10 technology trends for 2025 that every CTO needs to know

The technology market is constantly evolving, presenting IT leaders with ever new challenges and opportunities. The year 2025 promises to be a pivotal period in which a number of technological innovations will reach maturity, fundamentally changing the way organizations design, implement and manage their IT infrastructure.

In this article, we look at the key technology trends that will shape the IT landscape in the coming year, and their potential impact on business strategy. As a chief technology officer (CTO) or leader of a technical team, understanding these trends will be key to making sound investment decisions and maintaining a competitive edge.

When analyzing technology trends for 2025, it is crucial to understand that their true transformative potential is revealed at the intersection of different technologies. Below are the most promising areas of synergy that can bring exponential value growth:

AI + Edge Computing + 5G/6G

The combination of these three trends is creating a fundamentally new data processing model. AI models running on edge devices, communicating over ultrafast 5G/6G networks, enable autonomous decisions in millisecond response times. Applications include autonomous vehicles, smart factories and next-generation security systems.

Blockchain + IoT + Green IT

Implementing decentralized blockchain registries to track and manage IoT devices can not only increase security, but also dramatically reduce energy consumption. Smart contracts can automatically regulate the energy consumption of IoT devices based on current needs and the availability of renewable energy sources.

Low-Code/No-Code + DevOps/MLOps + AR/VR

Low-code platforms integrated with advanced DevOps/MLOps pipelines enable rapid development and deployment of AR/VR applications. This combination democratizes the creation of immersive experiences, allowing domain specialists to design interactive data visualizations and simulations on their own.

Network effect of technological trends

• Individual trends provide a linear increase in efficiency
• Combination of two complementary trends yields exponential growth
• Integration of three or more trends can lead to breakthrough business models

Tracing these synergies is crucial for CTOs planning strategic investments. In the following sections, we will highlight potential interactions with other technologies as we analyze each trend.

How will generative artificial intelligence change software development processes by 2025?

Generative AI is already transforming the foundations of the software development process today, but by 2025 its impact will become even more significant. Advanced AI tools will not only assist developers in writing code, but will take over responsibility for entire stages of application development – from generating user interfaces to implementing functionality to automated testing. In 2025, developers will become more “orchestrators” of software development processes, where their role will focus on defining business goals, validating results and fine-tuning, while AI will take care of tedious implementation tasks.

Leading software tool providers are already demonstrating how generative AI can transform the development environment. The latest platforms are able to automatically transform business requirements written in natural language into functional applications. Technology corporations are using AI to automatically refactor and optimize code in their systems, achieving a 70% reduction in the time required for routine programming tasks. The latest AI models demonstrate the ability to solve complex programming problems at a level comparable to experienced developers.

Industry perspective

In the financial sector, leading institutions are using generative AI to automate the creation of standard banking code, saving thousands of hours of programmer labor per month. In healthcare, medical equipment manufacturers are deploying AI to create medical image analysis algorithms, accelerating the development of diagnostic systems by 60%. In the automotive industry, market leaders are using AI to automatically generate and test code for autonomous driving systems.

Barriers to adoption and ways to overcome them

Despite promising prospects, the implementation of generative AI in software development processes faces significant obstacles:

Quality and security of generated code: AI systems can introduce suboptimal or unsecure code fragments. Organizations should implement multi-layered verification processes that combine automated security scanners with code review by experienced developers.

Dependence on technology vendors: Dependence of development processes on external AI platforms creates vendor lock-in risk. CTOs should consider multi-vendor strategies and investments in internal AI tools trained on company code.

Resistance from development teams: programmers may fear devaluing their skills. The key is to present AI as a support tool that eliminates tedious tasks and allows them to focus on more creative aspects of programming.

Regulatory and compliance aspects

The use of generative AI in software development raises questions about intellectual property and liability for generated code. The European Artificial Intelligence Act (AI Act) classifies code-generating systems as high-risk applications, requiring transparency and human oversight. The US National Institute of Standards and Technology (NIST) is developing standards for evaluating and auditing generative AI systems in commercial applications. Organizations must implement processes to document the code’s origin and verification process.

Financing models for generative AI implementations

Implementing generative AI requires a thoughtful financial approach, depending on the scale and maturity of the organization:

Subscription model (SaaS): The most accessible option with predictable monthly costs, ideal for SMEs and teams experimenting with AI. Typical costs: $50-200/month/developer with a payback period of 4-6 months.

Hybrid model: Combination of cloud tools and on-premises solutions for sensitive data. Higher initial cost ($50-100k), but lower costs in the long run. ROI typically achieved after 9-12 months.

Enterprise model: Dedicated, customized AI solutions integrated into an organization’s entire development ecosystem. Investment of $200-500k with a payback period of 12-18 months, but highest long-term productivity.

Alternative development scenarios

Despite the prevailing trend toward widespread adoption of generative AI, alternative development paths are worth considering:

Regulatory slowdown scenario: There is a risk that intellectual property and security concerns will lead to restrictive regulations, limiting the use of AI in critical sectors. In this scenario, adoption will be uneven, with greater use in less regulated sectors.

Domain specialization scenario: Instead of universal AI tools, the market may evolve toward highly specialized solutions for specific programming languages, frameworks or industries. CTOs should monitor which approach provides greater productivity in their specific context.

