The Future of Technology: What trends will shape the IT industry in the coming years?

The technology landscape is evolving at a pace that seemed impossible just a decade ago. For IT decision-makers – Chief Technology Officers (CTOs), Program Managers, Team Leaders, as well as key partners in HR and Procurement – understanding the fundamental trends shaping the future is no longer a matter of curiosity, but a strategic necessity. Technologies that seem like novelties today will become the foundation of business operations, competitive advantage and risk management in the coming years. Consciously anticipating these changes, investing in the right competencies and infrastructure, and adapting organizational strategies are the keys to success in a dynamic world by 2030. This article examines the key technology trends that will define the coming era in IT, providing the perspective needed to make forward-looking decisions.

Looking to the 2030 horizon, several key technologies are emerging as driving forces that will fundamentally transform the way companies design, build, deploy and manage IT systems. Their synergy and interaction will create new opportunities, but also new challenges. Undeniably, Artificial Intelligence (AI) and machine learning (ML) will be at the center of this transformation, permeating nearly every aspect of business operations, from process automation to hyperpersonalization of the customer experience. In parallel, the way data is processed will be revolutionized by Edge Computing, which will move computing power closer to data sources, enabling real-time analysis and supporting the growth of the Internet of Things (IoT). Cloud computing will remain the foundation for these technologies, evolving into hybrid and multi-cloud models that offer flexibility but also require advanced management. In this increasingly complex and connected world, cybersecurity will cease to be an add-on and become an integral part of system design and a key factor in building trust. These four pillars – AI, Edge, Cloud and Security – will form the core of the technological revolution, around which other important trends will crystallize. Understanding their potential and implications is the first step for any IT leader planning strategy for the coming years.

Moving from the big picture to the details, it’s worth taking a closer look at the force that seems to drive most innovations….

Why will artificial intelligence become the foundation of digital enterprise transformation?

Artificial intelligence has ceased to be a futuristic vision and has become a viable strategic tool that will form the absolute foundation of most enterprises’ digital transformation by 2030. Its ubiquity stems from its unique ability to analyze huge data sets, draw conclusions, automate complex tasks and create personalized experiences at an unprecedented scale. For CTOs, AI offers the ability to optimize operations, create new business models and increase efficiency. For Program Managers, it is becoming a tool to help manage projects and allocate resources. AI is driving hyper-automation, taking over not only simple, repetitive tasks, but also increasingly complex decision-making processes in areas such as finance, logistics and customer service (e.g., through advanced chatbots and virtual assistants). It enables deep data analysis (Advanced Analytics), uncovering patterns and trends invisible to the human eye, leading to better forecasts, more accurate strategic decisions and identification of new market opportunities. What’s more, AI is the key to hyperpersonalization, allowing companies to deliver customized products, services and communications to customers in real time, building loyalty and competitive advantage. However, fully realizing AI’s potential requires not only advanced algorithms, but also the right infrastructure, access to high-quality data and MLOps competencies to effectively implement and manage the models. It is strategic investment in these areas that will determine which companies will most effectively use AI as a lever for transformation.

While AI processes data to produce valuable insights, the fundamental question is: where should this processing take place to be most effective, especially in scenarios that require immediate response? The answer to this question leads us directly to another key trend….

How will edge computing revolutionize real-time data processing?

The traditional data processing model, based on sending information to a centralized cloud, faces limitations in applications that require minimal latency and immediate analysis. This is where Edge Computing– processing data at the edge of the network, close to where it originates – comes in. This architectural concept has the potential to revolutionize the way we process data in real time, opening the door to new opportunities in areas such as the Internet of Things (IoT), autonomous vehicles, smart cities and Industry 4.0. Moving computing to the “edge” of the network allows minimizing latency, which is critical in systems that require immediate response (e.g., machine control in a factory, video analysis in security systems). It also makes it possible to reduce the load on the backbone network, since not all data needs to be sent to the central cloud – at the edge it can be pre-processed, filtered and aggregated. What’s more, processing data locally can increase the security and privacy of sensitive information because it doesn’t leave the protected environment (e.g., factory, hospital). Edge computing is also key to ensuring the reliability of systems in the event of connectivity issues with the central cloud. For CTOs and Architects, implementing an edge strategy means managing distributed infrastructure, ensuring the security of edge devices, and integrating edge computing with central cloud analytics. This is a complex challenge, but the potential benefits in terms of faster, more responsive and efficient systems make edge computing one of the key components of the IT infrastructure of the future.

