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adesso Study: 83 Per Cent See an Urgent Need for Modernisation, Whilst 86 Per Cent Feel a False Sense of Security

Four out of five companies admit that their legacy systems are significantly or noticeably hampering their commercial success. This is according to the latest ‘Application Modernisation’ report from IT service provider adesso. Nevertheless, 86 per cent believe these same core applications are well positioned for the next five years. This discrepancy can hardly be explained by the actual state of the systems. A more likely explanation is that modernisation has long been regarded as too labour-intensive, too expensive and too risky. For the IT service provider, this is precisely the sticking point revealed by the survey of 300 decision-makers in German companies. A system that is up and running is considered fit for the future. However, whether it will also support the next business model is rarely questioned.

Recognised Need, Shied Away from Implementation

The problem is not that companies do not see a need for action. Already, 73 per cent are modernising their IT systems and a further 24 per cent are planning to do so. Rather, companies have long been reluctant to embark on such projects, as there are many examples of projects that have run over budget or failed. Fifty-three per cent of companies cite costs as the biggest obstacle, 35 per cent cite a lack of internal resources or expertise, and 31 per cent cite the risk of business interruptions. At the same time, the pressure on companies to modernise is mounting: each time a specialist who has maintained a system for years retires, the knowledge of the quirks that the system has developed over the years is also lost. Added to this is the need to adapt their own core applications to new technologies and changing business models – a requirement that many legacy systems are no longer structurally capable of meeting. Forty-three per cent of companies explicitly cite the absence or limited integration of new technologies such as AI in their existing systems as a driver for their modernisation projects.

The burden placed on companies by legacy systems is enormous. More than half of those surveyed report that it has a noticeable dampening effect on competitiveness. (Source: adesso SE)

Only one in six respondents to the study were unconvinced or sceptical about AI-supported application modernisation. The majority, however, are certain: this approach has a future. (Source: adesso SE)

Agent-Based Approaches Are Transforming the Logic of Modernisation

It is precisely with these hurdles that agentic AI makes the difference. What used to take analysts weeks to work out is now handled by AI agents, working in tandem with architecture and development teams, in a fraction of the time. Automated analysis of code, architecture and data models makes dependencies and technical debt in legacy systems visible even before the actual project begins. This reduces the workload, shortens the time to the first reliable decision and enables risks to be identified earlier than with traditional analysis methods. Responsibility always remains with people: they evaluate the results, make business-critical decisions and ensure that domain expertise is not lost.

“Agentic AI opens up entirely new possibilities for application modernisation. The whole balance between effort, risk and benefit is shifting,” says Oliver Kowalke, Business Line Lead Cross Industries at adesso SE. “Analyses that used to take months are now delivered by AI agents in just a few days. With our adSCAILE process model, we are seeing in practice that total modernisation costs can be reduced by 30 to 60 per cent compared with traditional approaches. This is a huge step forward that finally makes urgent modernisation projects economically viable.”

Oliver Kowalke is Business Line Lead Cross Industries and an expert in AI-supported application modernisation at adesso SE. (Source: adesso SE)

The full study results are available for download. adesso demonstrates how modernisation projects can be set up in practice using agent-based AI in an accompanying background paper.

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