ICT Trends – How is technology changing business in 2025?

Rok 2025 prawdopodobnie przyniesie znaczące zmiany w świecie technologii, które mogą fundamentalnie wpłynąć na sposób funkcjonowania przedsiębiorstw. W obliczu dynamicznie zmieniającego się krajobrazu cyfrowego, firmy będą musiały nie tylko nadążać za innowacjami, ale też umiejętnie wykorzystywać je jako dźwignię rozwoju. Współczesna infrastruktura teleinformatyczna coraz częściej przestaje być postrzegana wyłącznie jako zaplecze techniczne – staje się strategicznym zasobem wpływającym na konkurencyjność i odporność biznesu.

W niniejszym artykule przeanalizujemy najważniejsze przewidywane trendy technologiczne roku 2025, które mogą zmienić oblicze biznesu. Skupimy się zarówno na potencjalnych korzyściach, jak i wyzwaniach związanych z ich wdrażaniem, a także zaproponujemy praktyczne kroki, które przedsiębiorstwa mogą podjąć, aby przygotować się na nadchodzące zmiany.

What key trends in ICT infrastructure will dominate 2025?

ICT infrastructure in 2025 is likely to be characterized primarily by flexibility and the ability to adapt quickly. Traditional, static IT structures will give way to dynamic environments that can be reconfigured in response to changing business needs. This transformation will be driven by several parallel technology trends.

The first of the dominant trends is likely to be the widespread adoption of hybrid and multi-cloud models (using multiple cloud providers simultaneously). Enterprises will increasingly use a mix of environments – from local data centers to private clouds to multi-cloud services from multiple public cloud providers. In parallel, we are likely to see a growing demand for cloud services that support generative artificial intelligence, requiring significant computing resources.

In parallel, infrastructure automation is expected to grow in importance. DevOps (combining software development with IT operations) and Infrastructure as Code (IaC – software-defined infrastructure) tools could revolutionize the way IT resources are managed. It is worth remembering, however, that implementing these technologies will require significant investment in retraining IT teams and overcoming organizational resistance to new ways of working.

Also not to be overlooked is the potential role of edge computing (moving computing power closer to data sources), which may reach greater operational maturity in 2025. While this technology offers promising benefits, its implementation comes with challenges in managing distributed infrastructure and ensuring its security.

How will cloud computing change the approach to IT infrastructure management?

The year 2025 may bring a fundamental change in the perception of cloud computing – it will likely cease to be merely an alternative to traditional infrastructure, becoming the dominant paradigm for managing IT resources. It is expected that enterprises will increasingly adopt a “cloud-first” or even “cloud-only” strategy, especially in the context of new initiatives and projects.

The trend toward hybrid and multi-cloud environments, which allow organizations to strategically deploy workloads between different platforms, is likely to be particularly pronounced. This approach will be particularly relevant for generative AI deployments, where organizations may need a variety of environments: specialized AI services from one provider, large computing power from another, and cost-effective data storage from a third. It is worth noting, however, that managing such a complex environment presents significant challenges. Differences in interfaces, pricing models and functionality between cloud providers can lead to significant operational complexity. Organizations will need to invest in tools to centrally manage and monitor different cloud environments, as well as develop team competencies across multiple platforms.

As the cloud grows, the role of IT teams is also likely to evolve. The classic tasks of maintaining hardware and core systems may be replaced by more strategic functions. This transformation will require not only new technical skills, but also a change in organizational culture and processes. Many organizations may find it difficult to retrain existing IT professionals, which could lead to a skills gap and increased competition for talent with cloud experience.

The IT cost accounting model is also likely to change. Instead of large, cyclical investments in infrastructure, companies are likely to move to a pay-as-you-go model and dynamic scaling of resources. While such a model offers greater flexibility, it can also lead to unpredictable costs if not managed properly. Many organizations are discovering that without proper tools and processes to monitor and optimize expenses, cloud costs can quickly spiral out of control.

How is generative artificial intelligence changing enterprise infrastructure and operations in 2025?