It is worth emphasizing that edge computing is not an alternative to the cloud, but a natural complement to it. This synergy is leading many organizations toward a more nuanced approach to infrastructure, as reflected in the growing popularity of…

Will hybrid cloud be the dominant deployment model for IT solutions?

As organizations mature in their approach to cloud computing, it is becoming increasingly clear that the “all in one public cloud” model is not always the optimal solution. Instead, the hybrid cloud, which combines the advantages of the public cloud (scalability, flexibility, innovative services) with the benefits of private infrastructure (control, security, regulatory compliance, cost optimization for fixed workloads), appears likely to be the dominant model for deploying IT solutions in the coming years. This approach, often augmented by a multicloud strategy (using multiple public cloud providers), offers organizations the greatest flexibility to tailor infrastructure to specific business and technical needs. It allows individual applications and data to be placed where it makes the most sense – critical systems requiring full control can remain on-premise or in the private cloud, while new, scalable applications can use public cloud resources. Hybrid cloud also makes it easier to gradually migrate existing systems to the cloud, and to meet stringent regulatory requirements for data sovereignty or security. For Purchasing Directors, the hybrid and multi-cloud model gives greater negotiating power and avoids dependence on a single vendor (vendor lock-in). However, managing a complex hybrid and multi-cloud environment presents significant operational and technical challenges. It requires advanced orchestration, monitoring and cost management (FinOps) tools across heterogeneous environments, as well as a consistent security strategy that spans all domains. DevOps and DevSecOps teams must be competent in various cloud platforms and integration technologies. Despite these challenges, flexibility and optimization capabilities are making hybrid cloud the preferred model for many enterprises looking for a sustainable approach to IT infrastructure.

Managing such complex, distributed environments inevitably raises the bar on security, making it not just a technical requirement, but a fundamental pillar of trust….

How will cyber security affect system design and customer confidence?

In the digital age, where data is the new currency and IT systems are the lifeblood of businesses, cybersecurity is no longer just a technical aspect of operations, but is becoming a strategic imperative that fundamentally affects how systems are designed, how risks are managed and, most importantly, how trust is built and maintained with customers and business partners. Looking ahead to 2030, the importance of cyber security will only grow, and its integration throughout the product lifecycle will become an absolute necessity. The growing scale and sophistication of cyber attacks, driven by AI, among other things, make the traditional reactive approach to security insufficient. Companies need to move to a proactive “security by design” model, where security is built into systems from the very beginning, at the architecture and design stage, rather than added as a later layer of protection. This implies DevSecOps practices, threat modeling, secure coding and continuous security testing throughout the development process. For CTOs and Team Leaders, this means investing in tools, training and building a security culture within teams. Cyber security also has a direct impact on customer trust. Data breach incidents can lead to irreparable reputational damage and customer churn. That’s why companies must not only implement robust safeguards, but also transparently communicate their data protection and security practices, building an image as a trustworthy partner. Increasing regulatory requirements (such as GDPR/RODO, CCPA, AI Act) further reinforce the need to prioritize security and privacy, and failure to meet these requirements risks serious financial penalties. Finally, security is becoming a key criterion for evaluating suppliers and business partners. Chief Procurement Officers are increasingly requiring their suppliers (including IT services companies like ARDURA Consulting) to document mature security practices and hold appropriate certifications (e.g. ISO 27001, SOC 2). In the future of IT, systems will not only need to be functional and efficient, but above all secure and trustworthy.

While robust security protects the systems being developed, the development process itself is also evolving, striving for greater speed and accessibility to a wider range of users, leading us to the growing importance of…

Why will low-code and no-code become tools for democratizing software development?

In the face of growing demand for business applications and a concomitant shortage of skilled developers, low-code and no-code (LCNC) platforms are emerging as a powerful trend that has the potential to democratize the software development process, enabling people without deep programming knowledge (so-called citizen developers) to build applications as well. In the coming years, LCNCs will become an important tool in the arsenal of IT departments, accelerating digitization and allowing professional developers to focus on more complex tasks. LCNC platforms offer visual drag-and-drop interfaces, predefined components and templates that allow rapid development of business applications, forms, workflows or simple automations with minimal (low-code) or no (no-code) traditional coding. For the business, this means a significant acceleration of time-to-market, especially for simpler internal applications or prototypes. It also makes better use of IT resources, relieving professional developers of the burden of developing less complex tools and allowing them to focus on strategic, complex projects. What’s more, LCNCs enable business employees to directly create tools that address their specific needs, increasing their engagement and speeding up problem solving. For HR Partners, LCNCs can be seen as a way to partially alleviate the IT talent shortage. However, the democratization of software development also brings challenges. There is a risk of so-called “shadow IT, “ i.e., applications developed outside the control of the IT department, which can lead to security, quality, integration and governance issues. It is therefore necessary to implement an appropriate governance framework for LCNC platforms, including standards, security policies, application review and approval processes, and the provision of support and training for citizen developers. Despite these challenges, LCNCs’ potential to accelerate innovation and increase organizational agility means that they will become an increasingly important part of many companies’ technology strategy.

