According to the latest report from Deloitte, titled "Tech Trends 2026," artificial intelligence has evolved beyond mere experimentation and pilot projects, emerging as a foundational environment for business rather than just a tool. The report highlights a significant shift in how organizations approach AI, moving from the question of "what can we do with AI?" to "how quickly can we turn AI into sustainable and measurable business outcomes?" As the pace of technological change accelerates, companies find that the time for contemplation is quickly outpaced by the relevance of the technology itself.
One of the report's core insights is that technological advancements are no longer linear but are instead amplifying one another, creating a snowball effect. Improved models lead to new applications, which generate more data, attracting investments that enhance infrastructure, thereby reducing costs and encouraging further experimentation. This rapid evolution has drastically compressed traditional business cycles, with AI startups achieving significant revenues much faster than their Software as a Service (SaaS) predecessors. The "half-life of knowledge" in AI has shrunk from years to mere months, as emphasized by a CIO interviewed in the study, who noted that the time spent learning new technologies now exceeds their practical applicability.
This rapid shift presents a challenge for organizations structured around gradual improvements and careful scaling, often leading them to fall behind. The winners in this new landscape are those who can continuously adapt and make architectural decisions even before they are fully confident in their correctness.
Moreover, AI is no longer confined to screens and dashboards; it is increasingly taking on physical roles in various industries. Today, AI oversees robotic fleets in warehouses, manages autonomous vehicles in manufacturing, and operates drones and medical assistance systems. For example, Amazon coordinates over a million robots through a single AI system, while BMW employs autonomous vehicle movement within its factories. Deloitte predicts that humanoid robots will become commonplace in operational processes over the next decade, moving beyond mere prototypes to millions of devices in real-world applications.
However, the report also acknowledges limitations such as security, workforce training, cybersecurity risks, and reliability. Despite these challenges, decreasing costs and maturing technologies are making physical AI a reality rather than an experimental concept.
A significant focus of the report is on agent-based AI—autonomous systems capable of performing complex tasks without constant human input. Yet, a gap exists between expectations and current capabilities, with only about 11% of companies using AI agents in actual production environments, while nearly 40% are still in pilot phases. This gap indicates that many organizations are trying to fit agents into existing processes instead of restructuring those processes to accommodate new operational logics. Successful implementation of agent-based AI necessitates a new architecture, different management principles, fresh roles, and clear accountability rules; without these, many projects are likely to fail or remain too risky for scaling.
In terms of economics, the AI landscape is shifting from a focus on training to inference. Despite a significant decrease in inference costs, overall expenditures on AI continue to rise, with companies spending tens of millions of dollars annually. This increase is driven by the need for constant and widespread inference processes, pushing businesses to seek hybrid, specialized, and more manageable computing environments.
The role of IT functions is also transforming. The report notes that many large companies no longer view IT merely as a service center but as a source of growth and competitive advantage. CIOs increasingly report directly to CEOs, and organizations are revising their operational models to prioritize modularity and observability over outdated legacy structures. The key takeaway is that modernization should begin with addressing business problems, as investments in AI without a clear value proposition can quickly become costly experiments with little return.
Furthermore, AI is reshaping not only technology but also work structures. New roles emerge that require collaboration between humans and autonomous systems. Coding agents significantly boost developer productivity but complicate issues of contribution assessment, accountability, and outcome management. The focus is shifting from merely writing code to formulating tasks, managing complex systems, and making decisions based on outcomes rather than processes. Deloitte warns that companies hesitant to invest in talent development may fall behind those committed to adaptability and learning.
In the realm of cybersecurity, AI presents both challenges and solutions. While it amplifies cyber risks, it also provides tools for protection. Leading companies utilize AI for threat modeling, automated threat detection, and adaptive defense measures. The report emphasizes that security must be built into AI architectures from the outset; retrofitting security after the fact significantly diminishes scalability prospects.
Deloitte's report also points to emerging signals that could shape future trends, including potential slowdowns in fundamental model progress, the growing importance of synthetic data, advancements in neuromorphic computing, edge AI, wearable AI devices, biometrics, and privacy changes, as well as the transition from traditional SEO to optimization for generative search engines.
In conclusion, the central message of "Tech Trends 2026" is clear: success will not come from having the most advanced AI technology but from being the most agile and bold in restructuring organizations for a new reality. AI is no longer a mere add-on but the environment in which businesses must operate, signaling a transformative era for the market and its competitors.
Informational material. 18+.