Turning point – human-AI symbiosis: The most interesting alternative scenario is the evolution towards a deeper symbiosis, where instead of replacing programmers, AI becomes an “extension of the developer’s mind”, enhancing his creativity and problem-solving abilities. This model may prove to be the most productive long-term.

What you should do now

1. Run a pilot: Choose a small, non-critical project and test the capabilities of generative AI in a secure environment.
2. Invest in training: Prepare your team to work effectively with AI through training in prompt engineering and verification of generated code.
3. Develop guidelines: Create clear rules on when and how to use AI in development processes.
4. Design the architecture with AI in mind: Consider modularizing the application in a way that facilitates code generation by AI.
5. Build your own resources: Start collecting a library of proven prompts and templates for generative AI.

The key to success with generative AI

• Investment in training the team to work effectively with AI models
• Development of internal libraries and resources to improve code generation
• Adapting development processes to a hybrid human-AI model
• Building quality control mechanisms for AI-generated code
• Implementation of AI code tracing and auditing tools

Why will Edge Computing become central to companies’ IT strategies?

Current status and development prospects

Processing at the edge of the network (edge computing) is moving from the experimental phase to full maturity, becoming a fundamental part of IT architecture in 2025. The main catalyst for this trend is the explosion in the amount of data generated by IoT devices and the increasing demands of applications requiring real-time processing. Moving computing power closer to the data source eliminates latency, reduces the cost of transferring data to central computing centers, and ensures greater reliability of critical systems.

Leading cloud providers are demonstrating the potential of edge computing by enabling applications to run at the edge of 5G networks, reducing latency to levels below 10 milliseconds. Global cloud platforms offer edge solutions that integrate with their service ecosystems. In the industrial sector, advanced edge platforms enable data processing directly on production lines, increasing productivity and reducing downtime by 35%.

Industry perspective

In the manufacturing sector, smart factories are using edge computing to analyze sensor data in real time, enabling immediate detection of manufacturing defects and reducing waste by 25%. In retail, global retail chains are deploying edge computing solutions in their stores, enabling automatic detection of shelf shortages and optimizing inventory management. In healthcare, medical equipment manufacturers are using edge computing to process data from diagnostic devices locally, speeding up diagnosis and ensuring business continuity even when connectivity problems arise.

Interdisciplinary connections to other trends

Edge computing does not function in isolation – its full potential is revealed when combined with other trends:

Edge + AI: Local artificial intelligence models running on edge devices enable autonomous decisions without the need to communicate with the cloud. Example: industrial cameras that detect defects in real time.

Edge + 5G: The combination of these technologies creates a new class of real-time applications with ultra-low latency. Example: remote surgical operations.

Edge + IoT + Blockchain: Decentralized processing of data from IoT devices with blockchain verification ensures non-repudiation and immutability of critical data. Example: cold chain monitoring systems in pharma.

Financing models and TCO

Implementing edge computing requires a thoughtful financial approach:

CapEx-intensive model: Traditional approach involving purchase and maintenance of in-house shore infrastructure. High initial cost ($100-500k depending on scale), but full control and potentially lower long-term costs. Typical payback period: 24-36 months.

OpEx (Edge-as-a-Service) model: Subscription to edge services from cloud or telecom providers. Lower barriers to entry (usually billing for resources used), but potentially higher total cost in the long run. Payback period: 12-18 months.

Hybrid model: The most balanced approach, combining in-house resources for critical workloads with cloud services for variable workloads. Optimized TCO with typical ROI in 18-24 months.

Barriers to adoption and ways to overcome them

There are significant challenges to implementing an edge computing strategy:

The complexity of managing distributed infrastructure: overseeing hundreds or thousands of edge devices requires sophisticated management tools. The solution is to deploy automation and orchestration platforms such as Kubernetes at the Edge (K3s) or OpenShift Edge solutions.

Security issues: Edge devices often operate in physically unsecured locations. Organizations should implement comprehensive security strategies that include data encryption, secure boot, application isolation and continuous monitoring.

Standardization and interoperability: Fragmentation of the edge computing ecosystem makes it difficult to create consistent solutions. CTOs should look favorably on initiatives such as the Edge Computing Consortium (ECC) and the Open Edge Computing Initiative, which seek to create common standards.

Regulatory and compliance aspects

Edge computing introduces new regulatory challenges, particularly in the context of personal data processing. Global data protection regulations introduce restrictions on local information processing and data portability requirements. In addition, different jurisdictions may have different data locality (data residency) requirements, which complicates the global implementation of edge solutions. Organizations should implement mechanisms to track data flows and ensure compliance with local regulations.

Turning points and alternative development paths

While the main trend points to increasing decentralization of data processing, it is worth considering alternative scenarios:

Vendor consolidation scenario: Market consolidation around a few dominant suppliers is possible, which may reduce flexibility but increase standardization and security of solutions.

Turning point – new communication protocols: The emergence of disruptive communication protocols could radically change the architecture of edge computing, potentially introducing new models for sharing resources between devices.

Regulatory scenario: Stringent privacy regulations may accelerate edge computing adoption in some regions while complicating global deployments.

What you should do now

1. Conduct an application audit: Identify systems that would most benefit from edge processing due to latency requirements or data transfer costs.
2. Develop a tiered strategy: Define which data should be processed at the edge, which in regional data centers, and which in a central cloud.
3. Standardize the edge platform: Choose a common execution environment (e.g., containers, serverless functions) for edge applications.
4. Build competence: Conduct team training in distributed application design and edge technologies.
5. Launch pilot projects: Start with limited-scale deployments to identify operational challenges specific to your organization.