The democratization of application development is one manifestation of technological evolution. Another area that has been generating interest and promising fundamental changes for years, although its adoption has been slower, is distributed registry technology….

Will blockchain go beyond finance and change the rules of cooperation between companies?

For years, blockchain technology, inextricably linked to cryptocurrencies, has raised high hopes of revolutionizing many sectors with the promise of decentralized, transparent and immutable transaction records. Although the initial media hype has subsided somewhat, and adoption outside the financial sector (FinTech) has been slower than expected, blockchain’s potential to change the rules of business-to-business collaboration remains significant and is likely to materialize in more targeted applications in the run up to 2030. Moving beyond the world of finance, however, requires finding real business problems that blockchain solves better than existing technologies. One promising area is supply chain management, where blockchain can provide transparency and traceability in the flow of goods from producer to consumer, increasing trust and making it easier to verify the origin or authenticity of products. Another field of application is Decentralized Identity (DID) Digital Identity Management, where blockchain can give users greater control over their personal information and enable secure, verifiable sharing of identity attributes without relying on central intermediaries. There is also potential in protecting intellectual property, authenticating documents or creating decentralized data markets. For organizations, however, implementing blockchain-based solutions comes with challenges such as scalability, interoperability between different blockchain platforms, implementation costs and regulatory issues. It will also be crucial to move away from viewing blockchain as a one-size-fits-all solution and focus on identifying specific use cases where its unique features (decentralization, immutability, transparency) add real value. While the mass revolution may not come as quickly as anticipated, blockchain is likely to find its niches where it will improve and secure inter-organizational collaboration.

While blockchain bases its security on existing cryptographic standards, there is a technology on the horizon that could fundamentally change the rules of the game in computing and security….

What challenges will the spread of quantum technologies bring to IT?

Quantum computing (QC) is one of those technologies that, while still in the early stages of development, has the potential to bring about fundamental transformation, while posing unprecedented challenges to current IT infrastructure and security. Looking ahead to 2030, while full-scale, universal quantum computers may not yet be available, the development of quantum algorithms and specialized quantum devices will accelerate, forcing IT leaders to think strategically about their implications. The biggest and most discussed challenge is the threat to current cryptographic standards. Algorithms such as Shor’s algorithm theoretically enable quantum computers to break popular asymmetric encryption systems (e.g. RSA, ECC), which are the backbone of the security of today’s Internet, e-commerce and communications. This implies the urgent development and implementation of Post-Quantum Cryptography (PQC), i.e. algorithms that are resistant to attacks by both classical and quantum computers. Migration to PQC will be a complex and lengthy process, requiring significant investment and careful planning on the part of organizations and technology providers. Another challenge is the complexity of quantum technology itself and the shortage of specialists with expertise in the field. Understanding the principles of quantum mechanics and quantum programming requires a whole new set of skills. Companies that want to explore the potential of quantum computing (e.g., in drug discovery, optimization of complex systems, materials science or financial modeling) will need to invest in training or partner with specialized research centers and suppliers. Finally, there are infrastructure and cost challenges. Quantum computers are extremely expensive and require specialized operating conditions (such as very low temperatures). Access to quantum computing power is likely to be mainly through cloud platforms offered by major vendors. For CTOs and Architects, this means understanding how to integrate quantum computing (as a service) into existing IT infrastructure. While widespread adoption of quantum technologies may still be a long way off, proactively monitoring progress and preparing for its implications, especially in the area of security, is already a strategic task for prudent IT leaders.

Moving from a potentially distant quantum future to technologies that are already dynamically changing the way devices communicate and generate data, we encounter a powerful combination of…

How will 5G and IoT transform business models in the era of connected devices?

