As the banking, financial services and insurance sectors move beyond exploratory AI pilots toward scaled, governance-first deployment, the pressure on institutions to modernize responsibly has never been greater. To better understand what this shift demands from technology leaders, Digital Journal caught up with Pablo Cella, Division President at Amdocs.
Cella discusses how Amdocs is helping financial institutions navigate the evolution from legacy infrastructure to AI-driven operations, what “governed intelligence” means in practice and why the institutions treating governance as an architectural foundation, not an afterthought, will be positioned to lead.
Digital Journal: Can you provide an overview of Amdocs?
Pablo Cella: Amdocs is a leading software and services provider best known for helping telecommunications companies modernize, innovate and scale digital operations for more than 40 years. Building on over 15 years of experience supporting banks through strategic mergers, acquisitions and partnerships, Amdocs formally launched its financial services practice in 2022 to help financial institutions accelerate their digital transformation initiatives.
DJ: What is the current state of Banking, Financial Services and Insurance modernization?
Cella: We’re in an essential phase of Banking, Financial Services and Insurance (BSFI) modernization. The industry is moving beyond exploratory pilots in cloud, AI, automation and modernization toward scaled deployment that is measurable, compliant and operationally resilient.
We’re seeing growing demand for AI sovereignty, stronger model governance and controlled agentic AI deployment. Institutions that modernize responsibly will be best positioned to innovate with confidence while meeting rising regulatory expectations.
In practice, that means scaling AI and automation with confidence, strengthening compliance through continuous evidence and embedding governance as a first‑class design principle. BSFI institutions are functioning under a new operational standard where governance is not an afterthought but an architectural foundation that will enable more meaningful deployment.
DJ: Can you share what “governed intelligence” means, and why is it central to BSFI right now?
Cella: Governed intelligence is the ability to embed AI and automation into enterprise workflows while maintaining oversight, explainability and traceability required in highly regulated industries – like banking.
The rise of governed intelligence is causing several major shifts. Cloud and AI strategies are becoming sovereignty‑aligned and modular, while quality engineering is shifting to risk‑driven, explainable automation. At the same time, data modernization is accelerating to support real-time risk and personalization, experience design is becoming intent-led and AI-assisted without abandoning structure, and mainframe modernization is entering an era of hybrid pragmatism powered by explainable AI.
We’re also seeing a shift from traditional quality engineering toward what we call “Trust & Confidence Engineering.” As AI becomes embedded in critical business processes, organizations must validate more than application performance. They need confidence that decisions are explainable, data is trustworthy, controls are enforced and autonomous systems remain aligned with business and regulatory requirements. In highly regulated industries, trust is increasingly becoming an engineered capability.
Ultimately, governed intelligence is what allows organizations to scale innovations responsibly while maintaining trust and regulatory confidence.
DJ: Why is real-time data modernization considered a prerequisite rather than a goal in itself?
Cella: Real-time data modernization is no longer a long-term aspiration – it’s a foundational requirement for AI-driven banking.
Many institutions still operate on legacy batch architectures that remain a primary constraint because they limit decision speed, visibility and responsiveness. As a result, we’re seeing them shift decisively toward event-driven pipelines, streaming architectures and domain-driven data models that improve ownership and decision velocity.
Increasingly, AI performance is limited by data lineage, latency and quality rather than model sophistication. Institutions that modernize their data foundations first will be positioned to capture greater value from AI investments.
DJ: What role does cloud sovereignty play in BFSI’s strategy?
Cella: Cloud remains a foundational infrastructure layer to BFSI, but its strategic role is evolving beyond scalability and cost efficiency. It is increasingly the environment where institutions enforce data locality, model governance and AI controls to meet jurisdictional expectations.
Today, sovereignty extends across the full AI lifecycle – including where models are trained, how they operate and how agentic systems are supervised.
Cloud strategies increasingly embed requirements such as model lineage, regional training constraints, standardized guardrails and controlled orchestration of agentic workflows, making the cloud a central layer of governed AI operations.
Financial institutions are prioritizing cloud operating models that are more pragmatic and sovereignty-aligned to keep sensitive data, models and operational workflows within jurisdictional boundaries while applying consistent, regulator-aligned controls across hybrid and multi-cloud environments.