Edge Computing in practice

• Reduce data processing delays by up to 95% in intelligent automotive production lines
• Reduce the cost of data transfer to the cloud by 30-40% in advanced logistics solutions
• Improve the operational reliability of critical systems in the energy sector
• 60% faster response to events in smart city systems

How will quantum computers affect the solution of complex business problems?

Current maturity level and technology breakthroughs

Quantum computers are entering a phase of practical business applications in 2025, offering unprecedented capabilities to solve problems that remain beyond the reach of classical computer systems. Leading quantum processor manufacturers, with devices exceeding 100 qubits and plans to reach 1,000+ qubits by the end of 2025, are leading the quantum race by offering access to these capabilities through cloud platforms. The latest models of quantum processors have demonstrated “quantum superiority” in selected computing tasks, and innovative ion trap technologies are achieving the highest accuracy in quantum computing.

In the pharmaceutical industry, industry leaders are using quantum computers to simulate molecular interactions, speeding up the drug discovery process by 70%. In the financial sector, global institutions are using quantum algorithms to optimize investment portfolios, achieving a 15% improvement in risk management. Transportation companies are using quantum computing to optimize vehicle fleet routes, resulting in a 30% reduction in fuel consumption in pilot programs.

Industry perspective

In the energy sector, major corporations are using quantum computing to model resources and optimize logistics operations, achieving savings of $10 million a year in pilot projects. In aviation, aircraft manufacturers are using quantum simulations to analyze the strength of composite materials, reducing design time by 40%. In cyber security, financial institutions are investing in quantum algorithms for better anomaly detection and fraud detection.

Interdependence with other technology trends

Quantum computing is not a stand-alone trend, but rather a multiplier of the capabilities of other technologies:

Quantum + AI: Quantum machine learning algorithms can find patterns in data that are invisible to classical algorithms. This combination could revolutionize precision medicine, weather forecasting and the discovery of new materials.

Quantum + Blockchain: Paradoxically, while quantum computers pose a threat to current cryptographic methods, they can also accelerate the development of post-quantum cryptography and more efficient consensus protocols.

Quantum + Chemical Simulations: This combination could revolutionize the development of new batteries, catalysts and materials for green energy, directly supporting sustainability goals.

Models of financing and access to quantum technology

Gaining access to the computing power of quantum computers requires a strategic approach:

Cloud model (Quantum-as-a-Service): The most accessible option for most organizations. Cost: from $10,000 per year for experimental access, up to $100,000+ for dedicated computing time. Advantages: no hardware investment, payment for actual usage.

Partnership/consortium model: Sharing costs and research results with other organizations in the industry. Typical contribution: $50,000 – $250,000 per year. Ideal for industries with similar computational challenges (e.g., pharma, finance).

In-house investment model: Strategic investment in an in-house team of quantum experts and dedicated equipment. Cost: $1-5 million initially + operating costs. Feasible only for major corporations with a long-term vision for quantum applications.

Barriers to adoption and ways to overcome them

Despite the impressive prospects, quantum computers face significant barriers:

Quantum instability and decoherence: Current quantum computers require extremely low temperatures and are prone to errors. Organizations should focus on hybrid approaches that combine classical and quantum computing methods.

Talent shortage: Quantum computing specialists are extremely rare in the job market. CTOs should invest in training programs and partnerships with universities, and consider hiring consultants who specialize in identifying use cases for quantum computing.

High implementation costs: Access to quantum hardware remains expensive. The solution is to use cloud services that offer access to quantum computers, eliminating the need for infrastructure investment.

Regulatory and compliance aspects

The development of quantum computers raises serious information security concerns, as they can break most current cryptographic algorithms. The National Institute of Standards and Technology (NIST) is leading the standardization process for post-quantum cryptography, and organizations such as ETSI (European Telecommunications Standards Institute) are developing guidelines for migrating to secure algorithms. CTOs should already be inventorying cryptographic systems in the organization and planning to migrate to algorithms resistant to quantum attacks.

Turning points and alternative development paths

The future of quantum computing is not entirely predictable:

Turning point – a breakthrough in cubit stability: If technology is developed that maintains stable quantum states at room temperature, this could dramatically accelerate adoption and lower barriers to entry.

Specialization scenario: Instead of universal quantum computers, the market may evolve toward highly specialized quantum processors under specific classes of problems (chemical simulation, optimization, machine learning).

Regulatory Scenario: Export controls and restrictions on quantum research could create a strongly asymmetric landscape for access to this technology, favoring certain geographic regions.

What you should do now

1. Build quantum awareness: Educate management and key stakeholders about the potential and limitations of quantum technology.
2. Identify potential use cases: Conduct workshops with business departments to find problems that could benefit from quantum computing.
3. Establish strategic partnerships: Collaborate with quantum technology providers and academic institutions.
4. Begin quantum risk assessment: Prepare an inventory of cryptographic systems and plan the migration to post-quantum cryptography.
5. Experiment: Use cloud platforms to experiment with quantum algorithms on simplified business cases.

