supercomputer, but virtually all of them will have to access massive cloud computing servicesIt is the birth of a phase that many voices call Cloud 3.0: an environment where public cloud, private clouds, multicloud, sovereign infrastructures and edge computing coexist, all orchestrated as if it were a single intelligent platform.

At this stage, AI workloads are distributed according to latency, sovereignty, and cost requirements. Models that demand immediate response or maximum privacy are moving towards the edge or the deviceWhile heavy training and complex simulations are run in specialized data centers, the result is a more resilient but much more complex technical landscape to govern.

In parallel, edge-first computing is gaining prominence. Business logic is no longer confined to a monolithic backend but is instead distributed across lightweight functions that run close to the user, on gateways, IoT devices, or regional nodes. This multiplies performance and reduces dependence on a single point of failureHowever, it requires development teams to understand distributed databases, eventual consistency, and serverless functions with very strict time and memory limits.

All this computing power, however, has a downside: energy consumption. AI data centers are nearing the limits of what's sustainable, and by 2026 the focus will shift towards "efficiency per watt." The bottleneck will no longer be just the chip, but the capacity of the electrical grid and the cooling systemsThis necessitates rethinking architectures, algorithms, and deployment strategies. The relationship between AI deployment and the energy chain is addressed in analyses on energy chain in industrial environments.

GreenOps, Green AI and sustainability as a CIO KPI

Sustainability is no longer just a pretty PowerPoint presentation; it's becoming a a hard operational indicator for technology managersEuropean regulations such as the CSRD require detailed environmental impact reports, and the explosion of AI has highlighted the real cost of training gigantic models and maintaining computing-intensive infrastructures.

The concept of GreenOps is emerging strongly: an approach that combines cloud financial management (FinOps) with the optimization of environmental impact. It's about choosing regions, instance types, accelerators, and usage patterns that minimize both the bill and the carbon footprint., incorporating energy efficiency metrics into the very design of the applications.

In parallel, the so-called Green AI is gaining ground. Instead of always opting for increasingly larger models, organizations are beginning to value Smaller, more efficient models (SLMs), selective training, and optimized inference techniquesThe objective is clear: to achieve comparable or better results with a much lower consumption of resources, both in terms of costs and corporate responsibility.

In this scenario, not optimizing ceases to be a simple bad practice and becomes a hindrance to growth. Companies that cannot justify every watt consumed or every gigabyte transferred will see their ability to scale limited., both due to regulatory pressure and pure market economics.

Synthetic data, federated governance, and AI regulation

Large-scale generative AIs have already “devoured” much of the publicly available useful content, and at the same time, the quality of the web is degrading due to the growth of spam, misinformation, and content generated by other AIs. In this context, Continuing to train models solely with real internet data has diminishing returns and increasing legal risks..

The answer lies in the intensive use of synthetic data: artificially created sets of information to simulate real patterns without relying directly on personal records. These can be texts, images, audio, medical records, or financial transactions generated in a controlled manner., with fewer privacy risks and the possibility of modeling situations that are difficult to observe in the real world.

The challenge is not so much technical as epistemological: if synthetic data only replicates the past, AI remains trapped in a kind of digital echo; if designed with sufficient diversity and statistical rigor, it can open the door to behaviors, hypotheses, and solutions that were not explicitly in the original data2026 will be a key year to test how far this frontier extends.

All of this is happening under the watchful eye of increasingly demanding regulators. With frameworks such as the European AI Act and new data protection and intellectual property regulations, Organizations will need to accurately document what data they use, how they train their models, and how they audit automated decisions.AI governance (AI TRiSM: Trust, Risk and Security Management) becomes the "gateway" for any serious project.

This shift towards regulation entails more bureaucracy and slower approval processes, but also It filters out the smoke and forces us to differentiate between professional software and improvised solutions.In the medium term, trust and auditability will be as critical factors as the accuracy or technical performance of the model.

Preventive cybersecurity, digital trust, and the end of passwords

With the exponential growth of the attack surface—more AI, more IoT, more hybrid cloud—cybersecurity can no longer afford to be reactive. Automated attacks, self-healing malware, and multimodal social engineering campaigns make it impossible to... human teams manually manage all alerts.

