AI at the core: Key Technology Trends for 2026

Technology trends for 2026 are built around AI and its impact across strategy and operations, workforce transformation, tech stacks and tools, trust, and sustainability. However, the conversations have shifted. It’s no longer about what AI can do, but about how to turn experiments into tangible impact. The emphasis has moved from endless pilot projects to delivering real business value. DigitalMara has reviewed these key technology trends and compiled the findings into the following clear, business-focused guide.  

Moving from AI ambition to execution remains a challenging process. Deloitte points out that cloud-first infrastructure is not built to support AI-driven demands. Human-centered processes don’t translate to autonomous agents; security models lag behind machine-speed threats, and IT frameworks focused on service delivery are inadequate for true business transformation. Meeting the challenges of AI requires more than making improvements. It calls for a complete rebuild of systems and practices.  

To help you navigate this shift, this article will cover the following key technology trends for 2026:  

• Agentic AI,  

• Domain-specific language models,  

• AI platforms and infrastructure,  

• AI and security,  
• Physical AI.

Agentic AI

Agentic AI has gained traction in 2025 and is still an evolving trend. It’s growing, but companies face many obstacles. According to Deloitte’s 2025 Emerging Technology Trends report, 30% of respondents are exploring agentic options, and 38% are implementing pilot solutions. Some 14% have solutions that are ready for deployment, and only 11% are actively using these systems in production. Furthermore, 42% of organizations report that they are still developing their agentic strategy roadmap, with 35% having no formal strategy at all.   

The secret to effectiveness is not just layering agents onto old workflows, but redesigning operations in such a way that they coincide with agentic AI. This requires redefining the roles between humans and AI and establishing clear governance and monitoring frameworks. Without these structural changes, agentic AI will be limited to isolated pilots rather than becoming a scalable, value-driven capability embedded into core business operations.  

Multi-agent systems

Multi-agent systems (MAS) are the next step in the evolution of AI agents. Each agent of this set is focused on a specific task, and together they can automate more complex workflows. At the same time, all agents in an MAS also communicate with each other. This helps boost reliability, efficiency, and scalability. Gartner states that MASs are not a replacement for employees, but augment their work as part of the organization’s routine large-scale operations. They lack the free will and adaptability needed to solve complex problems. Their real value lies in more effective collaboration between humans and AI, allowing everyone to focus on what they do best.  

However, employing an MAS involves certain challenges, such as security threats, reliability concerns, a need for more thorough monitoring, and resource and cost allocation. To manage these complexities, it’s necessary to have improved agent frameworks, platforms, and protocols, as well as established standards for agent communication.  

When it comes to developing an MAS, careful design is crucial. They rely on a modular architecture that breaks workflows into independent, reusable agent components that can be updated, replaced, or scaled without disrupting the entire network. This design enables companies to adapt quickly, deploy new capabilities efficiently, and maintain reliability as operations grow. It also supports specialized and cross-functional tasks.  

Moreover, an MAS should handle mixed-dimensional data, integrating structured databases, sensor outputs, unstructured text, and contextual metadata. This enables agents to reason across these diverse data types, ensuring that actions are informed, coordinated, and fit for all objectives. Such capability is particularly important when real-time decisions rely on data from multiple sources.  

Domain-specific language models

Domain-specific language models (DSLM) are designed for particular industries or business domains and are trained on highly specialized data. This targeted approach allows them to deliver more precise results, meet regulatory requirements, and outperform general-purpose language models. As a result, companies can deploy AI faster, reduce operational risk, and lower costs in areas such as finance, healthcare, legal, supply chain optimization, and human resources.  

Developing a DSLM involves collecting high-quality domain-specific data and structuring it. Then, models are fine-tuned and trained with reinforcement learning to understand the unique language and context of the industry. This ensures the system provides reliable outputs, meets regulatory requirements, and aligns with business goals.  