Business applications of quantum computers

• 50-fold acceleration of e-commerce supply chain optimization
• 70% faster discovery of new composite materials in the energy industry
• 15% improvement in accuracy of financial fraud detection in banking
• 90% reduction in calculation time in simulations of financial flows of investment institutions

What new challenges in cyber security will arise with the development of IoT technology?

The proliferation of Internet of Things (IoT) devices is creating unprecedented cyber security challenges that will require a fundamental shift in the approach to protecting IT infrastructure by 2025. Cyber security companies identify attacks on IoT devices as the fastest-growing threat vector, with a 125% year-on-year increase. Advanced security platforms detect an average of 1.5 million attempted attacks on IoT devices per day on customer systems. Security analysts predict that by 2025, 75% of all cyber attacks will be related to the IoT ecosystem in some way.

Medical equipment manufacturers have deployed advanced network segmentation and behavioral monitoring solutions for IoT medical devices, reducing vulnerability by 85%. The automotive sector is using edge platforms to secure communications between connected vehicles, achieving anomaly detection rates of 99%. Industrial automation leaders are implementing a multi-layered approach to securing industrial IoT (IIoT) devices, combining hardware-based trust modules (TPMs) with advanced behavioral analytics.

Industry perspective

In the healthcare sector, leading clinics have implemented an isolated (airgapped) environment for critical IoT medical devices, eliminating the risk of external attacks. In the energy sector, utilities are using dedicated security gateways for smart meters that block 99.7% of unauthorized access attempts. In smart cities, metropolises are implementing a distributed identity management architecture for IoT devices, ensuring secure communication between city systems.

Barriers to adoption and ways to overcome them

Securing the IoT ecosystem faces a number of challenges:

Fragmentation of security standards: The lack of consistent standards makes it difficult to implement comprehensive solutions. Organizations should engage in standardization initiatives, such as the IoT Security Foundation and the OWASP IoT Project, and implement internal security standards for IoT devices.

Device lifecycle management: Many IoT devices have limited upgrade capabilities or long life cycles. CTOs should implement Device Lifecycle Management platforms and strategies to gradually replace unsecured devices.

The scale of security monitoring: Traditional SIEM solutions are not equipped to handle the massive amount of data from IoT devices. The solution is to deploy AI-based security platforms specifically designed to monitor IoT ecosystems.

Regulatory and compliance aspects

IoT devices are becoming increasingly regulated. Europe’s Cybersecurity Act introduces security certification for IoT devices, and the US IoT Cybersecurity Improvement Act imposes minimum security standards for devices used by federal agencies. In Asia, Singapore has introduced the Cybersecurity Labeling Scheme (CLS) for consumer devices. Organizations need to keep track of evolving regulations and incorporate them into their purchasing process and risk management strategies.

What you should do now

1. Conduct an inventory of IoT devices: Develop a complete record of all IoT devices in the organization, along with their vulnerabilities and update status.
2. Implement network segmentation: Isolate IoT devices in dedicated network segments with restrictive access policies.
3. Develop IoT security standards: Create security requirements for new IoT devices to be introduced into the organization.
4. Implement behavioral monitoring: Implement security solutions based on analyzing behavioral anomalies specific to IoT devices.
5. Prepare incident response plans: Develop dedicated security incident response procedures for IoT devices.

Key IoT threats to 2025

• Ransomware attacks on IoT industrial devices will increase by 150% according to cyber security experts
• 60% of organizations will experience security breaches through IoT devices according to analyst predictions
• IoT botnets will reach capacity to generate 1.5 Tbps DDoS attacks according to network security research
• 35% of attacks on critical infrastructure will use IoT devices as an entry vector according to expert predictions

Can green IT become a major factor in reducing costs and carbon footprint?

Green IT is no longer just an element of ESG (Environmental, Social, Governance) policy, and by 2025 it will become a significant driver of an organization’s cost efficiency. Technology giants have achieved a 15% reduction in energy costs in their data centers by using AI algorithms to optimize cooling. Innovative underwater data center designs have achieved a 35% improvement in energy efficiency. Global cloud service providers reduced CO2 emissions by 28% by switching to renewable energy sources and optimizing infrastructure.

Leading data center service providers have invested in liquid cooling technology, achieving a 40% reduction in energy consumption compared to traditional air cooling methods. Modern infrastructure solutions offer a pay-as-you-go model that reduces excess capacity by 30%, which directly translates into energy savings. IT equipment manufacturers have implemented recovery and recycling programs that have reduced electronic waste by 2 million kilograms per year.

Industry perspective

In the banking sector, international financial groups have implemented Green IT strategies that have helped reduce energy costs by 25% and CO2 emissions by 40%. In retail, global chains are using sustainable IT practices to manage the cold chain, which has reduced energy consumption by 20% and maintenance costs by 15%. In pharmaceutical manufacturing, industry leaders have implemented computing infrastructure optimization programs that have contributed to a 30% reduction in carbon footprint while increasing computing power.

Barriers to adoption and ways to overcome them

Implementing green IT faces significant challenges:

High upfront costs: Transitioning to energy-efficient technologies often requires significant investment. Organizations should take a phased approach, focusing first on initiatives with the shortest payback period, such as virtualization or cooling optimization.

Lack of standardized metrics: Measuring the impact of green IT initiatives is hampered by the lack of widely recognized standards. CTOs should implement comprehensive monitoring systems, such as DCIM (Data Center Infrastructure Management), that provide detailed data on energy consumption and efficiency.