The combination of 5G technology, which offers unprecedented speed, low latency and the ability to support a huge number of devices, with the Internet of Things (IoT), a network of billions of interconnected sensors and devices, creates a powerful technology platform that has the potential to fundamentally transform business models in many industries in the coming years. This synergy makes it possible to collect, transmit and analyze data at unprecedented scale and in real time, opening the door to new services, operational improvements and more personalized experiences. In industry (Industry 4.0), 5G and IoT enable smart factories where machines communicate with each other, processes are optimized in real time, and predictive maintenance minimizes downtime. In transportation and logistics, they enable real-time shipment tracking, route optimization, autonomous vehicle fleet management and improved safety. In healthcare, they enable remote patient monitoring, telemedicine, faster diagnostics and more personalized therapies. In retail, IoT and 5G support smart shelves, personalized in-store offers, automated warehouse processes and new delivery models. In energy and utilities, they enable smart grid management (smart grids), energy optimization and remote meter readings. For companies, this means the opportunity to create new revenue streams based on data and services (e.g., subscription models, pay-per-use), increase operational efficiency by automating and optimizing processes, and build deeper relationships with customers by better understanding their needs and behavior. However, fully realizing the potential of 5G and IoT requires organizations to invest in the right infrastructure (including edge computing), IoT data management platforms, advanced analytics tools (including AI/ML), and robust cybersecurity mechanisms to protect the vast number of connected devices and the data they generate. Companies that can strategically leverage the opportunities offered by 5G and IoT will gain a significant competitive advantage in the era of connected devices.

The massive amount of data generated by IoT and the need to respond quickly to change are also driving the need for even greater efficiency in software development and deployment processes, leading us back to evolving operational practices….

Will automating DevOps processes speed up IT project lifecycles?

Automation has been at the heart of the DevOps philosophy since its inception, and its continued refinement and expansion into more areas of the software development lifecycle (SDLC) remains a key factor in further accelerating IT project lifecycles and enhancing an organization’s ability to quickly deliver business value. Looking ahead to 2025-2030, we can expect even deeper and smarter automation of DevOps processes, driven by the development of AI (AIOps) and Platform Engineering tools, among others. Continuous Integration and Continuous Delivery (CI/CD) will become even more automated and optimized, covering not only building and deploying code, but also automatic generation of test environments, advanced deployment strategies (e.g., Canary Releases, Blue-Green Deployments) and automatic rollback of changes in case of problems. Infrastructure as Code (IaC) will be the standard, allowing complex infrastructure environments to be instantly created and modified in a repeatable and auditable manner. Test automation will move beyond unit and integration testing to increasingly include performance testing, security testing (as part of DevSecOps) and end-to-end testing, often aided by AI to generate and prioritize test cases. Monitoring and observability will become more proactive with AIOps that automatically detect anomalies, correlate events and suggest or even automatically implement corrective actions. Configuration and security management will also become increasingly automated, using Policy as Code to enforce standards and compliance. This ubiquitous automation is leading to significantly reduced time from idea to deployment (lead time for changes), increased deployment frequency, and improved stability and reliability of systems (lower change failure rate, faster mean time to recovery). For Program Managers, this means greater predictability and the ability to deliver projects faster. For CTOs, it’s key to making the organization more agile and innovative. However, success in DevOps automation requires not only the right tools, but also highly competent teams capable of designing, implementing and maintaining complex automated pipelines, as well as a culture of continuous improvement and experimentation.

The drive for efficiency and optimization, a driving force behind automation, is also reflected in the technology industry’s growing awareness of its environmental impact….

Why will sustainability (green IT) become a priority for the technology industry?

In the face of global climate challenges and increasing social and regulatory pressures, sustainability, often referred to as Green IT in the IT context, is ceasing to be merely an image issue and is becoming a strategic priority for the entire technology industry. Looking ahead to the next few years, IT companies will have to focus more and more intensively on minimizing their environmental footprint, optimizing energy consumption and promoting a closed-loop economy throughout the life cycle of technology products and services. There are several key reasons for this growing focus. First, data centers and IT infrastructure are significant consumers of electricity, and their demand is increasing with the growth of cloud, AI and big data processing. Optimizing the energy efficiency of servers, cooling systems and networks is becoming a necessity for both environmental and economic reasons (reducing operating costs). Public cloud providers are already investing heavily in renewable energy sources and more energy-efficient data centers. Second, the production of electronic devices (IT equipment) involves the use of valuable natural resources and the generation of waste. Pressure is growing to design devices that are more durable, easier to repair and recycle (in line with the principles of a closed-loop economy) and to responsibly manage electro-waste (e-waste). Third, the expectations of customers, investors and employees are also playing an increasingly important role. Companies are increasingly choosing technology suppliers that demonstrate a commitment to sustainability. Investors are incorporating ESG (Environmental, Social, Governance) criteria into their decisions, and employees, especially of the younger generation, want to work for socially and environmentally responsible companies. Finally, there are increasingly stringent regulations on energy efficiency, waste management and carbon footprint reporting. For IT leaders, this means integrating sustainability considerations into decision-making processes for technology selection, system architecture, infrastructure management and vendor collaboration. Optimizing code for energy efficiency (Green Software Engineering), choosing greener cloud providers, extending hardware lifecycles, or promoting remote working (reducing commuting emissions) are just some of the activities that fit into the Green IT strategy. In the future, sustainability will become an integral part of the business and operational strategy of any responsible technology company.

