Across banking and financial services, artificial intelligence has moved beyond experimentation into production workflows — underwriting, fraud detection, risk monitoring, customer engagement, and operational decisioning. Yet most institutions now face a structural dilemma: whether AI should operate as a centralized enterprise platform to ensure trust and control, or as distributed intelligence embedded within business functions to enable contextual decision-making and speed.

The challenge is not only model accuracy — it is also critically, the organisational design. Scaling AI in BFSI requires clarity on ownership, governance, data accountability, and decision rights across the enterprise. Without this alignment, institutions either slow innovation through over centralisation or create unmanaged risk through fragmentation.

Publicis Sapient, with deep experience in digital transformation across financial services, partners with organisations to architect AI as an enterprise capability — balancing platform standardisation with business autonomy. By combining strategy, engineering, and regulatory-aware design, Sapient helps banks move from isolated AI deployments to trusted, scalable decision systems.

Why This Matters

	From Models to Decisions: AI adoption succeeds only when institutions determine where machines are allowed to act — not just predict.
	Speed vs Accountability: Financial institutions must innovate at digital-native pace while maintaining auditability, explainability, and regulatory oversight.
	Operating Model Transformation: AI is reshaping organisational topology — redefining roles of central data teams, business units, and risk functions.