The definitive trend for 2026 is predictive defense: platforms that, thanks to AI models trained on huge volumes of traffic, logs, and attack patterns, They detect anomalies before they become actual intrusionsIn response to a suspicious signal, the systems isolate services, block identities, or adjust access policies in real time, without interrupting operations.

In parallel, confidential computing extends data protection even during processing. Thanks to secure, hardware-supported enclaves, The information remains encrypted while the application uses it.This drastically reduces the attack surface. Designing applications for zero-trust environments becomes mandatory: APIs that operate on encrypted data, strict separation of privileges, and continuous behavior monitoring.

From the end-user perspective, we are witnessing a historic shift: passwords are beginning to fade into the background. Passkeys, based on public-key cryptography stored on the device, they become the primary authentication methodThe private key never leaves the mobile phone or computer, and is unlocked with biometrics; the server only receives a mathematical proof, so phishing becomes pointless: there is no password to steal.

This package of changes transforms cybersecurity into a core component of business resilience. It's not just about avoiding breaches, but about ensuring business continuity, reputation, and regulatory compliance. in an environment where every security incident can have systemic consequences.

Composable architectures, hyperautomation, and Service as Software

For AI agents to orchestrate complex processes, the underlying technology must be flexible. Composable architecture is gaining ground. Systems are built as sets of packaged business capabilities., connected to each other like LEGO pieces using standard APIs.

This approach allows modules to be replaced, updated, or reconfigured without affecting the rest of the system, enabling a speed of change that perfectly matches the needs of agentic AI. Agents need access to data and functions in a granular wayA rigid architecture based on monoliths hinders that orchestration.

This is the foundation upon which hyperautomation takes off. We're not just talking about classic RPA, but a combination of AI, process orchestrators, agents, and cloud services that cover high-value tasks such as document review, code generation or refactoring, incident triage, risk scoring, and complex customer service. Companies are starting to pay not per user, but per automated result., a shift that some define as “Service as Software”.

Instead of simply purchasing an application, organizations are contracting fully automated services: “so many contracts reviewed per month,” “so many hours of code audited,” “so many claims processed.” The software becomes a digital assembly line where humans supervise and fine-tune, and machines execute.

This evolution necessitates redefining jobs, metrics, and cost models, but it also frees up human talent for more analytical, creative, and strategic roles. The CIO and business leaders must act as architects of this new blend of people, processes, and algorithms.

AI physics, collaborative robotics, and robot swarms

Artificial intelligence is definitively leaving the screen to inhabit the physical world. Drones, mobile robots, autonomous industrial systems, and household devices are all combining. Sensors, actuators, and AI models to perceive, decide, and act in real timeThis is what many are already calling "physical AI." To better understand the profiles of those who design and integrate these systems, see what a Engineer in robotics and digital systems.

In industry we will see robots moving among humans in warehouses, production plants and logistics centers, taking on repetitive, dangerous or nighttime tasks. Traditional “cobots” remain the most robust option in many factories, but humanoid robots are starting to appear in very limited commercial pilots, especially where the environment is designed for people and it wouldn't make sense to redesign the entire infrastructure.

These robots, still expensive and relatively fragile, improve every year in motor skills, manipulation, and balance. The real revolution, however, will come from their integration with agentic AI. robots capable of receiving high-level objectives, planning subtasks, using tools, and coordinating with other systems, with much less granular human supervision.

Beyond individual robots, research into robotic swarms opens the door to distributed systems with collective intelligenceMany simple robots collaborate on tasks such as inspection, maintenance, logistics, or exploration. Training and coordinating these swarms will be a key challenge in sectors like energy, construction, and precision agriculture.

Immersive reality, lightweight spatial computing, and virtual worlds

Augmented and virtual reality technologies, which for years seemed relegated to leisure, are making the leap to mass uses in education, training, design, simulation and commerce. Lighter, more accessible, and connected immersive experiences are starting to take hold in companies and professional sectors..

While high-end headsets continue to evolve, "light spatial computing" solutions are also expanding: discreet glasses with retinal projection or directional audio, non-invasive neural wearables (bracelets, rings, gloves), and devices capable of displaying contextual information without blocking the view of the real worldThe first users will be very early adopter profiles, but they mark the beginning of a relationship with technology that is less dependent on the mobile screen.