By using DSLMs, companies can move beyond small AI experiments and embed intelligence into their core operations. Gartner projects that by 2028, more than 60% of generative AI models used by enterprises will be domain-specific. A practical example comes from IBM, which built a DSLM for the German court system, allowing AI to assist with legal document analysis. This reduces repetitive work for judges and clerks and potentially cuts case processing time significantly.  

AI platforms and infrastructure  

Secure and efficient AI development and deployment require the right foundation. To support this, companies need robust platforms and infrastructure. In 2026, focus is placed on platforms that integrate generative AI into software development, high-performance computing for advanced analytics, and confidential computing for secure data processing  

AI‑native development platforms

AI‑native development platforms embed generative AI directly into the software development lifecycle. They enable teams to rapidly build applications, automate repetitive tasks, and improve productivity without expanding traditional engineering resources. By combining low-code/no-code tools, AI-powered code assistance, and pre-trained models, these platforms accelerate time-to-market and reduce development costs. They are particularly valuable for small, agile teams that need to experiment and iterate quickly while maintaining enterprise-grade standards.  

AI supercomputing platforms

AI supercomputing platforms provide the computing power required to train large models, run advanced analytics, and support complex AI workloads. These platforms include CPUs, quantum processors, GPUs, AI ASICs (Application-Specific Integrated Circuit), and high-performance software. They are essential for developing large-scale complex AI models. Gartner predicts 40% of enterprises will adopt hybrid computing architectures by 2028, while vendors will include supercomputing platforms in their offerings. This shift enables companies to balance performance, cost efficiency, and scalability while supporting increasingly demanding AI workloads.  

Confidential computing

Confidential computing protects sensitive data while it is being processed by isolating workloads in hardware-based trusted execution environments (TEEs). This approach ensures that data remains encrypted and inaccessible not only to external attackers, but also to system administrators, cloud providers, and other unauthorized parties. For businesses handling regulated or high-value data, confidential computing provides an additional layer of protection across the entire data lifecycle, maintaining compliance with privacy laws, data localization requirements, and industry regulations. Confidential computing supports broader AI adoption at scale.  

AI and Security

Artificial intelligence provides expanded capabilities but also introduces new security risks, serving as a tool for both defense and attack. Specific security threats include shadow AI deployments, AI-accelerated attacks, and the intrinsic risks of AI systems, such as poor control and governance. Traditional cybersecurity measures should remain constant but adapt to new conditions, such as the ability to react at machine speed in real time.  

AI security risks relate to these main areas:  

• The amounts of data processed by AI models during training, testing, validation, and providing outputs after deployment. To mitigate risk, it’s vital to carefully manage data and maintain high data quality, constantly monitor it to detect anomalies, and use robust access control.  

• The security of AI models involves their architecture, data parameters, and everything related to training, validation, and deployment. The primary security measures are model isolation during training and deployment, and comprehensive access management.  

• The security risks of applications relate directly to their operation: inaccurate or biased AI decisions, errors in model behavior, and data leakage. Constant monitoring and continuous training of the model and access control are required.  

• Infrastructure risks relate to the hardware and networking components used for developing and hosting AI systems. Protecting the infrastructure involves isolating AI workloads, exercising strict control over network components, and maintaining continuous traffic inspection.  

These areas should not be considered individually but rather as part of a holistic security strategy that integrates proactive monitoring, governance frameworks, and adaptive defenses.  

Preemptive cybersecurity 

Preemptive cybersecurity (PCS) represents a shift from reactive defense to proactive protection, leveraging AI-driven techniques to anticipate, disrupt, and neutralize threats before they can affect systems, data, or operations. Traditional detection-and-response models are no longer sufficient, as attacks increasingly operate at machine speed and target diverse environments including cloud infrastructures, IoT networks, enterprise applications, and critical operational systems.   

PCS combines multiple advanced strategies:  

• Advanced Cyber Deception (ACD) uses decoy systems, fake credentials, and simulated vulnerabilities to mislead attackers, detect intrusions early, and divert malicious activity away from real assets.   