Minimizing negative impact is one aspect of responsibility. Another is to look for new, positive applications of technology that can revolutionize the way people interact with the digital and physical world….

What new opportunities will AR/VR integration with enterprise systems open up?

Augmented Reality (AR) and Virtual Reality (VR) technologies, although mainly associated with the consumer market (games, entertainment), have huge, still largely untapped potential in business and industrial applications. The integration of AR/VR with enterprise systems (e.g. ERP, CRM, PLM, production management systems) in the coming years will open up exciting new possibilities in areas such as training, technical support, design, data visualization or remote collaboration. In training, VR allows the creation of immersive simulations of difficult or dangerous tasks (e.g., operation of complex machinery, medical procedures, fire training) in a safe, controlled environment, which accelerates learning and increases knowledge retention. AR, meanwhile, can provide workers with contextual instructions and information “superimposed” on the real world while performing tasks (e.g., assembly directions for a technician, patient information for a surgeon). In remote technical support, an expert can remotely “see” what a worker sees in the field (thanks to AR glasses) and provide precise visual guidance, significantly reducing troubleshooting time and travel costs. In design and engineering, AR/VR enables visualization of 1:1 scale prototypes, collaboration on 3D models in virtual space, and early detection of potential design problems. In logistics and warehousing, AR can optimize order picking processes by displaying navigation directions and product information to employees. In sales and marketing, AR allows customers to visualize products in their own environment (e.g., furniture in an apartment) before making a purchase. Integrating these technologies with existing enterprise systems is key to providing real-time access to relevant data and enabling a seamless flow of information. Challenges related to hardware cost, ergonomics, data security and the need to develop dedicated AR/VR applications still exist, but the potential benefits in terms of increased efficiency, error reduction, improved security and new collaboration opportunities mean that more and more companies will explore AR/VR applications in their operations.

Creating more immersive and contextual experiences with AR/VR is one way to improve interactions. Another fundamental approach to increasing value for users is to customize the software itself….

Creating more immersive and contextual experiences through AR/VR is one way to improve user interaction with technology. Another, perhaps even more fundamental approach to increasing user value and building a competitive advantage, is to deeply customize the software itself to their individual needs and preferences….

Will software personalization be the key to competitive advantage for companies?

In the digital age, where customers expect increasingly tailored and relevant experiences, advanced personalization of software and digital services is ceasing to be an option and is becoming a key component of competitive advantage-building strategies for companies in almost every industry. In the coming years, the ability to deliver hyper-personalized interfaces, content, recommendations and functionality will determine customer loyalty, marketing effectiveness and market success. Personalization already goes far beyond simply inserting a customer’s name in an email. By analyzing massive amounts of data (using AI and ML), companies can create dynamic profiles of users in real time, understanding their preferences, context, interaction history and predicting their future needs. Based on this, it is possible to automatically customize the app’s user interface, present the most relevant products or content, offer personalized promotions or even dynamically modify the app’s functionality depending on the needs of a specific user. In the B2B sector, personalization can mean tailoring workflows in ERP/CRM systems to the specifics of a particular customer or industry. The key to successful hyperpersonalization is the ability to collect, integrate and analyze data from multiple sources (transactional systems, CRM, behavioral data from applications, social media, etc.) and having a flexible software architecture (e.g., based on microservices) that enables the dynamic composition and delivery of personalized experiences. However, the pursuit of personalization also brings challenges related to data privacy and ethics. A balance must be struck between delivering customer value and respecting customer privacy, being transparent about data use and obtaining informed consents. Companies that can master the art of hyperpersonalization responsibly and ethically will gain a significant advantage by building stronger customer relationships and differentiating themselves from the competition.

The drive toward personalization relies heavily on the use of data, which inevitably leads us to the growing importance of legislation that seeks to keep up with technological advances and regulate how that data is used, especially in the context of the rapid development of artificial intelligence….

How will regulations (e.g., AI Act) affect IT innovation?