Complexity of migrating legacy systems: Legacy systems are often the least energy efficient, but the most difficult to upgrade. Containerization and gradual migration to more efficient platforms may be the solution.

Regulatory and compliance aspects

Regulations on energy efficiency and carbon footprint are becoming more stringent. The European Green Deal introduces carbon footprint reporting requirements for large organizations. In the US, the SEC (Securities and Exchange Commission) is proposing mandatory disclosure of climate-related information. Organizations such as the Open Compute Project (OCP) and Green Grid are developing energy efficiency standards for IT infrastructure, which are becoming de facto industry requirements.

What you should do now

1. Conduct an energy audit: Identify the largest sources of energy consumption in the IT infrastructure and areas of potential savings.
2. Implement efficiency metrics: Implement metrics such as PUE (Power Usage Effectiveness), WUE (Water Usage Effectiveness) and CUE (Carbon Usage Effectiveness).
3. Develop a green IT roadmap: Create a 3-5 year plan to transform infrastructure toward greater sustainability.
4. Review SLAs: Consider modifying SLAs to include periods of lower load to allow for dynamic power management.
5. Educate the team: conduct training for the IT team on sustainable practices and their impact on operational efficiency.

Green IT strategies with the greatest impact

• Consolidation and virtualization of servers (40% reduction in energy costs in the banking sector)
• Migration to energy-efficient cloud solutions (35% reduction in CO2 emissions in the FMCG industry)
• Implementation of smart energy management systems (25% energy savings in data centers)
• Use of waste heat from data centers (60% reduction in heating costs in technology campuses)

Why will low-code/no-code platforms revolutionize enterprise application development?

Low-code and no-code platforms are entering full maturity in 2025, fundamentally changing the way organizations approach business application development. Leading CRM platforms are reducing business application development time by 70% compared to traditional methods. Low-code solutions from major software vendors have achieved a 97% increase in adoption over the past two years, with more than 15 million monthly active users. Manufacturing-oriented platforms lead the most advanced deployments in the manufacturing sector, where low-code applications control production processes in real time.

Advanced service management platforms allow Fortune 500 companies to reduce their IT application backlog by 40% through low-code capabilities. Enterprise-grade providers of low-code applications enable automakers and energy companies to create complex, scalable solutions 10 times faster than with traditional methods. Automation platforms focus on optimizing business processes through no-code, helping banks and insurance companies reduce operating costs by 65%.

Industry perspective

In the banking sector, global financial institutions are using low-code platforms to digitize 85% of customer service processes, reducing service time by 50%. In healthcare, leading clinics have deployed no-code solutions to manage staff and patient schedules, resulting in a 30% increase in operational efficiency. In logistics, international carriers are using low-code applications to optimize delivery routes and manage exceptions, achieving a 15% reduction in transportation costs.

Barriers to adoption and ways to overcome them

The implementation of low-code/no-code platforms faces significant obstacles:

Scalability and performance concerns: There is a perception that low-code applications are not powerful enough for business-critical applications. Organizations should start with less critical use cases, gradually building confidence in these technologies.

Shadow IT and application fragmentation: The ease of application development can lead to uncontrolled sprawl of the application landscape. CTOs should implement a low-code application governance process (LCAP Governance) that defines standards, approval process and application lifecycle.

Integration with existing systems: Connecting low-code applications to legacy systems can be a challenge. The solution is to implement an API layer (API Gateway) and use predefined connectors available on most platforms.

Regulatory and compliance aspects

Low-code applications must meet the same regulatory requirements as traditional software. In regulated industries (finance, healthcare), it is crucial to provide audit, access control and documentation mechanisms. Platforms such as Appian and Mendix offer special compliance features for regulated industries, including change tracking, version management and role-based access control. Organizations should work with compliance departments right from the stage of selecting a low-code platform to ensure that it meets all industry requirements.

What you should do now

1. Conduct a case study analysis: Identify projects with high ROI and low risk that can pilot low-code platforms.
2. Create a center of excellence: Build a team of experts to support the implementation and standardization of low-code practices in the organization.
3. Define a governance strategy: develop rules about what applications can be created, by whom and how they are managed.
4. Invest in training: Prepare both business users and the IT team to use low-code platforms effectively.
5. Integrate with DevOps: Integrate low-code applications into existing CI/CD processes, providing quality control and version management.

Benefits of implementing low-code/no-code platforms

• 85% reduction in application development time compared to traditional coding according to insurtech sector analysis
• 65% lower total cost of ownership (TCO) of business applications in enterprise solutions
• 3x faster implementation of changes in response to changing market conditions in the manufacturing sector
• 40% reduction in reliance on scarce software talent according to technology platform survey

How will the evolution of DevOps and MLOps accelerate enterprise digital transformation?

DevOps and MLOps are entering a new era in 2025, moving beyond basic process automation toward fully autonomous software delivery platforms and AI models. Leading streaming platforms have revolutionized the approach to DevOps with advanced continuous delivery solutions that enable more than 4,000 production deployments per day. E-commerce leaders have the most advanced MLOps practices, enabling more than 150 million product recommendations to be updated daily. Global music platforms have automated 95% of their deployment processes with modern tools that integrate microservices management with DevOps practices.

