1. HOW DO SOFTWARE MODERNIZATION INITIATIVES CONTRIBUTE TO THE STRATEGIC EVOLUTION OF BUSINESSES?

Software modernization is now a strategic driver to enable agility, time-to-market, and scalability; it allows aligning the application portfolio with business priorities (e.g., customer experience, multichannel integration, data analytics). It’s not just a technological issue: it’s also an ethical choice. Modernizing means responsibly responding to the increasingly complex and multifaceted demands of digitalization in both the public and private sectors, ensuring inclusivity, accessibility, sustainability, and continuous adaptability. Furthermore, modernization can contribute significantly to sustainability objectives: it can reduce the environmental impact of IT systems through optimization of computational resources, lower energy consumption, and the gradual phase-out of obsolete infrastructures. Making applications lighter, modular, and more efficient also helps reduce TCO (Total Cost of Ownership) and extend their lifecycle, with benefits in terms of economic efficiency and environmental sustainability.

3. WHICH TECHNOLOGIES, INCLUDING AI, ARE ENABLING THE EVOLUTION OF BUSINESS APPLICATIONS AND HOW ARE THEY INFLUENCING ARCHITECTURAL CHOICES, IN TERMS OF SCALABILITY, INTEROPERABILITY AND RESILIENCE?

Technologies such as containers, microservices, API management, DevOps toolchains, and cloud-native platforms (Kubernetes, serverless) are now the foundational pillars of application evolution. However, simply introducing new technologies is not enough: it is equally essential to adopt architectural patterns that are consistent with the business’s strategic vision, capable of ensuring adaptability, consistency, and scalability over time. Models such as event-driven architecture, domain-driven design, data mesh, and digital integration hubs are increasingly used to address complex and ever-evolving scenarios. Architectural choices are no longer just technical decisions: they are a strategic lever that can either enable or hinder transformation. For this reason, in many contexts, IT must not only be enabling but also take on a leadership role in the transformation, working closely with business functions to define sustainable, secure, and value-driven solutions.

Artificial Intelligence is radically transforming the way systems are designed, developed, and maintained. In the short term, AI-based tools (e.g., generative AI for coding, test automation, code refactoring) are accelerating modernization initiatives. In the medium to long term, AI will become increasingly integrated into business processes, requiring new architectures, data governance models, and a strong focus on ethics and transparency. AI is both a technological accelerator and a point of discontinuity that must be managed with intelligence and strategic vision.

Furthermore, with the arrival of Generative AI in user interactions, we can expect the emergence of new architectural models in the coming years—such as applications that integrate AI agents, conversational orchestration, or multimodal interfaces. For this reason, it is crucial for companies to build flexible architectural capabilities today, ready to embrace innovations, to avoid future challenges and fully leverage the potential of these emerging technologies.

5. WHICH ORGANIZATIONAL AND GOVERNANCE MODELS ARE PROVING MOST EFFECTIVE IN ENSURING THE LONG-TERM SUSTAINABILITY OF SOFTWARE MODERNIZATION INITIATIVES?

"Dual-mode" (or bimodal) models are emerging, where teams focused on stability coexist with others dedicated to continuous innovation. Effective governance is more about enabling than controlling: structures like a Cloud Center of Excellence, enterprise architects with an evolutionary mandate, and distributed Product Ownership by domain have proven to work well. In addition, FinOps (financial optimization for the cloud) and Platform Engineering are becoming strategic levers to ensure long-term sustainability, transparency, and cost control.

However, strong governance—while essential—is not sufficient on its own. To make software system modernization sustainable over time, it’s also necessary to adopt a lifecycle management model inspired by lean principles, aiming to reduce waste, ensure value continuity, and promote shared responsibility for quality.

This means building a culture of measurement and a ‘quality first’ mindset, applying paradigms such as:

• Shift left – to bring quality checks earlier into the development lifecycle 
• Waste management – to identify and eliminate activities that do not directly contribute to value 
• Lean delivery – to maintain high throughput while limiting work in progress

As mentioned earlier, systematically collecting the right KPIs remains critical. Finally, the adoption of modern DevOps toolchains—integrated with practices and tools that enhance Developer Experience (DevEx)—is key to helping teams work more efficiently, with higher quality and greater motivation.