Digital transformation never stops. In 2025, businesses continue to grapple with challenges that hinder growth and efficiency. Rising operational costs, complex cloud management, cybersecurity issues, and difficulties in integrating new technologies remain pressing concerns. Additionally, companies are working to balance sustainability efforts with the demand for high performance. DigitalMara, an IT company specializing in full-stack development, compiled this report highlighting key tech trends for 2025, exploring the transformative potential, challenges, and opportunities that lie ahead.
Cloud management
Deloitte predicts cloud spending globally will reach $825 billion in 2025. However, as the process of adopting cloud technology accelerates, it becomes more complex. Some companies manage hybrid cloud infrastructures, balancing private computing resources with public cloud services. Others use cloud services from multiple providers to take advantage of promotions or specialized capabilities, or to avoid vendor lock-in. Furthermore, individual departments may purchase cloud software applications independently. These situations can cause issues for data integration, compliance, and security.
As companies increasingly rely on cloud services for their key processes, managing cloud investments efficiently has become more critical than ever. The complexity of incorporation means that businesses need a more structured approach to optimize their cloud spending. For this reason, a new concept called FinOps has emerged. It combines the expertise of finance and DevOps to create a comprehensive strategy for optimizing, controlling, and tracking cloud spending in real-time. FinOps bridges the gap between technical teams and financial departments and provides transparency into cloud costs. Deloitte estimates savings from implementing FinOps practices as $21 billion in 2025 alone.
AI-driven legacy systems modernization
Modernizing outdated IT infrastructure has traditionally been a complex, multi-year process that was costly and often carried significant risk. Legacy systems, often deeply embedded inside companies, are critical to operations but can hinder innovation due to their inflexibility and high maintenance costs. Mckinsey says that up to 71% of the value of a business transformation derives from technology. Artificial Intelligence (AI), and particularly Generative AI (GenAI), can transform the modernization of legacy systems, allowing businesses to achieve faster, cheaper, and higher-quality results.
GenAI addresses the challenges by automating labor-intensive tasks, such as code refactoring, documentation generation, and system integration. Beyond automation, it enhances modernization by identifying inefficiencies and optimizing workflows through advanced pattern recognition and data analysis. For instance, it can predict potential bottlenecks and recommend the most efficient actions. With these capabilities, businesses can adapt more quickly to market changes, enhance operational resilience, and unlock new opportunities for growth.
Ambient Intelligence
Even when they’re collecting large volumes of data, companies still have areas of uncertainty when it comes to interpreting and actually making use of that data in real time. This is where Ambient Intelligence comes into play. Gartner defines this as the integration of sensing technologies and AI-driven systems to create environments that are responsive to individuals’ actions and needs. This concept supports the idea of “intelligent environments,” where everyday tasks are enhanced through automated and context-aware interactions.
Ambient Intelligence can be applied to enhance operational precision and safety in critical scenarios. For example, sensors embedded in medication packaging can prevent loss, track usage, and sense when environmental conditions such as temperature fall outside required parameters, ensuring product efficacy. Similarly, sensors in food packaging enable safety tracking and tracing, reducing spoilage through temperature monitoring and real-time alerts. In manufacturing, companies can employ predictive maintenance and enhance production efficiency by using connected sensors to identify potential equipment failures before they occur. Retailers can create highly tailored shopping experiences by adjusting the shopping environment.
Experts define this technology as relatively cost-effective to implement. This is due to low-power wireless technologies like Bluetooth that enable devices to communicate efficiently without significant energy consumption. Energy harvesting allows for the use of small, battery-free tags with long lifespans, while low-cost, low-energy electronics employ chips that run on minimal power, supporting sensors and communication. All together, these components reduce the costs associated with building and maintaining smart, responsive environments.
Spatial computing
Spatial computing is the next level of interaction between the physical and digital worlds. It relies on multiple technologies including AR (Augmented Reality), VR (Virtual Reality), MR (Mixed Reality), AI, and sensor technology. This system can serve both internal and external purposes – enhancing employees’ activities and interactions with customers.
In the workplace, spatial computing enables seamless collaboration and innovation. Employees can interact with digital content in 3D spaces, whether designing complex products, conducting virtual meetings, or training in simulated environments. Such tools can fuel both productivity and creativity. On the customer side, this creates an immersive and personalized experience. It’s a new layer of interactivity for at-home or in-store shopping, games, and live events.
