Artificial intelligence requires a future-proof foundation. Find out how to objectively assess your modernisation needs before investing in costly AI projects.
Artificial intelligence and automated processes are right at the top of the agenda today. Yet for many IT managers in established organisations, the initial euphoria is quickly followed by disillusionment: AI requires a future-proof foundation.
Those wishing to integrate AI models, process data in real time or utilise scalable AI services often find themselves held back by the rigid limitations of existing systems. The potential is enormous, but the technological infrastructure acts as a stumbling block. Reality shows that without a future-proof foundation, AI remains an expensive experiment rather than a genuine competitive advantage.
Before any measures are taken or budgets allocated for AI projects, one thing is therefore essential: direction. IT modernisation should never be an end in itself, but must always be based on a thorough analysis of the current state of affairs. Only when companies understand their biggest bottlenecks, stable areas and risks can they justify investments, reduce technical debt and lay the foundations for AI and innovation.
But how does one determine the future-proofing and, consequently, the AI readiness of an application landscape? In practice, six key dimensions have proven effective for assessing the maturity of an IT modernisation programme for AI. They indicate just how modern an IT landscape actually is, or to what extent it requires modernisation.
Assessing modernisation needs is not a simple yes-or-no question. Rather, it is a multi-dimensional analysis that goes beyond the code itself and also encompasses the organisation and its processes.
The most obvious dimension is the technology stack, together with its level of automation. This concerns the programming languages, frameworks, operating systems, databases and automated deployment processes (CI/CD) used. Key questions include: How up-to-date, maintainable and future-proof is the infrastructure, and how well is it suited to working with AI?
Outdated technologies and manual processes lead to increased technical debt, as even minor adjustments require a disproportionately large amount of effort. In the worst-case scenario, the systems are no longer supported by the manufacturer, making them risky and updates virtually impossible. A modern technology stack – one that, for example, supports cloud-native principles and enables automated testing – is essential for providing modern interfaces and integrating AI efficiently and without risk.
A lack of data quality, integrity and connectivity is a direct indicator of a lack of AI readiness. The focus here is on the availability of data, the accuracy and integrity of the data, the elimination of data silos and the provision of modern interfaces for working with the data.
With isolated data sets and a lack of interfaces, companies squander valuable potential, as modern technologies are then unable to access the information they require. High data quality and seamless system integration are prerequisites for effectively integrating AI services and fully realising their potential.
This dimension examines the internal structure of the application and its dependencies. Modularity and the degree of coupling between components are of crucial importance here.
A highly monolithic architecture has evolved over time for technical reasons; it is often difficult to modify and results in small changes having significant knock-on effects. This makes modernisation whilst the system is in operation particularly challenging. Only modular and decoupled systems make it possible to integrate AI services in isolation and replace components incrementally without jeopardising the overall system.
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Whilst the first three factors assess the technical foundation, the focus here is on the business perspective and delivery capability: how quickly and flexibly can new business requirements be implemented?
If every change represents a major, high-risk project, the capacity for innovation suffers massively. Cumbersome and risky processes require long test cycles and extensive coordination, even for minor changes. A modern IT landscape is characterised by the ability to implement changes quickly, in isolation and without dependencies, so that new functions can go live within a short timeframe.
Even the best technical architecture will fail if the organisation does not get on board. Leadership approach and collaboration also have a significant impact on IT modernisation. How agile are your development and decision-making processes? How well are business processes understood? To what extent have business processes been digitised, and where are the areas where AI could or should provide support?
A traditional, rigid waterfall model – or even ad hoc decisions – means that changes have to fight their way through the hierarchies for a long time. Highly agile processes, on the other hand, with autonomous, interdisciplinary teams and short iterations, create scope for independent and entrepreneurial action. This is a core value for modern, people-centred organisations.
The final dimension addresses inherent stability and the risk of losing know-how. It raises the following questions: How well are your systems documented? And is the knowledge distributed across the team?
If the majority of knowledge rests with just a few key individuals and the documentation is incomplete, operations will grind to a halt in the event of absences or staff changes. Only when systems are comprehensively and up-to-date documented, and knowledge is widely embedded within the team, can the maintainability, further development and, consequently, the long-term viability of the company be secured. Without reliable documentation, maintenance and operational risks grow exponentially.
Furthermore, inthe age of AI,documentation is taking on a new strategic significance. Well-structured documents serve as a valuable data source for training AI systems. This enables AI systems to provide more precise answers and support teams even more effectively.
IT modernisation for AI is a strategic investment that requires a clear business case. The following six dimensions show that the challenges lie not only in the code, but also at the interface between technology, processes and organisation.
A realistic assessment across these dimensions – from the technology stack to team agility – is the first essential step towards modernising legacy systems to make them AI-ready. It enables you to quantify the modernisation requirements and prioritise the measures that will havethe most positive impact on your time-to-market, your teams’ satisfaction and the desired deployment of AI.
A thorough assessment of the current state provides you with the necessary transparency to convince a sceptical management team and draw up a sustainable roadmap for the transformation.
However, the assessment is only the beginning: as an AI enabler and experienced implementation partner, iteratec supports you beyond mere analysis. We develop the right strategy and carry out the step-by-step modernisation and implementation of forward-looking AI solutions whilst operations continue as normal. This turns your vision into a measurable competitive advantage.
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