In parallel, persistent virtual worlds are proliferating where Simulated artificial intelligences interact with each other and with people.Learning, evolving, and testing strategies that are then transferred to the physical world. This affects everything from product and service design to team building, urban planning, and scientific research.

The convergence of physical reality, industrial metaverses, persistent AI, and cognitive robotics is shaping a new ecosystem in which The boundaries between the real and the digital are blurring2026 marks a turning point in which these pieces cease to be isolated demos and begin to be integrated into production processes.

Advanced connectivity: WiFi 7, Matter and Direct-to-Cell

All this technological infrastructure needs networks that can keep up. At home, the WiFi 7 standard is becoming the new minimum requirement. Thanks to techniques like MLO (Multi-Link Operation), The devices use multiple bands simultaneously to reduce latency and increase stabilityenabling 8K streaming, mixed reality, or cloud gaming without bottlenecks in device-heavy environments. For guidance on home connectivity, consult a ADSL, fiber and home telephone guide.

At the same time, the Matter protocol is establishing itself as the “common language” of the connected home. Light bulbs, locks, sensors, thermostats, and appliances, regardless of brand, They can be integrated into the same local network without proprietary gateways or manufacturer-specific appsThe smart home is no longer a puzzle but a coherent infrastructure; the relationship between devices and urban efficiency is discussed in articles on intelligent buildings.

Outside the home, direct-to-cell satellite connectivity is beginning to be deployed commercially. Mobile phones connect directly to low-Earth orbit satellites when there is no terrestrial coverageInitially for emergency messaging and text, and gradually for basic data. The "blank areas" on the map are shrinking, which has significant implications for critical activities, logistics, tourism, and civil protection.

Hardware of the near future: NPUs, memory, and new batteries

The exclusive reliance on the cloud is being tempered by the massive arrival of NPUs (Neural Processing Units) in laptops, desktops and smartphones. Running AI models locally reduces latency, improves privacy, and offloads work from data centers.By 2026, having on-device AI acceleration will be as basic as having enough RAM or storage.

Memory, in particular, is becoming another hotspot. Demand for high-performance DRAM and NAND for AI servers is straining the supply chain, resulting in... Higher prices and lower availability of high-capacity consumer devicesThe law of supply and demand is starting to have a direct impact on the user's wallet.

In parallel, silicon anode batteries and the first commercial solid-state implementations in high-end ranges are reaching the consumer market. These technologies offer much higher energy densitiesallowing for mobile phones of the same size that last two days of real use, or lighter electric cars with greater autonomy.

Furthermore, they withstand ultrafast charging with less degradation than current chemical batteries, which reduces “battery anxiety” and facilitates more intensive use models of connected devices, mobile robots or distributed sensors.

Technological sovereignty, regulatory compliance, and the strategic role of the CIO

In an unstable geopolitical context, technological sovereignty ceases to be an abstract discourse and becomes a reality. a strategic priority for countries, regions, and large companiesNo one can be totally self-sufficient, but it is possible to design a "resilient interdependence": diversifying critical suppliers, betting on sovereign clouds, local AI models, alternative chips, and interoperability frameworks that allow switching suppliers without trauma.

This quest for control is combined with increasing regulatory demands regarding privacy, digital identity, trusted services, and intellectual property. Sectors such as public administration, finance, and healthcare must to ensure that electronic identity, digital signatures, and traceability of automated decisions meet strict standards, relying on certification authorities and advanced trust services.

In this scenario, the CIO ceases to be a mere systems manager and becomes architect of digital trust, operational resilience and sustainabilityTheir decisions regarding investment in AI, hybrid infrastructure, talent, data ethics, and governance will determine the organization's ability to compete in a market where technology is no longer an "extra," but the very backbone of the business.

The landscape unfolding in 2026 blends maturity and vertigo: AI ceases to be an experiment and begins performing real work, infrastructure expands from the cloud to the edge and satellite, regulators accelerate the pace, and energy becomes hard currency; those who can combine AI agents, composable architecture, intelligent operations, predictive security, and sustainability will thrive. They will have a competitive advantage that is hard to match, while the rest will discover that improvising with technology is no longer cheap..