Generative Artificial Intelligence (GenAI) is likely to become one of the most transformative technology trends in 2025, affecting almost every aspect of businesses. Unlike traditional AI systems, which focus on analyzing and classifying data, generative models can create new content – from text and code to images, videos or audio. This fundamental difference makes GenAI applicable to virtually every department of a company, from marketing and customer service to product development and IT operations.

Przedsiębiorstwa prawdopodobnie przejdą od etapu eksperymentowania z GenAI do strategicznej integracji tych technologii z podstawowymi procesami biznesowymi. Ten przełom będzie wymagał znaczących zmian w infrastrukturze IT, aby wspierać zarówno zewnętrzne usługi AI, jak i rozwiązania wdrażane lokalnie. Organizacje staną przed strategicznym wyborem między wykorzystaniem modeli AI dostępnych jako usługa (AIaaS – AI as a Service) a wdrażaniem i dostosowywaniem własnych modeli. Wybór ten będzie uwarunkowany nie tylko kwestiami technicznymi i finansowymi, ale również regulacyjnymi – szczególnie w branżach przetwarzających wrażliwe dane, takich jak finanse czy opieka zdrowotna. Firmy mogą napotkać poważne wyzwania związane z zachowaniem zgodności z przepisami o ochronie danych (np. RODO), szczególnie gdy dane wykorzystywane do treningu modeli lub w zapytaniach zawierają informacje poufne.

One of the most promising approaches is likely to be Retrieval-Augmented Generation (RAG), which combines the capabilities of generative models with access to corporate knowledge and data bases. RAG allows AI models to generate answers based on up-to-date, internal sources of information, significantly improving their accuracy and business utility. However, implementing RAG will require a robust infrastructure for indexing, searching and knowledge management, as well as mechanisms to ensure that models only have access to authorized information. Organizations may find it difficult to integrate distributed and disparate data sources, especially if some of them are in older, hard-to-reach systems.

Massive GenAI deployments are likely to present significant infrastructure challenges. Training and running advanced models requires significant computing resources, including specialized hardware such as GPUs (Graphics Processing Units) and TPUs (specialized tensor processing units). As a result, we may see growing demand for edge AI solutions that enable lighter versions of models to run directly on end devices or in local data centers. This approach can reduce latency and data transmission costs, as well as address privacy concerns. At the same time, organizations will need to strike a balance between performance and energy consumption – large language models are known for their high energy requirements, which can conflict with companies’ sustainability goals.

How is artificial intelligence revolutionizing network infrastructure monitoring and diagnostics?

Artificial intelligence (AI) in 2025 will likely cease to be an experimental technology and become an essential part of network infrastructure management. AI systems will be able to take over responsibility for the continuous monitoring of thousands of parameters, analyzing them in real time and identifying anomalies that might have escaped human attention.

A particularly significant trend is likely to be the development of AIOps (Artificial Intelligence for IT Operations – the use of AI to manage IT infrastructure), which combines artificial intelligence with traditional monitoring tools. However, the implementation of these technologies will not be without challenges. One major concern is data quality – AI systems require large amounts of correctly tagged historical data to effectively learn patterns. Many organizations may find it difficult to prepare an adequate set of training data, especially if their monitoring systems have not previously been integrated or consistent.

AI is also likely to revolutionize approaches to network optimization. Advanced algorithms will be able to analyze traffic and resource usage patterns, dynamically adjusting the configuration. It is worth noting, however, that trusting AI systems to make autonomous decisions about network configuration carries risks. Incorrect decisions by the algorithms could potentially lead to service interruptions or performance issues. Therefore, most organizations are likely to opt for a hybrid approach, where AI systems suggest optimizations, but the final decision rests with humans, at least for critical changes.

The potential role of AI in the context of infrastructure security should also not be overlooked. Intelligent monitoring systems will be able to detect unusual traffic patterns indicating potential intrusions or DDoS (Distributed Denial of Service) attacks. However, it is important to note that AI systems can also become targets for attacks – techniques referred to as “adversarial AI” (exploiting weaknesses in AI algorithms) can be used to fool security systems by injecting noise into the input data. Organizations will need to invest not only in AI solutions, but also in tamper-proofing them.

