Don't Fall Behind in 2025: International Competition Makes the Case for 'Intelligent Engineering' European tech companies rank high with advanced skills, but that's not always enough to make the cut.

When it comes to software engineering in Europe, the industry is closing the doors of 2024 at something of an impasse.

The year has seen increasing competition from deep tech companies in the U.S., which are generating three times as many companies, according to a report from Zeki Data. Yet despite the high volume of companies coming from across the Atlantic, the report suggests that European companies hold an important edge when it comes to the talent and advanced skills that the developers and engineers here have.

On the one hand, this means that the expertise held within European tech companies are well positioned to address market needs and create innovative digital solutions. However, the challenge that comes from the volume of international competitors is too large to be ignored.

This trend highlights the importance of finding more efficient solutions to support development teams that allow digital products to be released quickly without compromising on quality. Here, an emerging methodology known as "intelligent engineering" promises to help European deep tech companies address the competition by saving time and resources across the development cycle.

The intelligent engineering approach will also help European companies tackle several prevailing megatrends that have had a fundamental impact on what a typical release cycle entails. These trends have made the process significantly more complex. It has also compartmentalized the development pipeline in such a way that traditional productivity frameworks offer limited improvements on overall performance.

Let's take a closer look at the trends having an impact on software engineering in Europe and why Intelligent Engineering has the ability to increase the velocity of release cycles for high-end apps and digital products.

Cloud computing adds complexity

The rise of cloud computing has been stratospheric in recent years. The global cloud computing market is growing rapidly at a CAGR of 16% to 19%, driven by SaaS, PaaS, and IaaS adoption across industries like IT, telecom, healthcare, and manufacturing. Public cloud services revenue is expected to exceed $800 billion by 2024, with the total market nearing $1 trillion soon after, fueled by hybrid models and AI integration.

The rise of cloud computing has been transformational, particularly since the COVID-19 pandemic accelerated its adoption. Organizations turned to cloud technologies to support distributed teams, ensuring business continuity through hybrid storage models.This shift has permanently reshaped computing architectures, data storage, and operational processes, enabling greater flexibility and cost savings. Cloud computing now drives real-time data usage, as seen in scenarios like predictive analytics for supply chain management, integrating IoT, on-prem, and cloud-stored sales data.

However, challenges persist. Collaboration across software, cloud, and IT teams often introduces delays, unclear task ownership, and inefficiencies. Without continuous pipeline evaluation, quality issues and bottlenecks can hinder progress.

Service-driven architectures drain resources

Next, the Service-Oriented Architecture (SOA) continues to shape the software development landscape in 2024, with its focus on flexibility, reusability, and interoperability gaining prominence as businesses strive for agility in a digital-first world.

The rise of cloud computing has transformed development environments and tools, enabling faster system operations through cloud-hosted networks. Developers increasingly rely on cloud provider tools to create native apps and services efficiently, avoiding the need to build codebases from scratch.

While this approach can save time and resources, the shift toward SOAs introduces challenges. Development teams must navigate governance and security complexities as programs combine existing assets and third-party elements. This leads to frequent release cycles, patches, and updates, consuming time and resources. Missed updates can compromise quality, leaving engineers less time for tasks that enhance value and improve velocity.

Citizen programmers add to security risks

The rise of cloud computing and SOAs combined have also opened the development environment to citizen programmers. This refers to the practice of non-IT professionals leveraging low-code and/or no-code platforms to develop custom applications, thereby easing the burden on IT departments and increasing operational efficiency.

However, in parallel to the above trends, this change has placed even more pressure on the development cycle and the ability of engineers to maintain quality code and reliable products.

This is because low code/no code platforms allow non-professional developers to customize apps without understanding the underlying principles of software development that senior engineers are ahead of. As a result, the presence of citizen programmers has also increased the amount of security risks or poorly designed products that can undermine the overall quality of the software product. At worst, this trend also creates significant security risks for organizational and customer data if not managed properly.

Unfortunately, the task of doing so falls to the development teams. This adds another dimension to the already slow rates of production and another layer of complexity to manage in order to maintain quality.

AI and automation tools offer agility

Finally, as with the majority of industries today, the evolution of sophisticated AI-powered tools is making waves within software engineering. Specifically, GenAI and advanced large language models have incredible new capabilities when it comes to generating code and helping out with a number of development tasks.

AI systems can boost developer productivity, reduce coding errors, and accelerate software release cycles. While AI handles basic coding tasks, developers can focus on complex programming challenges and code reviews.

This becomes a huge velocity accelerator. On the one hand, this has the potential to support software engineers in incredible new ways. However, the application of the technology must be managed carefully to avoid associated risks. To understand which tasks can be outsourced to AI and automation to reduce the burden placed on development teams, we need to start with data.

Intelligent engineering starts with data-driven insights

The influence of these overarching megatrends is forcing the industry to confront a new paradigm of software engineering. Here, a data-driven approach is fundamental to an organization's ability to deliver high-quality software products at speed and remain in pole position. This is an approach we refer to as intelligent engineering. Intelligent engineering uses end-to-end data analysis to improve engineering productivity. It uses an unprecedented level of insight to understand pinpoint sources of waste and gets to the root cause of performance issues.

With this information at hand, European business leaders can process changes that are required and know when to apply AI and automation tools. The combined result delivers a powerful boost to the velocity of development cycles without compromising quality. Intelligent engineering isn't just about adapting to change; it's about leading it.