A Disciplined Route to Scalable AI

A Disciplined Route to Scalable AI: How a Top 10 US Bank Built a Path to Production for Generative AI

A leading US bank transformed its generative AI strategy from endless pilots to a disciplined, scalable framework—ensuring trust, governance, and institutional alignment at every stage.

The Challenge

Generative AI had become a strategic priority, but while experimentation was accelerating, production deployments lagged. There was no shared definition of what it meant for an AI system to be “ready.”

Without a formalized path to production, the Bank risked:

• Stalling innovation in endless pilot cycles.
• Deploying AI without sufficient controls for explainability, auditability, or compliance.
• Misalignment across business, technology, and risk functions.
• Reputational and regulatory exposure from poorly governed AI initiatives.

The Solution

The Bank partnered with Rational Exponent to design a GenAI-specific Path to Production Framework—a structured lifecycle model that defined the stages, criteria, and evidence required to safely and confidently deploy generative AI.

Framework Delivered

• Eight Readiness Dimensions — Business justification, technical maturity, data strategy, explainability, risk controls, and operational sustainability.
• Gated Workflow — Required cross-functional alignment at each decision point.
• Evidence-Based Progression — Defined expectations for what must be demonstrated—not assumed—at each phase.
• Unified Language & Process — Connected business sponsors, technologists, and compliance stakeholders.

The result: a scalable operating model for GenAI deployment that balances innovation with institutional discipline.

Strategic Impact

The framework transformed how the Bank moves AI initiatives forward:

• Innovation is evaluated and scaled with rigor, not ad hoc.
• Compliance and governance are integrated early, not retrofitted later.
• Executives can make structured, evidence-based go/no-go decisions.
• “Production” now means proven, governed, and aligned—not just finished.

Results

The framework introduced not just a checklist, but an internal certification model. AI systems must continually prove their fitness to operate within the Bank’s risk posture—building earned trust across the enterprise.