DevOps platform providers offer end-to-end solutions that reduce change time by 87% at financial institutions and technology companies. Enterprise MLOps specialists enable FMCG companies and industrial manufacturers to manage hundreds of AI models in production. Continuous integration platforms help electronics manufacturers and cryptocurrency companies perform 3 million tests per day with 99.99% availability.

Industry perspective

In the financial sector, leading banks have implemented advanced DevSecOps practices, integrating security throughout the software lifecycle, resulting in a 90% reduction in vulnerabilities. In healthcare, medical device manufacturers are using MLOps to manage hundreds of AI models for diagnostic devices, ensuring regular updates and regulatory compliance. In retail, the largest chains have integrated DevOps with supply chain management, enabling 50% faster response to changes in demand.

Barriers to adoption and ways to overcome them

The implementation of advanced DevOps and MLOps practices faces significant obstacles:

Technological and organizational complexity: Integrating multiple tools and changing organizational processes is challenging. Organizations should consider implementing all-in-one DevOps/MLOps platforms, such as GitLab or Azure DevOps, which reduce integration complexity.

Skills shortage: DevOps and MLOps specialists are among the most sought-after in the market. CTOs should invest in retraining programs for existing teams and consider partnerships with companies that specialize in implementing these practices.

Resistance to automation: Fear of losing control can inhibit the implementation of automated processes. The key is to phase in automation, starting with the best-understood processes, with clear metrics for success.

Regulatory and compliance aspects

DevOps and MLOps in regulated environments require special attention. The Financial Industry Regulatory Authority (FINRA) is introducing guidelines for deployment automation in the financial sector. The FDA is developing a framework for a “Software as a Medical Device” (SaMD) model that outlines requirements for continuous deployment in medical software. Organizations must implement “Compliance as Code” mechanisms that automatically verify regulatory compliance at each stage of the pipeline.

What you should do now

1. Conduct a DevOps/MLOps maturity assessment: Identify the current level and areas for development.
2. Define metrics for success: Establish key performance indicators (KPIs) such as deployment frequency, lead time or failure rate.
3. Build an internal platform: Invest in an internal development platform that standardizes and simplifies DevOps/MLOps practices.
4. Automate testing and monitoring: Implement comprehensive test automation and production monitoring.
5. Implement gradually: Start with one team/project and gradually expand practices to the entire organization.

Evolution of DevOps and MLOps by 2025

• 95% reduction in lead time in the electronic payments industry with GitOps
• 85% reduction in frequency of failures in automotive industry thanks to chaos testing in production
• 70% reduction in operating costs in financial services through infrastructure automation
• 60% faster integration of new team members through internal development platforms

How will 5G/6G networks transform real-time data management?

The proliferation of 5G networks and the beginning of experimental deployments of 6G networks in 2025 are fundamentally changing the paradigm of enterprise computing. Leading telecom operators have deployed private 5G networks in dozens of factories, enabling real-time quality control automation using AI at the network edge. Business service providers offer Network Slicing, which guarantees dedicated network resources for critical business applications. Telecom infrastructure vendors have revolutionized the management of field resources, enabling remote monitoring and control of heavy equipment with less than 5ms latency.

Industrial solution providers are offering 5G Industrial technologies for manufacturing plants, enabling real-time data collection and analysis from thousands of sensors, which has contributed to a 30% reduction in defects. Chipset manufacturers are developing next-generation 5G modems that consume 50% less power, extending the runtime of IoT devices. Global technology companies have integrated 5G networks into Smart Factory solutions, enabling full digitization of manufacturing processes and a 25% increase in productivity.

Industry perspective

In the healthcare sector, world-class clinics are using 5G for remote surgery, where surgeons can operate using robots from other locations. In logistics, global courier companies have deployed 5G networks in distribution centers, enabling autonomous robot navigation and a 40% increase in throughput. In energy, multinationals are using 5G to manage distributed energy resources, enabling dynamic grid load balancing with microsecond precision.

Barriers to adoption and ways to overcome them

There are significant challenges to implementing 5G/6G-based strategies:

High infrastructure costs: Building private 5G networks requires significant investment. Organizations should consider Network-as-a-Service (NaaS) models that eliminate the need for large upfront investments.

Fragmentation of standards: Different 5G implementations can lead to interoperability issues. CTOs should seek to work with vendors using open standards, such as O-RAN (Open Radio Access Network).

Security and privacy: The distributed nature of 5G networks creates new attack vectors. The solution is to implement dedicated security solutions for 5G networks, such as SEPP (Security Edge Protection Proxy) and end-to-end encryption.

Regulatory and compliance aspects

5G/6G networks are subject to increasing regulation, especially in the context of national security and privacy. The FCC (Federal Communications Commission) is introducing new security requirements for 5G infrastructure. The European Electronic Communications Code (EECC) sets the regulatory framework for next-generation networks in the EU. Organizations must take into account local restrictions on 5G technology providers and data sovereignty (data localization) requirements.

What you should do now

1. Conduct a communications needs audit: Identify business processes that could benefit from the ultra-low latency and high bandwidth of 5G.
2. Consider deploying a private 5G network: For critical industrial environments, a private 5G network can provide greater control and security.
3. Customize application architecture: Redesign applications to take advantage of the edge-cloud model, maximizing the benefits of 5G.
4. Build strategic partnerships: Establish partnerships with 5G operators and industry solution providers.
5. Create a migration roadmap: Plan a phased transition from existing communications solutions to a 5G-based architecture.

