Legacy IT Systems Still Outperform Modern Cloud and AI Solutions in Many Firms

Recent research indicates that despite significant efforts to modernize IT infrastructures, legacy systems continue to deliver strong performance in many organizations. A survey conducted among over 1,500 IT decision-makers in Germany reveals that four out of five companies are currently engaged in IT modernization projects, yet the complete transition away from older technologies remains a challenge.

Artificial intelligence has emerged as a core driver for these modernization efforts, with 43% of respondents stating their IT upgrades are aimed at enabling or scaling AI capabilities. Other prominent motivations include reducing operational costs (33%) and improving agility to meet evolving business and market demands (31%). Notably, nearly three-quarters of the surveyed organizations have already adopted AI technologies in some capacity, with the figure rising to 80% among larger enterprises with more than 250 employees.

The main areas where AI is being implemented include IT management (45%), customer service (42%), and data analysis or business intelligence (36%). Companies predominantly measure the success of their modernization initiatives by tracking increases in productivity (40%), enhancements in customer satisfaction (38%), and reductions in error rates (35%). Cost savings, while important, rank slightly lower at 33%. Only a small fraction--4%--of organizations reported no current or planned AI initiatives.

Legacy Systems Remain Operational

While modernization projects are widespread, legacy IT systems continue to pose significant obstacles. Only one in three companies has managed a complete shutdown of its old systems. More than half of the survey participants (52%) reported that cloud-based infrastructures did not yet match the performance of their legacy systems. Additional reasons for maintaining legacy operations include difficulties with data migration (35%), the risk of operational disruptions (33%), and dependencies on critical business processes (29%).

Efforts to expand AI applications are further hindered by structural challenges. Less than half of respondents consider their existing data models to be suitable foundations for AI-driven projects, and 14% find them entirely inadequate. Key barriers to successful AI integration include limited data access (44%), insufficient real-time capabilities (44%), unsuitable tools, data silos, and poor data quality (each cited by 43%).

Another major obstacle is the shortage of specialized expertise. Nearly half of those surveyed indicated that a lack of skilled professionals is impeding both the technical implementation and the strategic planning of AI initiatives.

Company Size Influences Modernization Success

The survey highlights significant differences based on company size. Larger organizations, particularly those with more than 250 employees, are more likely to drive IT modernization and AI adoption, with 58% already executing such projects and 12% having completed them. These firms also invest more heavily in training and knowledge development (54%).

However, larger enterprises also encounter greater integration complexities. About 65% cited above-average challenges in integrating new systems and noted increased risks related to performance and security post-migration. Small and medium-sized enterprises (SMEs) demonstrate comparable interest in modernization but face constraints due to more limited financial and personnel resources, which can slow their progress.

Overall, the findings underscore the persistent role of legacy systems in today's digital transformation landscape. While cloud and AI technologies continue to gain ground, technical, operational, and human resource challenges mean that many organizations must continue to rely on their established systems for the foreseeable future.