The regulatory landscape around digital technologies, and artificial intelligence in particular, is becoming increasingly complex and has a direct and significant impact on IT innovation strategy. Legislative acts such as the European AI Act, in addition to already existing data protection regulations (e.g., GDPR/RODO), not only impose new obligations on companies developing and deploying technologies, but also shape the direction of development and potentially influence the pace of innovation. On the one hand, the purpose of these regulations is to build public trust in new technologies, protect citizens’ fundamental rights (privacy, non-discrimination), and ensure the security and accountability of AI systems, especially those deemed “high-risk” (e.g., in the areas of recruitment, credit scoring, critical infrastructure systems). Introducing a clear legal framework can stimulate innovation in the area of “trustworthy AI” (Trustworthy AI), promoting the development of technologies that are transparent, explainable (explainable AI), resistant to error and bias. This can also level the playing field in the market, preventing unfair practices and building a more stable environment for business development. On the other hand, there are concerns that overly stringent or unclear regulations may stifle innovation, especially for smaller companies and startups that may struggle to meet complex compliance requirements. The costs associated with audits, certification, documentation and implementation of controls can be significant. There is also the risk of regulatory fragmentation at the global level, which will make it difficult for companies operating in multiple markets. For IT leaders (CTOs, Architects, Team Leaders), this means the need to consider regulatory requirements as early as the design stage of AI systems (“compliance by design”). Building competencies in AI ethics, algorithm risk management and implementing processes to ensure compliance is becoming essential. For legal and procurement departments, ongoing analysis of changing regulations and assessment of risks associated with implementing new technologies and working with suppliers is crucial. The impact of regulation on innovation will depend on the ability of legislators to strike a balance between protecting and supporting growth, and by companies to adapt and proactively manage compliance.

Regulatory issues often focus on protecting personal data and ensuring user control over their information, which directly ties into the fundamental issue of identity management in an increasingly complex digital ecosystem….

Why will digital identity management be key in service ecosystems?

In a world where more and more services are moving into the digital realm, and users are interacting with multiple platforms and applications within complex ecosystems (e.g., finance, e-commerce, healthcare, government), secure and effective Digital Identity Management (DAM) is becoming absolutely critical to ensure smooth operations, secure transactions and build trust. Looking ahead to the next few years, the importance of this area will only grow, and traditional models based on logins and passwords will prove insufficient. The need for robust identity management is driven by several factors. First, the need for security – protection against identity theft, unauthorized access and fraud is fundamental to any online service. Mechanisms such as multi-factor authentication (MFA), biometrics and behavioral analysis are becoming standard. Second is the need for a convenient and seamless user experience (UX) – no one likes to manage dozens of different logins and passwords. Solutions such as Single Sign-On (SSO) or identity federation make it easier to access multiple services with a single set of credentials. Third, increasing regulatory requirements for privacy and user control over data (e.g., GDPR/RODO) are forcing solutions that allow users to manage their data and consents in a granular way. In this context, the concepts of Decentralized Identity (DID) and Self-Sovereign Identity (SSI), often based on blockchain technology, which give users full control over their digital credentials, are gaining importance. For organizations, this means investing in advanced Identity and Access Management (IAM) platforms that can handle a variety of authentication and authorization scenarios, manage the identity lifecycle, and integrate with other systems in the ecosystem. For CTOs and Security Architects, choosing the right IAM strategy and technology becomes a key architectural decision. Ensuring robust, secure and user-friendly digital identity management will be the foundation for trust and success in future digital services ecosystems.

Building and managing such complex, secure systems as those for digital identity management, however, requires highly skilled professionals, highlighting one of the most persistent challenges facing the entire IT industry….

How will IT staffing shortages force companies to revise recruitment and training models?

The persistent and, in many areas, worsening shortage of qualified IT professionals (talent shortage) is one of the most serious challenges facing technology companies and IT departments in enterprises around the world. Looking ahead to 2025-2030, this problem will not only not go away, but will likely force organizations to fundamentally revise their existing models for recruiting, developing and retaining talent. The traditional approach of looking for candidates with years of experience and a perfectly matched skill set is becoming less and less effective in the face of massive competition and limited supply. Companies must become more creative, flexible and proactive in their talent strategies. Above all, this means a greater emphasis on internal development and reskilling/upskilling current employees. Instead of looking for “ready-made” professionals externally, organizations will need to invest in training, mentoring and academy programs

Above all, this means more emphasis on internal development and reskilling/upskilling of existing employees. Instead of looking for “ready-made” specialists externally, organizations will need to invest in training, mentoring programs and internal academies that will allow employees to acquire new, desired competencies (e.g., in the area of cloud, AI, cyber security or MLOps). It is becoming crucial to build a culture of continuous learning (lifelong learning) and create opportunities for employees to grow within the organization.

