By Ingo Forstner, Senior Portfolio Manager at Device Insight
IT Modernization: The Missing Link to Scalable AI
Over the past few years, investments in AI initiatives have surged. Yet many projects stall, not because of a lack of ideas or technology, but because the foundation is missing. Legacy IT landscapes are struggling to keep pace with the demands of modern AI, while pressure to innovate continues to rise. As a result, IT modernization has become a prerequisite for unlocking the full potential of AI-driven business models. The real question is: How do you move from rigid legacy systems to an agile, AI-ready IT and data landscape?
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Many IT decision-makers describe the same dilemma: Up to 80% of IT budgets are tied up in maintaining and operating existing systems (Gartner 2025). These systems have often evolved over decades — technologically outdated, fragmented, and siloed. As a result, financial resources are tied up, and skilled teams are occupied with keeping systems running instead of building new capabilities. This has direct consequences. Data-driven services are difficult to implement, scalability remains limited, and time-to-market for digital products increases.
In addition to these structural challenges, several external factors are accelerating the need for action:
- 47% of IT applications are already running in the cloud, while VMware licensing costs have increased by up to 300% following the Broadcom acquisition
- 80% of industrial data cannot be used for data-driven decision-making due to silos, proprietary protocols, and missing APIs
- 62% of companies still rely on outdated business-critical applications that do not meet regulatory requirements such as NIS2 or the Cyber Resilience Act
Delaying IT modernization is therefore not a viable option. Legacy systems eventually reach a point where they can neither scale nor be maintained efficiently. At the same time, cost pressure continues to increase.
The Path to AI-ready: More Than a Technology Upgrade
IT modernization is often misunderstood as a purely technical exercise. In reality, it is a strategic transformation that enables new business models.
This includes modernizing applications, for example by moving from monolithic architectures to microservices, building cloud-native infrastructures, and establishing a centralized data platform that serves as a foundation for analytics and machine learning.
A modern IT and data landscape provides the foundation for bringing together data from production systems, ERP platforms, and other sources and making it usable for AI applications. It enables organizations to deploy new applications faster, make data-driven decisions, and scale additional AI use cases more efficiently. True AI readiness is therefore not achieved by purchasing individual AI tools, but by building a robust data architecture and pursuing a consistent IT modernization strategy.
In this blog post, our IoT specialist Device Insight explores the opportunities of IT modernization and the practical benefits it brings to businesses:
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