The financial services industry has long been driven by structured, rule-based IT systems,  but the relatively newer AI driven paradigm doesn’t readily fit into traditional IT management frameworks. This is because unlike legacy software systems which follow  predictable workflows, AI models evolve, learn, and require continuous adaptation. 

Managing AI initiatives like traditional IT projects can lead to frustration, inefficiencies, and  even failure. It’s time for financial institutions to adopt a new deployment mindset – one  that embraces AI’s unique development lifecycle, the inherent uncertainty, and its dynamic  nature. 

Why Traditional IT Methodologies Fall Short for AI 

• Fixed Requirements vs. Evolving Models 

Traditional IT projects rely on clearly defined requirements upfront, where well  outlined business needs dictate the system’s behavior. AI, however, is data-driven and  probabilistic. This means that as data evolves and changes, so do the underlying  algorithms, their pathways, and ergo – the outcomes. 

�� Example: A fraud detection system built with AI doesn’t just follow rule-based  patterns; it learns from new fraud trends, and requires continuous refinement. 

• Release Once vs. Continuous Learning

In traditional IT, once software is tested and deployed, it remains relatively stable with  occasional patches. AI driven models, however, degrade over time as financial behaviors  change, regulatory requirements evolve, and adversaries adapt. 

�� Example: An AI-powered AML monitoring system may start producing more false  positives or missing new money laundering techniques unless it is regularly “re” trained  with freshly data, thereby capturing within the AI engine the newer trends that it  represents. 

• Waterfall/ Agile versus AI-Specific Methodologies 

While Agile brought flexibility to IT projects, AI demands an even more iterative,  “experimental” approach. 

– AI projects require “data readiness checks” before development begins. – Model performance must be continuously validated against real-world outcomes. – AI projects can’t have static success criteria. Instead, they require ongoing monitoring  and explainability audits. 

A New AI-First Deployment Mindset: How to Adapt 

• Adopt MLOps and AI Governance – Integrate AI models into a continuous training  and monitoring pipeline to ensure models remain updated and effective. • Shift from Fixed Requirements to Outcome-Based AI KPIs – Measure AI success not  just by deployment milestones but by real-world performance (e.g., fraud detection  accuracy, reduction in false positives, improved credit risk assessments). The  business mindset needs to move from a “once and done” to “continuous  improvement”. 

• Ingrain Cross-Functional Collaboration – AI success requires data scientists,  compliance officers, IT teams, and business leaders working together from day one. • Prioritize Ethical AI and Explainability – In financial services, AI must be  transparent, fair, and auditable to meet regulatory scrutiny. Given that it is non negotiable, this needs to be one of the critical factors for evaluating any AI oriented  solutions. 

The Bottom Line: AI Projects Are a Journey, Not a One-Time Deployment Financial institutions that treat AI projects like traditional IT deployments risk stagnation,  underperformance, and regulatory non-compliance. To expertly navigate this challenge and  unlock AI’s full potential, enterprises need to embrace an adaptive, iterative approach that  aligns with AI’s intrinsic and fundamental nature: the ability to learn, evolve, and improve over time!