To implement this technology, companies must build a comprehensive infrastructure. This includes AR/VR tools, advanced sensors, and robust software platforms for creating and managing immersive spatial environments. Scalable cloud solutions are necessary to process real-time interactions. Data management plays a critical role, requiring efficient methods to securely collect, store, and process vast datasets from diverse sources. Companies should also prioritize interoperability between devices and systems and ensure robust cybersecurity measures to protect sensitive data.
Despite its potential, spatial computing faces significant limitations, including high costs for hardware, software, and infrastructure, along with challenges in scalability and the need for high-speed connectivity. Additionally, technology relies on cameras and sensors to collect vast amounts of data about users’ environments, actions, and interactions. This raises critical concerns around data protection, privacy, and security. Companies must address these issues by developing ethical and legal frameworks to ensure responsible use and build trust with users.
Software-defined hardware
Software-defined hardware is revolutionizing how products and services evolve, enabling developers to deliver continuous improvements through software updates instead of costly and time-intensive hardware upgrades. This approach transforms rigid, task-specific devices into flexible systems capable of adapting to new demands. As a result, performance and user experiences are significantly enhanced, offering greater personalization and functionality. This concept is being applied in various industries, such as consumer electronics, medical devices, automobiles, and industrial automation.
The integration of AI and Machine Learning further amplifies the potential of software-defined hardware. These technologies empower developers by automating, optimizing, and enhancing key stages of design, development, and testing. AI can analyze data from devices to fine-tune software configurations, maximizing both efficiency and performance. GenAI takes this a step further, enabling smarter, more adaptive systems that respond dynamically to user needs — all while reducing costs and complexity compared to traditional hardware upgrades.
Another critical area of innovation lies in improving human–machine interfaces — the channels through which people interact with devices. These interfaces are often a source of frustration due to their limitations. By leveraging AI, companies are enhancing these interactions, creating more intuitive and seamless communication between humans and technology.
Developing software-defined hardware requires a convergence of skills from both the hardware and software engineering fields. Teams must design modular, programmable hardware platforms capable of running a wide range of software updates. Furthermore, rigorous testing environments are essential, combining digital twins and real-world simulations to validate performance under diverse conditions. Companies may either build internal teams or opt for outsourcing software development to access specialized expertise, reduce costs, and accelerate project timelines.
Energy-efficient computing
We live in an era defined by resource-intensive technologies and a growing focus on sustainability challenges. As advancements like AI, cloud computing, and Big Data analytics drive innovation, they also significantly increase energy demands, particularly in data centers and computing infrastructure. This has made energy-efficient computing a critical priority for companies aiming to balance technological growth with environmental responsibility.
By optimizing energy use through smarter algorithms, energy-efficient hardware, and renewable energy sources, businesses can reduce their carbon footprint while maintaining high performance. These efforts not only address sustainability concerns but also lower operational costs, offering a dual benefit of environmental stewardship and financial efficiency. Energy-efficient computing is emerging as a cornerstone of green IT strategies, ensuring that the digital future is as sustainable as it is innovative.
Cybersecurity resilience
According to PwC’s 2025 Global Digital Trust Insights survey, 77% of companies plan to increase their cybersecurity budget in response to expanding vulnerabilities. The top 4 threats include cloud-related threats, hack-and-leak operations, third-party breaches, and attacks on connected products. These emerging threats highlight the growing complexity of the cybersecurity landscape, driving organizations to adopt more comprehensive and resilient security strategies. This includes threat intelligence, continuous monitoring, and zero-trust architectures.
AI and GenAI play an important role in facilitating cybersecurity. AI-powered systems enhance threat detection by analyzing vast amounts of data in real-time, identifying anomalies, and predicting potential attack vectors before breaches occur. Further, GenAI can automate the creation of defense strategies and simulate potential attack scenarios, allowing businesses to better prepare for and mitigate risks.
Final words
As the pace of digital transformation accelerates, those who embrace these trends with a clear vision and commitment to continuous innovation will thrive. Companies should move beyond adopting technology for technology’s sake and focus on embedding it into their core strategies to drive transformative change. This requires fostering a culture of adaptability, investing in a more skilled workforce, and addressing challenges such as cybersecurity, sustainability, and operational complexity.
DigitalMara stays at the forefront of emerging tech trends, enabling our clients to embrace the most innovative and effective solutions. With expertise in custom software development, we deliver tailored, forward-thinking strategies that drive our clients’ success.