How does the Zero Trust model change the approach to corporate network security?

The Zero Trust model is likely to revolutionize the approach to corporate network security, moving away from the traditional paradigm based on the concept of a secure perimeter. The classic model assumed that everything inside a corporate network was trustworthy, while external connections required strict verification. Zero Trust introduces the fundamental principle of “never trust, always verify” – regardless of the location of the user or device.

In practice, the implementation of Zero Trust in 2025 will likely mean that every request for access to company resources will be treated as a potential threat and will require full verification. A key element of this architecture will be network microsegmentation (dividing the infrastructure into small, logically isolated segments). It is worth noting, however, that implementing such a fundamental change in security architecture comes with significant challenges. Microsegmentation requires a detailed understanding of all application dependencies and data flows in an organization, which can be extremely difficult with older, poorly documented systems.

The centerpiece of Zero Trust is likely to be advanced Identity and Access Management (IAM), which takes into account the context of the user’s actions. However, fully implementing such systems presents both technical and user experience challenges. Overly strict security policies can lead to user frustration and attempts to circumvent security. Organizations will have to strike a balance between security and usability, which may require significant investments in user training and interface redesign.

Zero Trust is also likely to introduce the concept of continuous verification – access will not be granted on a one-time basis, but continuously monitored. While this approach enhances security, it also comes with performance challenges – continuous verification can increase the load on IAM systems and introduce additional latency. In addition, implementing Zero Trust in environments with a large number of legacy systems that were not designed with this model in mind may require significant investment in upgrading or creating special intermediary layers.

How to effectively manage multi-cloud infrastructure in 2025?

Managing a multi-cloud environment in 2025 will likely require a comprehensive and systematic approach that takes into account the complexity of distributed infrastructure. A key challenge will be to ensure consistent management, security and visibility in a heterogeneous ecosystem where different cloud providers offer different interfaces, service models and security mechanisms.

The centralization of access and identity policy management is likely to become the foundation of an effective multi-cloud strategy. Next-generation IAM (Identity and Access Management) solutions will be able to enable consistent privilege management across clouds. However, it is worth noting that the implementation of such solutions will require overcoming significant technical and organizational hurdles. Differences in identity models and authentication mechanisms between cloud providers may hinder full integration. In addition, centralized identity management can create a single point of failure, posing a risk to business continuity.

Automation is likely to become an essential part of multi-cloud infrastructure management. Infrastructure as Code (IaC) tools will be able to enable the definition of resources in a declarative manner, independent of the cloud provider. However, implementing these technologies will require significant investment in training and skill development for IT teams. Many organizations may find it difficult to adapt to the “Infrastructure as Code” paradigm, especially if their IT processes are heavily based on manual operations and traditional organizational structures.

Optymalizacja kosztów w środowisku multi-cloud będzie prawdopodobnie stanowić szczególne wyzwanie. Współczesne organizacje będą mogły wdrażać dedykowane strategie FinOps (Financial Operations – zarządzanie finansami IT), które łączą narzędzia monitorowania wydatków z procesami zarządzania finansowego. Należy jednak pamiętać, że skuteczne wdrożenie FinOps wymaga nie tylko odpowiednich narzędzi, ale również fundamentalnych zmian w kulturze organizacyjnej i procesach decyzyjnych. Tradycyjne modele, gdzie koszty IT są zarządzane centralnie przez dział finansowy, mogą być nieadekwatne w świecie multi-cloud, gdzie decyzje o alokacji zasobów są podejmowane dynamicznie na wielu poziomach organizacji.

How to secure ICT infrastructure against AI threats?

With the growing use of artificial intelligence in business, a new category of cybersecurity threats is also likely to emerge – attacks that use or target AI systems. In 2025, we may face a twofold challenge: on the one hand, cybercriminals will increasingly turn to AI tools to automate attacks; on the other hand, AI systems themselves may become targets for sophisticated adversaries.