At the same time, recruitment models must become more flexible and open. Companies will have to move away from rigid requirements for education or years of experience, and focus more on assessing potential, problem-solving skills, adaptability and willingness to learn. Alternative avenues for talent acquisition will be developed, such as partnerships with universities, software bootcamps, internship and apprenticeship programs, and actively seeking talent in less obvious places (such as among industry changers). Global remote work, as mentioned earlier, also plays a key role, expanding the geographic reach of the search.

Faced with the difficulty of finding full-time employees, flexible cooperation models such as contracting (B2B) or Staff Augmentation and Team Leasing offered by specialized partners such as ARDURA Consulting will become increasingly important. They allow companies to quickly gain access to needed competencies for the duration of specific projects, without the need for a lengthy and costly full-time recruitment process. For Purchasing and HR Directors, working with such partners becomes a strategic tool for managing human resources and optimizing costs.

In addition, companies will have to invest even more in building an attractive employer brand (employer branding) and in retention strategies, offering not only competitive salaries, but also flexible working conditions, development opportunities, interesting work and an organizational culture based on trust and wellbeing. IT staff shortages are not a temporary problem, but a structural challenge that is forcing organizations to fundamentally change their thinking about talent management – from reactive search to proactive competence building and nurturing.

The change in approach to talent goes hand in hand with changes in the approach to systems architecture, where the drive for flexibility and scalability is leading to a move away from traditional monolithic structures….

Will microservices architectures replace traditional monolithic systems?

The debate over microservices architectures (microservices) versus traditional monolithic systems has been going on in the IT industry for years. While microservices have gained enormous popularity and are often seen as the default choice for modern applications, the answer to the question of whether they will completely replace monoliths is more complex. Looking ahead to 2025-2030, microservices architectures will continue to gain importance and become the dominant approach in many scenarios, but monoliths, especially well-designed ones (so-called modular monoliths), will still have their place. The choice between these approaches should be a conscious architectural decision based on project specifics, scale, business requirements and team competencies.

Microservices architectures, which involve building applications as a set of small, independent and autonomous services that communicate with each other (most often via APIs), offer a number of significant advantages. They allow independent development, deployment and scaling of individual system components, which increases the agility of development teams and speeds up the cycle of delivering new functionality. They enable the selection of the best technology for each service (polyglot programming/persistence) and increase the system’s resilience to failures (failure of one service does not necessarily mean unavailability of the entire application). For large, complex systems and organizations with multiple development teams, microservices often prove to be a more efficient and scalable approach.

However, microservice architectures also introduce significant operational and architectural complexity. Managing a distributed system, ensuring data integrity, monitoring, debugging and securing communications between services is much more difficult than with a monolith. This requires advanced DevOps/DevSecOps expertise, mature CI/CD practices and the right tools (e.g., service mesh, observability platforms). For smaller applications or teams with limited resources, the overhead associated with implementing and maintaining microservices may outweigh the potential benefits.

Therefore, a well-designed monolith, especially a modular one, where individual components are logically separated within a single code base and deployment process, can still be a reasonable choice, especially in the early stages of product development or for applications of smaller scale and complexity. It simplifies the development, testing and deployment processes.

In practice, we are also increasingly seeing hybrid approaches, where organizations start with a monolith and then gradually spin off the most critical or dynamically evolving parts of the system into microservices. The key to success, regardless of the architecture chosen, is thoughtful design, attention to modularity, clean code and solid DevOps practices. Architecture decisions should be made by Architects and Technical Leaders based on specific needs and context, rather than blindly following trends.

Designing and implementing systems, regardless of their architecture, is not only a technical challenge, but also a growing ethical responsibility that is becoming an increasingly important part of working in IT….

How will technology ethics affect the design of IT solutions?

As technologies, especially artificial intelligence, penetrate our lives more deeply and make decisions of increasing importance, technology ethics ceases to be an abstract philosophical concept and becomes a key aspect that must be considered at every stage of the design, development and implementation of IT solutions. Looking ahead to the next few years, awareness of the ethical implications of technology will grow, and technology companies will be under increasing pressure (both social and regulatory) to create solutions that are not only innovative and efficient, but also responsible, fair and trustworthy. Technology ethics covers a wide range of issues. One of the key ones is the problem of bias (bias) in AI algorithms. Machine learning models trained on data reflecting historical social inequalities can perpetuate and even reinforce these biases, leading to discrimination in areas such as recruitment, credit scoring and facial recognition systems. AI system designers need to work proactively to identify and mitigate these biases, using techniques such as data auditing, fairness-aware ML, and continuous monitoring of the systems’ impact on different social groups. Another important aspect is the transparency and explainability (explainability) of AI systems. Users and regulators are increasingly demanding an understanding of how algorithms make decisions, especially those of high importance. The development of Explainable AI (XAI) techniques is becoming crucial to build trust and enable verification of system performance. Data privacy remains a fundamental ethical challenge. Designing systems with respect for privacy (“privacy by design”), minimizing the data collected, using anonymization techniques and giving users control over their information are fundamental ethical responsibilities. In addition, there are questions about accountability for decisions made by autonomous systems, the impact of automation on the labor market, the ethical use of data in marketing and personalization, and potential abuses of technology (e.g., deepfakes, surveillance systems). For Technical Leaders, Architects and entire development teams, this means integrating ethical considerations into the design process from the very beginning. This requires not only awareness of potential issues, but also the development of internal ethical guidelines, the implementation of Ethical Impact Assessment processes, and the promotion of a culture of discussion and accountability for the technologies being developed. Technology ethics is becoming not only a matter of “doing the right thing,” but also a key component of managing reputational risk and building long-term corporate value.

