Let’s consider what industry experts predict to be the upcoming technology trends.
PaySpace Magazine has written this article for those who want to keep up with the latest trends, implement cutting-edge technologies in their businesses, and stay ahead of their competitors.
1. Increased automation
Experts believe that humanity will get more tools and applications that are able to work for both businesses and consumers. Artificial intelligence (AI) is taking automation to a new level, allowing software to perform tasks that previously required continuous human input.
One of the most important developments is the growing use of AI agents. Unlike conventional applications that respond to individual commands, these systems can plan actions, use different digital tools, and complete several steps towards a specific goal. For instance, an AI agent could collect information, prepare a report, organize a schedule, or handle parts of a customer service process.
Multiple AI agents can also work together, with each responsible for a particular task. This approach is expected to make business processes more efficient and give employees more time to focus on work that requires human judgment and experience.
However, autonomous systems still need appropriate supervision. Companies must ensure that AI agents have limited access to sensitive information and require human approval for important decisions. According to Deloitte’s Tech Trends 2026 research, only 11% of surveyed organizations had AI agents in production, despite wider experimentation with the technology.
2. AI-native software development
Artificial intelligence is changing the way software is developed too. AI-powered coding assistants can now generate code, identify errors, suggest improvements, and help developers test applications. More advanced platforms allow teams to use natural-language instructions to build software and coordinate multiple AI tools throughout the development process.
This approach can make software development faster and give smaller teams access to capabilities that previously required more extensive technical resources. AI can also help maintain existing systems by identifying potential problems and suggesting solutions.
However, the technology does not eliminate the need for experienced developers. AI-generated code still requires testing, security checks, and human review. Errors can remain unnoticed, and applications may contain vulnerabilities if developers rely on automated suggestions without proper verification.
Experts expect AI-native development platforms to become increasingly important as businesses look for ways to introduce new software and improve existing digital services.
3. Enhanced human/AI interaction
There are already many uses for artificial intelligence, such as programmatic tasks, recruitment processes, customer service, and business analytics. However, companies are increasingly looking beyond general-purpose AI tools and exploring more specialized solutions.
One important development is the growing use of domain-specific language models. These systems are designed or adapted for particular industries, such as finance, healthcare, manufacturing, and law. They can work with industry-specific terminology, internal documentation, and specialized workflows, potentially delivering more relevant results than general-purpose models.
Furthermore, AI-powered chatbots are evolving into more advanced digital assistants. They can process text, voice, images, and other types of information, making communication with software more natural. Businesses can use these capabilities to answer customer questions, support employees, and manage routine requests.
Nevertheless, human involvement remains important. AI systems can misunderstand context, provide inaccurate information, or make inappropriate recommendations. Therefore, successful implementation will depend on finding the right balance between automated assistance and human decision-making.
4. Physical AI and automated transportation
Although autonomous transportation has been discussed for years, fully self-driving cars and municipal vehicles are not yet common everywhere. The technology has developed considerably, but its adoption still depends on safety requirements, infrastructure, regulations, and operating conditions.
A related development is physical AI, which brings artificial intelligence into the physical world through robots, vehicles, drones, and other intelligent equipment. These systems can collect information about their surroundings, make decisions, and perform actions with limited human intervention.
Robotics is already finding practical applications in manufacturing and logistics. For example, intelligent machines can move goods through warehouses, assist with production processes, and perform repetitive tasks in industrial facilities. Autonomous vehicles and delivery systems are also being developed to handle specific transportation and logistics activities.
The wider adoption of physical AI could make industrial operations more efficient and reduce the need for people to perform dangerous or repetitive tasks. However, deploying these technologies in unpredictable environments remains complicated. Safety, reliability, and the ability to respond to unexpected situations are still major considerations.
5. The spread of connected devices and edge computing
Connected devices proved to be a useful concept across consumer and business markets. Therefore, there is no reason to think that this technology will stop developing. The Internet of Things (IoT) continues to connect more devices, creating opportunities for smarter systems for living and working.
Smartwatches, fitness trackers, connected home appliances, industrial sensors, and smart building systems are among the devices that can collect and exchange information. Businesses can use this data to monitor equipment, manage resources, and improve operational processes.
One important development is the growing combination of IoT and edge computing. Instead of sending all collected information to a centralized cloud platform, edge computing allows devices and nearby systems to process data closer to where it is generated. This can reduce delays, lower data transmission costs, and support faster decisions.
According to Omdia’s October 2026 research, 62% of surveyed organizations had adopted edge architectures for IoT deployments. The findings suggest that connected devices are increasingly moving beyond simple data collection towards intelligent systems capable of taking action.
6. Augmented and virtual reality inclusion in more applications
Augmented reality (AR) is nothing new in the world of mobile devices. Originally, this technology gained attention through entertainment applications, particularly games. However, its everyday utilization has continued to expand.