• Automated Moving Target Defense (AMTD) continuously changes system configurations and attack surfaces, making it harder for cybercriminals to exploit vulnerabilities.   

• Predictive Threat Intelligence (PTI) leverages AI and machine learning to analyze threat data, identify emerging patterns, and forecast potential attacks before they occur.   

• Automated Exposure Management (AEM) constantly scans IT environments to identify misconfigurations, vulnerabilities, or sensitive assets exposed to risk, enabling rapid remediation.  

Gartner highlights preemptive cybersecurity as a key evolution in security strategy, noting that by 2029, technology products that lack built-in preemptive security capabilities will lose market relevance as proactive defense becomes a baseline expectation rather than a competitive advantage.  

AI security platforms

AI security platforms (AISPs) provide a unified framework to protect both third-party AI services and custom-built AI applications. As enterprises increasingly deploy AI at scale, these platforms help mitigate AI-native risks such as prompt injection, rogue agent behavior, and potential data leakage.  

AISPs typically address two main areas: AI usage control focuses on governing how AI is used across the company; AI application cybersecurity is centered on securing AI-powered applications themselves.

By implementing these measures, AISPs help enterprises maintain trust, comply with regulations, and safely scale AI initiatives. Gartner emphasizes that companies without AI-specific security controls will struggle to manage AI-native threats at scale. Deloitte highlights the importance of integrating these protections early in AI development to reduce risk and ensure operational continuity.  

AI and Robotics

Experts have dubbed AI for robotics “physical AI.” The goal of AI-powered models, industrial robots, bio-robots, general robotics, autonomous vehicles, and wearables, is to have them not just follow preprogrammed instructions but to sense, decide and act. They can perceive the environment, learn from experience, and change their actions in real time. This offers the opportunity to bridge digital intelligence with the physical world. Gartner reports that 80% of warehouses will use intelligent robots by 2028.  

What distinguishes physical AI is its components: inputs from sensors, spatial understanding, and decision-making capabilities. Models rely on neural graphics, synthetic data generation, physics-based simulation, and advanced AI reasoning. Their capabilities go beyond large language models (LLMs) to become vision-language-action (VLA) models that combine computer vision, natural language processing, and motor control. This brings their functioning closer to that of the human brain.   

The construction of such robots assumes the use of special neural processors that are optimized for edge computing. This enables low-latency, energy-efficient, and real-time AI processing directly on the robot. Onboard computing also eliminates cloud dependency, which is beneficial for autonomous systems. Such robots can be trained with reinforcement learning through trial and error and imitation learning that wholly mimics real-time conditions.  

Real-world adoption of physical AI is already taking place across various industries. Amazon has scaled robotics to unprecedented levels, deploying over one million robots that work alongside human employees in its fulfillment centers. These robots are coordinated by Amazon’s DeepFleet AI system, which optimizes movement across facilities and is expected to significantly improve operational efficiency. In manufacturing, BMW is introducing autonomous systems into its factories worldwide, including AI-driven vehicles that can independently move newly assembled cars through testing and finishing stages without human involvement.   

In healthcare, GE HealthCare is advancing autonomous medical imaging by combining robotic arms with machine vision to support X-ray and ultrasound procedures, while other MedTech companies are developing intelligent robotic assistants to support patient care and automate complex surgical tasks. Together, these examples illustrate how physical AI is moving beyond experimentation and becoming an integral part of real-world operations, connecting intelligent software directly with the physical environment.  

Final words  

Artificial Intelligence continues to reshape business, technology, and society at an unprecedented pace. Companies that strategically adopt AI across their operations, workforce, tech stacks, and security will be better positioned to capture value, drive innovation, and maintain a competitive edge.  

DigitalMara remains deeply in touch with progress in Artificial Intelligence. We help companies integrate and adapt AI into their core systems, turning experiments into scalable, value-driven solutions. Learn more about our expertise and AI development services and discover how your business can leverage AI to unlock new opportunities.