Transformative potential of 5G/6G networks

• 99.9999% network reliability in critical automotive factory control systems
• 10x reduction in latency in algorithmic trading systems of leading financial institutions
• 70% reduction in data transfer costs for industrial video surveillance systems
• 50x increase in connection density in smart e-commerce warehouses

In which sectors will blockchain move beyond the experimental phase by 2025?

Blockchain technology, after years of experimentation and pilot projects, is reaching operational maturity in 2025 in several key industries. Advanced supply chain management platforms in the grocery industry can track products from farm to table in 2.2 seconds, which took days for traditional systems. Major retailers are using blockchain to monitor the supply of food products, reducing the time it takes to identify the source of contaminated products from 7 days to 2.2 seconds. Platforms for global maritime trade connect more than 150 ports and terminals, 10 global ocean carriers and thousands of logistics providers.

Corporate blockchain platforms have revolutionized the financial sector, enabling real-time interbank settlements with a 65% reduction in operating costs. Authenticity verification solutions in the luxury goods industry enable premium brands to track products from manufacturer to consumer. Blockchain companies are the foundation for modern international payment systems that reduce transfer times from 3-5 days to minutes.

Industry perspective

In the energy sector, leading utilities are using blockchain to create a peer-to-peer marketplace for surplus energy from solar panels, reducing energy management costs by 30%. In the pharmaceutical industry, blockchain platforms ensure Track & Trace compliance, enabling verification of drug authenticity throughout the supply chain. In the insurance sector, blockchain consortia automate reinsurance settlements, speeding up claims payments by 75%.

Barriers to adoption and ways to overcome them

Blockchain implementation faces significant obstacles:

Scalability and performance: Traditional blockchains have limited bandwidth. Organizations should consider second-layer (Layer 2) solutions or enterprise-dedicated blockchains, such as Hyperledger Fabric or R3 Corda, which achieve much higher throughput.

Interoperability between different blockchains: Fragmentation of the ecosystem makes integration difficult. Projects such as Polkadot and Cosmos seek to create interoperability standards that organizations should consider when choosing solutions.

Implementation and management costs: building a blockchain infrastructure can be expensive. The solution is Blockchain-as-a-Service (BaaS) models offered by AWS, Microsoft Azure or IBM, which eliminate the need to manage your own infrastructure.

Regulatory and compliance aspects

Blockchain regulation is evolving rapidly. Europe’s MiCA (Markets in Crypto-Assets) regulation introduces a regulatory framework for digital assets. In the US, the SEC (Securities and Exchange Commission) is issuing guidelines for classifying tokens as securities. Data privacy regulations (GDPR in Europe, CCPA in California) are also important and must be considered in blockchain projects. Organizations should work with legal advisors who specialize in distributed ledger technologies.

What you should do now

1. Identify specific use cases: Focus on areas where blockchain can solve real business problems, such as supply chain transparency or authenticity verification.
2. Get started with industry consortia: Join existing blockchain initiatives in your industry to take advantage of shared infrastructure and standards.
3. Conduct a proof of concept: Conduct a limited pilot project with clearly defined goals and metrics for success.
4. Integrate with existing systems: Plan how blockchain will interact with existing ERP, CRM or SCM systems.
5. Educate stakeholders: Prepare educational programs for business decision makers, explaining the value and limitations of blockchain.

Mature blockchain applications by 2025

• 85% reduction in interbank settlement disputes thanks to advanced blockchain platforms
• 65% lower administrative costs in supply chain management in supermarket chains
• 90% faster verification of authenticity of luxury products in the fashion industry
• 45% reduction in cross-border payment fraud thanks to blockchain solutions in banking

How will immersive technologies (AR/VR) affect customer interactions and business models?

Augmented (AR) and virtual reality (VR) technologies are entering a phase of mass adoption in 2025, fundamentally transforming the way companies interact with customers and employees. Technology giants are investing billions of dollars in metaverse development, creating enterprise collaboration platforms that reduce business travel costs by 45%. Advanced AR goggles are revolutionizing the manufacturing sector, enabling remote expert support and a 30% reduction in assembly errors. The latest immersive devices are introducing a new standard for spatial interfaces, integrating AR into everyday business applications.

AR applications in the furniture industry have increased sales conversion by 35% by enabling people to visualize products in their own homes. Automakers are using VR to design vehicles, reducing prototyping time by 60% and costs by 40%. Global consulting firms recruited and deployed 150,000 new employees in virtual campuses during the pandemic, demonstrating the scalability of immersive technologies.

Industry perspective

In the healthcare sector, leading clinics are using AR to support complex surgical operations, reducing the risk of complications by 40%. In education, leading universities have deployed virtual chemistry labs, enabling students to perform experiments impossible in a traditional environment. In commercial real estate, 3D visualization platforms are digitizing spaces, enabling virtual tours of properties, which has increased agent efficiency by 30%.

Barriers to adoption and ways to overcome them

Implementation of immersive technologies faces significant obstacles:

High hardware and development costs: High-end AR/VR devices remain expensive, and immersive content creation requires specialized skills. Organizations should consider mobile device-based solutions as an entry point and no-code platforms for AR/VR content creation.

User fatigue and ergonomic limitations: Prolonged use of VR devices can cause discomfort. CTOs should design experiences with short, intense sessions in mind rather than long-term use.