AR can be integrated into applications to provide additional information about the surrounding environment, help consumers visualize products, and create new interaction options. For example, shoppers can use AR to see how furniture might look in their homes before making a purchase.
Virtual reality (VR) also has considerable potential. Both technologies can be successfully used in education, employee training, programming, engineering, and product design. They allow people to explore virtual environments, practice complex tasks, and test ideas without using physical equipment or materials.
Nevertheless, the industry still faces the problem of adoption costs. Specialized headsets and other hardware can be expensive, while developing useful applications requires additional investment. Therefore, wider adoption is likely to depend on more affordable devices, better user experiences, and practical applications that provide clear benefits.
7. Machine learning for scientific discovery and engineering
It is quite possible that technology will help resolve some of the most difficult scientific and industrial problems. Machine learning and artificial intelligence are increasingly being used to analyze large datasets, identify patterns, and support research that would otherwise take considerably more time.
One promising application is drug discovery. AI systems can help researchers identify potential drug candidates and analyze biological data. Similar technologies can support materials science by identifying promising compounds and helping engineers explore new product designs.
Machine learning can also contribute to agriculture. By analyzing information from sensors, satellites, and other sources, these systems can help farmers monitor crop conditions, detect potential problems, and manage water and other resources more efficiently.
However, AI-generated research results still need to be tested and validated. A promising drug candidate, for example, must go through laboratory research, clinical trials, and regulatory review before it can become an approved treatment.
The combination of machine learning, robotics, and scientific research is expected to create new opportunities across healthcare, engineering, agriculture, and other industries.
8. Increased cybersecurity and data protection
Data security remains one of the most important issues for businesses and consumers. As more activities move online and companies rely on interconnected systems, the amount of sensitive information stored and exchanged digitally continues to grow.
Artificial intelligence adds another dimension to this problem. Cybercriminals can use AI to create convincing phishing messages, automate attacks, and produce synthetic voices or images for impersonation. At the same time, organizations are using AI-powered security tools to identify suspicious activity, detect vulnerabilities, and respond to incidents more quickly.
According to the World Economic Forum’s Global Cybersecurity Outlook 2026, 87% of respondents identified AI-related vulnerabilities as the fastest-growing cyber risk over the course of 2025. This reflects the growing need to protect both traditional IT infrastructure and newly introduced AI systems.
Another important development is digital provenance. These technologies help verify the origin and integrity of software, data, and AI-generated content. They can use digital signatures, metadata, and other verification methods to establish where information came from and whether it has been modified.
However, no single technology can eliminate cybersecurity risks. Companies still need effective security policies, regular system assessments, employee training, and appropriate access controls.
9. AI infrastructure and energy efficiency
The growing use of artificial intelligence requires increasingly powerful computing infrastructure. Training and operating advanced AI models depend on specialized chips, data centers, storage systems, and high-speed networks.
As businesses introduce AI into everyday operations, the demand for computing resources continues to increase. This also creates pressure on electricity supplies, cooling systems, and data center infrastructure.
Technology companies are therefore exploring ways to make AI computing more efficient. These include developing specialized semiconductors, optimizing models, and distributing workloads between cloud platforms, on-premises systems, and edge devices.
Energy efficiency is becoming an important consideration for businesses that want to scale their AI operations without allowing infrastructure costs to grow uncontrollably. According to McKinsey’s Technology Trends Outlook 2026, investment in AI infrastructure doubled in a single year, while energy technologies attracted nearly $200 billion in investment in 2025.
The development of computing infrastructure will influence how quickly companies can adopt advanced technologies and how affordable these solutions become.
10. Even higher e-commerce sales and the rise of agentic commerce
E-commerce is a convenient way to shop, and this statement sounds more like an axiom. Using this way to shop means you can compare prices online, save time by avoiding long queues and trips between shops, and access products that may not be available locally.
Online shopping continues to develop as consumers become more accustomed to digital services. Businesses are also exploring new ways to make purchasing more convenient, improve payment experiences, and personalize product recommendations.
One emerging trend is agentic commerce. AI assistants are beginning to take a more active role in the shopping process. Instead of simply displaying search results, they can help consumers compare products, look for reviews, identify suitable offers, and potentially complete purchases on their behalf.
Research published by the National Retail Federation in January 2026 found that 41% of surveyed consumers used AI assistants to research products, while 33% used them to look for reviews and 31% to search for deals.
Payment systems will play an important role in this development. AI agents need secure ways to initiate and complete transactions, while merchants must be able to verify purchases, manage authorization, and prevent fraud. The Merchant Risk Council’s 2026 Global eCommerce Payments & Fraud Report found that 19% of surveyed merchants already had plans and solutions in place to accept agentic AI payments, while another 63% were exploring or implementing them.
These developments could change how consumers discover products and interact with online stores. However, data protection, payment security, and the ability to control purchases made by AI agents will remain important considerations.
