Ronja SchmiedchenRonjaSchmiedchenRedaktion, Social Media & Moderation
3 min
Agentic AI represents more than a technological upgrade. It marks a turning point where incremental optimisation gives way to architectural redesign.Adobe Stock / Sandwish
Agentic AI is widely regarded as the next evolutionary step in artificial intelligence. Yet according to a recent Cognizant study, most IT landscapes are not prepared. Technical debt, legacy systems and rigid budget structures are increasingly becoming strategic liabilities.
The German federal government has recently initiated
legislation to implement the EU AI Act. With the Federal Network Agency
designated as the central supervisory authority, it is clear that artificial
intelligence in Europe is no longer viewed solely as an innovation driver but
as a regulated technology domain governed by clear rules.
Companies will be required to demonstrate how their AI
systems operate, how risks are assessed and who holds internal responsibility.
The AI Act follows a risk-based approach, focusing particularly on high-risk
applications. For the automotive industry, this is far from marginal. Advanced driver assistance systems, autonomous functions,
AI-supported production control and even data-driven decision processes in
sales may fall into regulated categories.
AI therefore becomes not only a matter of performance and
scalability, but of governance, documentation and technical resilience.
In this context, a recent Cognizant study entitled “AI in
a Two-Year Window: The Path to Legacy Modernisation” gains strategic
relevance. Based on an international survey of business and IT executives, the
study argues that organisations seeking to scale AI within the next two years
must fundamentally modernise their existing IT landscapes.
The urgency becomes particularly evident when examining
agentic AI. These systems do not merely analyse data or generate
recommendations. They plan tasks, coordinate multiple systems and execute
end-to-end workflows within defined boundaries.
Agentic AI operates across system layers and assumes
operational responsibility. In doing so, it directly exposes weaknesses in
fragmented architectures, inconsistent data models and outdated governance
structures.
A widening gap between ambition and readiness
According to the Cognizant study, 85 per cent of executives
fear that their current technology base could hinder AI integration and
scaling. At the same time, at least three quarters believe they can achieve
their modernisation targets within two years.
This optimism contrasts sharply with structural reality. Only
17 per cent of respondents believe their existing infrastructure is capable of
supporting agentic AI systems that coordinate multiple platforms and execute
complex workflows autonomously. In other words, more than four out of five
organisations see themselves as insufficiently prepared for the next stage of
AI evolution.
Technical debt remains a central obstacle. Ninety-three per
cent of organisations report having reduced no more than a quarter of their
accumulated technical debt. Looking ahead to 2030, 79 per cent expect to have
eliminated less than half.
For agentic AI, this is critical. Such systems depend on
clearly defined interfaces, consistent data governance and stable security
architectures. Without standardisation and clear accountability, the risks of
flawed decisions, security gaps and non-transparent process chains increase
substantially.
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Key facts: Agentic AI and legacy risk
What? Agentic AI refers to AI systems that autonomously plan tasks, coordinate multiple systems and execute end-to-end workflows.
Why? Scaling agentic AI requires modern, standardised IT architectures with transparent governance and traceable data flows.
Who? According to a Cognizant study, 85% of business and IT leaders fear their current infrastructure may hinder AI integration.
What’s the barrier? Technical debt remains high, with 93% of organisations having reduced no more than a quarter of it.
What’s at stake? Under the EU AI Act, explainability, compliance and system robustness become mandatory, increasing pressure on legacy IT environments.
The legacy budget trap
The study also highlights a structural imbalance in IT
spending. Currently, around 61 per cent of IT budgets are allocated to
operating and maintaining legacy systems. By 2030, this figure is expected to
drop to 27 per cent, with the released resources redirected towards innovation,
automation and AI.
However, this financial reallocation will only materialise
if modernisation efforts accelerate significantly. Otherwise, legacy
maintenance will continue to absorb capital that is urgently needed for AI
transformation.
Implications for the automotive industry
The automotive sector faces particular complexity. Software-defined vehicles, over-the-air updates,
AI-driven production planning and data-based aftersales services already depend
on deeply integrated digital infrastructures.
Agentic AI amplifies these dependencies. It interacts with
ERP systems, MES environments, PLM platforms and fleet data streams. It
orchestrates cross-functional workflows and operates within safety-critical and
regulated contexts.
Cognizant sets out three phases to ensure modernisation becomes self-financing and to enable organisations to become AI-ready.Cognizant
Under the EU AI Act, explainability, traceability and robust
governance become mandatory, especially where AI systems influence vehicle
safety or regulated production processes. Logging, monitoring and role-based
accountability are no longer optional features but prerequisites for scalable
deployment.
In this sense, agentic AI functions as an architectural
stress test. It reveals whether organisations have truly modernised their IT
foundations or merely layered innovation onto legacy structures.
Modernisation pace versus AI momentum
Cognizant’s conclusion is unequivocal: organisations must
align the pace of IT modernisation with the speed of AI development. If
companies succeed in approaching the two-year transformation window, they will
be better positioned in an increasingly AI-driven economy.
If not, technical debt may evolve from an operational
inconvenience into a structural competitive disadvantage. Agentic AI therefore
represents more than a technological upgrade. It marks a turning point where
incremental optimisation gives way to architectural redesign.