One of the potentially most serious threats will be adversarial AI attacks (methods of fooling AI systems), which involve manipulating AI systems’ inputs in ways that are imperceptible to humans, but lead to erroneous decisions by the algorithms. To counter these threats, organizations are likely to implement adversarial training techniques (training AI on potentially manipulated data). However, it is worth noting that developing effective defense mechanisms requires machine learning expertise, which may not be readily available to many organizations.

A particularly disturbing trend is likely to be the use of generative AI models to create sophisticated phishing and disinformation attacks. Deepfake (realistic but fake audio and video) technologies can enable the creation of convincing fake footage that can be used to impersonate employees. While deepfake detection tools are being developed, they remain in a constant race with ever-improving techniques for generating fake content. Organizations will need to invest in multi-level verification systems that do not rely solely on biometrics or voice recognition.

Protecting AI systems themselves from compromise will be a separate challenge. Model poisoning (intentional input of harmful training data) can lead to long-term degradation of prediction quality. Implementing rigorous training data validation procedures will be essential, but could significantly slow down the development and deployment of AI systems, creating a difficult trade-off between security and innovation.

How do you prepare your infrastructure for the challenges of edge computing?

Edge computing is likely to enter a phase of greater operational maturity in 2025, becoming an essential component of modern IT architectures. Moving computing power closer to data sources – whether to endpoint devices or distributed mini-data centers – will potentially reduce latency, save bandwidth and better control sensitive information.

Pierwszym krokiem w przygotowaniu infrastruktury na edge computing będzie prawdopodobnie zaprojektowanie odpowiedniej warstwy sieciowej. Organizacje będą mogły inwestować w modernizację sieci WAN (Wide Area Network – sieć rozległa), wdrażając rozwiązania SD-WAN (Software-Defined WAN – programowo definiowana sieć rozległa), które oferują inteligentne zarządzanie ruchem. Warto jednak zauważyć, że rozbudowa infrastruktury sieciowej dla wsparcia edge computing będzie wymagać znaczących inwestycji, szczególnie w lokalizacjach o ograniczonej infrastrukturze telekomunikacyjnej. Dodatkowo, zależność od łączności 5G wiąże się z wyzwaniami związanymi z dostępnością i niezawodnością tych sieci, szczególnie w obszarach poza głównymi centrami miejskimi.

Managing distributed edge infrastructure will pose significant operational challenges. The traditional approach, based on manual configuration and maintenance, does not scale well in an environment consisting of hundreds or thousands of edge points. Deployment of edge infrastructure management platforms is likely to be key, but the process is likely to encounter significant technical and organizational difficulties. The variety of edge devices, from simple sensors to sophisticated mini-data centers, will require flexible yet consistent management mechanisms. In addition, updating distributed edge systems can be logistically complex, especially for devices in hard-to-reach locations.

Security of edge infrastructure will require a specific approach that takes into account the physical distribution of resources. Organizations are likely to implement security mechanisms, but providing a uniform level of protection for all geographically dispersed components may be extremely difficult. Edge devices may be vulnerable to physical tampering or theft, creating additional attack vectors not available in traditional centrally managed data centers. The implementation of advanced security can be further limited by the computing power and energy resources available on edge devices.

How does the integration of IT-OT systems affect infrastructure security?

Integrating IT (information technology) with OT (operational technology – systems that control industrial processes) is likely to become a business imperative in 2025, enabling the digital transformation of manufacturing and industrial processes. The merging of these traditionally separate worlds can bring enormous benefits, but will also create new security challenges.

The fundamental challenge is likely to be the difference in priorities between the IT and OT worlds. In the IT environment, the main focus is often on confidentiality and data integrity, while for OT systems the absolute priority is availability and business continuity. These differences translate into different approaches to updates, change management and incident response. Coming up with compromise security policies that take into account the specifics of both environments can be extremely difficult. Many organizations may encounter resistance from OT teams, for whom any change to a stable operating environment represents a potential risk to critical business processes.