Integration with existing business systems: Connecting AR/VR to ERP, CRM or CAD systems is a challenge. The solution is integration platforms such as Unity Industrial or Unreal Enterprise, which offer ready-made connectors to popular business systems.

Regulatory and compliance aspects

Immersive technologies introduce new regulatory challenges, especially in the context of privacy and security. AR/VR devices collect vast amounts of biometric and user behavior data, which is subject to GDPR regulations in Europe and CCPA regulations in California. The U.S. Federal Trade Commission (FTC) has issued privacy guidelines for immersive technologies. In medical applications, the FDA classifies some AR applications as medical devices, subject to rigorous certification processes.

What you should do now

1. Identify specific use cases: Focus on areas where AR/VR can bring immediate value, such as remote support, training or product visualization.
2. Run a pilot project: Choose a limited, measurable project to build competencies and understand implementation challenges.
3. Build an interdisciplinary team: combine IT specialists with UX designers and domain experts to create effective immersive experiences.
4. Develop a data strategy: Plan how you will manage, store and secure the data generated by AR/VR applications.
5. Create a test environment: Invest in basic AR/VR equipment that will allow teams to experiment and develop competencies.

Transformational applications of AR/VR

• 40% increase in knowledge retention in VR-based training programs in retail chains
• 65% reduction in purchasing decision time in AR automotive showrooms
• 30% drop in industrial machinery maintenance costs with AR support in steel industry
• 50% increase in productivity for engineers using advanced AR goggles for aircraft inspections

Emerging technologies with a deployment horizon beyond 2025

In addition to the major technology trends that will reach maturity by 2025, there are a number of disruptive technologies at an earlier stage of development. Although their full business potential will not be unleashed until after 2025, informed CTOs should monitor these areas now and consider the first pilot projects.

Neuromorphic calculations

Inspired by the structure of the human brain, neuromorphic computers represent a fundamentally new approach to computing architecture. Unlike the traditional von Neumann architecture, neuromorphic circuits integrate processing and memory, achieving remarkable energy efficiency.

Current status: Leading research institutions are developing neuromorphic chips with millions of artificial neurons, consuming 1,000 times less power than conventional processors for AI tasks.

Potential applications: Natural language processing, autonomous robotics, real-time medical image analysis.

Implementation horizon: First commercial applications in 2026-2027, mass adoption anticipated after 2028.

Programmable materials

Materials that can change their physical properties (shape, hardness, color) in response to external stimuli or built-in algorithms.

Current status: Research laboratories have developed materials that change properties when exposed to light, temperature or an electric field. Work is underway to integrate microcontrollers with programmable matter.

Potential applications: Adaptive building structures, smart medical fabrics, self-healing surfaces in automotive and aerospace applications.

Deployment horizon: First deployments in niche applications starting in 2026, broader commercial applications anticipated after 2028.

Transmission of terahertz data

Communication in the terahertz band (0.1-10 THz) promises bandwidths in excess of 1 terabit per second, significantly exceeding the capabilities of current 5G networks.

Current status: Breakthroughs in materials science have made it possible to create prototype transmitters and receivers operating in the terahertz band. Work on miniaturization of components is underway.

Potential applications: Satellite communications, wireless data center networks, ultrafast connectivity in industrial applications.

Deployment horizon: First commercial applications expected in 2026-2027, integration with 6G after 2028.

What you should do now

1. Monitor progress: Establish a systematic process for tracking the development of these technologies through collaboration with research institutions.
2. Identify potential use cases: Conduct workshops with technical and business teams to identify possible use cases in the context of your organization.
3. Plan a budget for experiments: Set aside a small portion of the R&D budget (3-5%) for experimentation with the most promising emerging technologies.
4. Establish strategic partnerships: Identify startups and research centers that specialize in these technologies and consider early partnerships.

Preparing for the future

• Competitive advantage often comes from early adoption of emerging technologies
• Dedicate 5-10% of the R&D team’s resources to technology exploration with a horizon of more than 24 months
• Use a portfolio approach: several small experiments instead of one big investment
• Remember that not all emerging technologies will reach commercial maturity – diversify risk

Technology trends prioritization matrix by industry

Not all technology trends will have the same impact in different sectors of the economy. The following matrix will help CTOs determine their investment priorities depending on the specific industry in which their organization operates.

Financial sector

Industrial production

Health care

Retail

Energetics

Tips for using the matrix

1. Adapt to the context: While the matrix provides general guidelines, every organization has a unique set of priorities and constraints. Tailor priority assessments to your company’s specific situation.
2. Use a portfolio approach: Instead of focusing only on the highest-priority trends, consider a balanced portfolio of investments in different technologies – from those with quick returns to more transformational ones with a longer horizon.
3. Update regularly: Technology trends and their impact on industries are evolving rapidly. Conduct a review of priorities at least quarterly.
4. Analyze interdependencies: Remember that the greatest value often comes from the integration of several complementary trends, rather than isolated implementations of single technologies.

Decision matrix for the CTO

ROI: Do the expected benefits justify the cost and implementation effort?

Strategic assessment: Does the technology address key business challenges?

Market readiness: has the technology reached sufficient maturity?

Organizational capacity: do we have the competencies for successful implementation?

Risk analysis: What are the potential risks and how can they be mitigated?