Accelerating AI Time-to-Production at a Tier 1 Bank
67%
Reduction in time-to-production
7
Use cases in production in 12 months
23
Approved use cases unblocked
The Challenge
A Tier 1 financial services firm had 23 approved AI use cases and no production deployments after 18 months. The use cases themselves were well-designed. The models had been developed and validated. Business cases had been approved. Funding had been allocated.
The bottleneck was governance and infrastructure. Each use case required a separate risk review process. Each deployment required a custom infrastructure build. Each model required its own monitoring framework. The result was a pipeline of approved initiatives with no path to production.
The firm had the AI capability. It didn’t have the deployment capability.
Our Approach
Eswara Advisory Group designed and built a unified AI deployment platform with standardized governance checkpoints. Rather than treating each use case as a separate implementation project, we designed a platform that would serve as the deployment path for all 23 use cases.
The platform included: a standardized governance framework with defined checkpoints that risk and compliance teams could audit; a model registry and deployment infrastructure that reduced the infrastructure work for each new use case to near zero; a monitoring framework that provided the observability data the risk function required; and an operating model that defined the roles and responsibilities for managing AI in production.
We designed and built this platform in parallel with moving the first three use cases through it. By the time the platform was stable, three use cases were in production and the remaining 20 had a clear, audited path.
The Outcome
Seven use cases were in production within 12 months. The remaining 16 were on the deployment roadmap with defined timelines. Time-to-production for new use cases dropped by 67%.
The governance platform became the standard for all AI deployments at the firm. Risk and compliance teams reported higher confidence in AI deployments. Business units reported faster time from approved use case to production value.
“The bottleneck was never the models. It was governance and infrastructure.”