Microsegmentation is likely to become a key strategy for securing integrated IT-OT environments. It involves logically dividing networks into small, tightly controlled segments to minimize the potential scope of a security breach. However, implementing effective microsegmentation in OT environments can be much more complicated than in traditional IT networks. Many industrial systems use outdated communication protocols that were not designed with security in mind and can be difficult for modern security tools to monitor or control. In addition, introducing additional layers of security can potentially affect the performance and reliability of OT systems, which is often unacceptable in production environments.

Monitoring and visibility are likely to become critical security elements of integrated IT-OT environments. Organizations will be able to deploy specialized monitoring solutions for industrial systems that can detect abnormal communication patterns. Implementing effective monitoring in OT environments, however, comes with significant challenges. The need to preserve the performance of critical production systems may limit the ability to collect data or deploy monitoring agents. Additionally, interpreting data from OT systems requires domain expertise that is often not available in traditional IT security teams.

How to ensure the resilience of ICT infrastructure against cyber attacks in 2025?

Cyber resilience (cyber resilience) in 2025 is likely to go beyond the traditional notion of cyber security, focusing not only on preventing attacks, but also on the ability to recover quickly from an incident. This paradigm shift stems from the recognition that in the face of ever-evolving threats, it is virtually impossible to completely eliminate the risk of a cyber attack.

The foundation of cyber resilience is likely to be a security architecture based on the principle of defense in depth (defense in depth – multi-layered security). This means implementing multiple layers of security, both technical and process. It is worth noting, however, that there are significant challenges to implementing such an architecture. The complexity of multiple layers of security can lead to difficulties in management and increase the potential attack surface by introducing additional components. In addition, each additional security layer has the potential to impact system performance and user experience, requiring a careful balancing of security and usability.

A key element of the resilience strategy is likely to be regular incident simulations and tests. Organizations will be able to conduct tabletop exercises (table top simulations) and red team tests (simulated attacks). However, effective implementation of such programs requires not only the right tools, but also a change in organizational culture. Many organizations may encounter resistance to conducting realistic tests that could potentially affect production systems or expose inadequacies in current security features. In addition, conducting advanced simulations requires specialized skills that can be difficult to access or expensive.

The likely importance of business continuity and disaster recovery plans cannot be overlooked either. Organizations will be able to deploy advanced backup solutions that are resistant to ransomware (malware that encrypts data to extort ransom) attacks. However, implementing effective restoration strategies requires significant investments in both technology and processes. Regular testing of recovery plans, which is essential to ensure their effectiveness, can be logistically complex and costly. Additionally, for distributed multi-cloud environments, providing a consistent approach to disaster recovery can be particularly difficult due to differences in the mechanisms and tools offered by different vendors.

Action plan for companies of all sizes

To effectively prepare for the trends and challenges described, companies should tailor their approach to digital transformation according to their size, technological maturity and available resources. Below are specific action plans for three categories of organizations, with a focus on preparing for the implementation of generative artificial intelligence.

For small companies (up to 50 employees)

Short-term (6-12 months):

1. Conduct an audit of your current IT infrastructure, identifying key systems and data
2. Implement basic security features such as multi-factor authentication and regular backups
3. Consider moving core services (email, document storage) to trusted cloud providers
4. Invest in basic cyber security training for all employees
5. Conduct pilot projects with publicly available GenAI tools to identify potential use cases

Medium-term (1-2 years):

1. Develop a cloud migration strategy for remaining business systems
2. Implement basic security monitoring tools
3. Establish a partnership with an external IT security expert on a consulting basis
4. Build a basic business continuity plan that takes into account cyber attack scenarios
5. Train key employees on how to use GenAI tools effectively with security

Long-term (2-3 years):

1. Consider implementing the basic elements of the Zero Trust approach
2. Automate routine IT tasks to free up resources for strategic initiatives
3. Implement selected GenAI solutions for specific business processes (customer service, marketing)
4. Regularly test and update incident response plans
5. Consider developing guidelines for ethical and compliant use of AI