Ethical considerations also apply to how we build and deploy infrastructure, especially in the context of resource-intensive technologies such as artificial intelligence, which leads us to the practical challenges of preparing IT environments….

How to prepare IT infrastructure for the rapid development of artificial intelligence?

The rapid growth and increasing adoption of artificial intelligence pose significant challenges to IT departments and their leaders (CTOs, Infrastructure Architects) in preparing the right infrastructure to handle the specific computing, data storage and lifecycle management requirements of AI/ML models. Traditional IT infrastructure often proves insufficient or inefficient for the resource-intensive tasks of training and deploying advanced machine learning models. Ensuring sufficient computing power, especially for training deep neural networks, is key. This means investing in specialized hardware, such as graphics processing units (GPUs) or tensor processing units (TPUs), which are optimized for the matrix computing used in ML. Access to these resources is most often realized through public cloud platforms (AWS, Azure, GCP), which offer flexible and scalable instances with GPU/TPU gas pedals, allowing you to pay only for the resources used. Equally important is managing the massive amounts of data used to train models. This requires the implementation of scalable and efficient data storage systems (e.g., data lakes, ML-optimized data warehouses) and efficient data processing pipelines (ETL/ELT) that provide access to high-quality training data. It is also necessary to implement robust MLOps practices and appropriate tools to manage the entire lifecycle of AI/ML models – from data and model versioning, to training and deployment automation, to monitoring model performance in production. This often requires building dedicated MLOps platforms that integrate various tools and processes. A high-bandwidth, low-latency network is also crucial, especially when transferring large data sets and handling inference in real time. Finally, security should not be overlooked – protecting training data, securing models from theft or tampering, and ensuring secure deployment and access to AI model APIs are critical aspects of infrastructure for AI. Preparing IT infrastructure for the AI era is a complex task, requiring strategic planning, significant investment (often in a cloud model), and the development of new competencies in IT and MLOps teams.

Infrastructure is the foundation, but equally important is the evolution of how people interact with increasingly intelligent systems, which brings us to the concept of a new standard in software development….

Will human-machine collaboration become the new standard in software development?

The traditional software development model, in which programmers write, test and debug code on their own, is beginning to give way to a new paradigm in which close human-machine collaboration (Human-Machine Collaboration), aided by artificial intelligence, is becoming the new standard. Looking ahead to the next few years, AI tools will be integrated more and more deeply into development processes, not replacing developers, but acting as intelligent assistants that increase their productivity, creativity and quality of work. The most visible examples of this trend are AI-based code generation tools (AI code assistants), such as GitHub Copilot, Amazon CodeWhisperer and Tabnine. These tools, trained on huge collections of code, can suggest entire chunks of code, functions and even unit tests in real time, significantly speeding up the coding process and reducing routine tasks. The programmer still plays the role of architect, verifier and decision-maker, but AI becomes an active partner in the development process. AI also finds application in automatically detecting bugs and vulnerabilities in code at earlier stages, intelligently helping refactor code to improve its quality and readability, and automatically generating technical documentation. In the area of testing, AI can help generate test cases, prioritize regression tests and analyze test results, easing the burden on testers and allowing them to focus on more complex scenarios. This synergy between human creativity, experience and the ability to solve complex problems and AI’s ability to analyze data, recognize patterns and automate tasks, leads to increased efficiency throughout the software development lifecycle. For Team Leaders, this means supporting developers’ adaptation of new AI tools, promoting a culture of experimentation and learning, and redefining some roles and processes. Human-machine collaboration does not mean the end of the programmer’s role, but its evolution into a more strategic and creative partnership with technology, which will become the standard in modern software development.