AI Doesn’t Fail in the Models. It Fails in the Architecture.
Over the past few months, the debate around Agentic AI has entered a more mature phase. The conversation is no longer limited to tools capable of generating content or supporting human decisions, but has shifted toward systems able to reason, plan, and execute autonomous actions within the core processes of financial institutions.
This represents a profound paradigm shift, one that is redefining how banks, asset managers, and FinTech players approach artificial intelligence. Yet despite intense media attention and growing investments, many AI initiatives still stall at the experimental stage, never achieving real, scalable adoption.
The reason is becoming increasingly clear: it is not the quality of the models.
The Real Limitation of AI Is Architecture
This point was recently highlighted by Alessandro De Leonardis, CIO of Armundia Group, in an in-depth analysis focused on the evolution of AI in the FinTech sector. His perspective starts from a simple but often overlooked observation: the primary barrier to effective AI adoption is not technological capability, but architectural readiness.
Many institutions are attempting to deploy Agentic AI on monolithic systems designed for a different era. Rigid architectures, long release cycles, complex integrations, and inflexible governance models make it extremely difficult to orchestrate specialised agents, continuously retrain models, and manage different levels of operational autonomy.
The outcome is evident: promising initiatives remain confined to innovation labs, unable to deliver tangible business impact.
The Numbers Confirm the Challenge
The competitive context makes this limitation even more evident. According to the McKinsey Global Banking Annual Review 2025, the banking sector risks up to USD 170 billion in profit erosion over the next decade if it fails to translate AI into real operational value. By contrast, early adopters of Agentic AI could achieve significant profitability gains in the medium term.
The differentiator, however, will not be who experiments first, but who is structurally prepared to bring AI into production.
Deloitte already reports cost reductions ranging from 30% to 50% in targeted workflows, while MIT Technology Review highlights the growing adoption of agentic systems in live production environments. These benefits, however, remain accessible only to organisations built on solid technological foundations.
Why Monolithic Systems Don’t Work
Legacy systems still absorb a substantial share of IT budgets and were designed for stability and control, not for adaptability and continuous experimentation. Agentic AI, by contrast, requires exactly the opposite: modularity, interoperability, rapid update cycles, and graduated governance.
An autonomous agent managing a compliance process, for example, requires very different controls, audit trails, and supervision levels compared to an agent handling customer interactions. Monolithic architectures offer an “all-or-nothing” control model, which is incompatible with this complexity.
This is where many AI strategies stall.
The Strategic Role of Modular Architectures
As De Leonardis underlines, the solution lies in a fundamental architectural shift. Modular, service-based architectures enable AI to be integrated selectively and progressively, activating it on well-defined processes—from compliance and credit management to reporting and customer interaction—measuring impact and scaling only what delivers value.
In un modello modulare:
- ogni servizio ha responsabilità chiare
- gli agenti AI operano in contesti controllati
- i rischi restano confinati
- l’evoluzione è incrementale, non traumatica
Essere “AI-ready”, quindi, non significa aggiungere un livello di intelligenza sopra piattaforme esistenti. Significa progettare sistemi nativamente pensati per accogliere, governare e far evolvere l’AI nel tempo.
Precision Over Scale
In the era of Agentic AI, success belongs to those who adopt a precision-driven approach. Not sweeping, top-down technological revolutions, but targeted interventions on high-impact processes, supported by architectures that enable experimentation, learning, and controlled scaling.
The real question today is no longer whether to adopt artificial intelligence.
It is whether the architecture underlying our information systems is truly ready to support it.
For many organisations built on monolithic foundations, the answer demands a serious reassessment. For those able to address it with clarity and foresight, Agentic AI represents not just an emerging technology, but a structural and sustainable competitive advantage.
Contact us: info@armundiafactory.com