For medium-sized companies (50-500 employees)

Short-term (6-12 months):

1. Conduct a comprehensive security and infrastructure maturity audit
2. Develop a multi-cloud strategy that takes into account existing systems and future needs
3. Invest in tools to centralize identity management (IAM)
4. Implement a vulnerability management system and regular scanning
5. Establish a team to evaluate GenAI applications and develop an initial strategy

Medium-term (1-2 years):

1. Build DevOps competency and start implementing Infrastructure as Code
2. Implement network microsegmentation, starting with the most critical systems
3. Implement monitoring solutions with AI elements for anomaly detection
4. Develop a security awareness program with regular phishing simulations
5. Start pilot GenAI deployments in selected departments with measurable KPIs

Long-term (2-3 years):

1. Implement a comprehensive Zero Trust strategy
2. Build a Cloud Center of Excellence
3. Consider edge computing pilot projects for specific use cases
4. Implement advanced AIOps tools for predictive infrastructure management
5. Develop a platform to securely deploy GenAI solutions with data access control mechanisms
6. Consider implementing simple RAG (Retrieval-Augmented Generation) solutions for internal knowledge bases

For large enterprises (more than 500 employees)

Short-term (6-12 months):

1. Conduct a maturity assessment to prepare for key trends
2. Establish formal governance structures for digital transformation program
3. Start pilot deployments of Zero Trust technology in selected areas
4. Identify AI use cases in infrastructure monitoring and management
5. Establish an AI strategy team with representatives from the business and IT departments
6. Conduct infrastructure readiness assessment for GenAI requirements (GPU, data storage)

Medium-term (1-2 years):

1. Implement a comprehensive multi-cloud strategy with tools to centralize management
2. Build infrastructure automation team and IT staff retraining programs
3. Implement advanced network microsegmentation solutions across your organization
4. Expand FinOps program to optimize spending in cloud environments
5. Start deploying GenAI solutions in key business processes
6. Develop a comprehensive AI governance and oversight framework that addresses ethical and legal issues

Long-term (2-3 years):

1. Implement a comprehensive edge architecture (edge) for critical use cases
2. Integrate IT-OT systems with specific security requirements in mind
3. Build advanced cyber resilience capabilities, including dedicated recovery environments
4. Develop an internal innovation center exploring new technologies and business opportunities
5. Build advanced RAG platforms that integrate distributed enterprise data sources
6. Consider investing in specialized infrastructure to train and host your own AI models
7. Implement mechanisms to continuously test and monitor AI models for security and compliance

Regardless of the size of the organization, the key is to adopt an iterative approach that allows for incremental capacity building, testing new technologies in a controlled environment, and continually adjusting strategies based on lessons learned and changing market circumstances.

Summary

The year 2025 is likely to bring unprecedented changes to the ICT landscape, presenting businesses with both challenges and opportunities. The trends discussed in this article – from hybrid cloud and edge computing to artificial intelligence and advanced security models – are not separate phenomena, but elements of a larger digital transformation.

Flexibility and adaptability are likely to become key features of modern IT infrastructure. Static, monolithic systems will give way to dynamic, programmable environments that can evolve with changing business needs. At the same time, automation and orchestration will become essential to effectively manage the growing complexity of technology ecosystems.

Information security in the digital age will require a fundamental rethinking of traditional protection models. Zero Trust approaches, microsegmentation and advanced security analytics will no longer be optional add-ons, but essential components of a cyber security strategy. At the same time, organizations will need to build cyber resilience that will allow them to continue critical operations even in the face of inevitable incidents.

However, it is worth remembering that success in adapting to these trends will not depend solely on technology, but primarily on people and processes. Digital transformation is first and foremost a cultural and organizational change that requires commitment at all levels of the organization, from the board to line employees. Companies that manage to successfully combine technological innovation with organizational transformation will be best prepared to take advantage of the opportunities that 2025 will bring.