CASE STUDY

Streamlining AI Initiatives For Impact-driven Results

Customer Profile
Location:
East America
Industry:
FinTech
Vertical:
Operations
Technologies Used
MLOps
Cloud Computing and Storage
Data Governance & Management
Architecture Modernization 
Business Impact
52%
Reduction in Operational Costs
30%
Costs Saved on Duplication of AI Efforts
8/10
PoCs Practitionalized With Proven Results

Problem Statement

A fintech organization failed to mark its AI success despite having investments in multiple AI initiatives.The main issues were duplication of AI efforts, limited reusability, no streamlined roadmap for prioritizing AI initiatives, and the lack of an AI foundation. As a result, the fintech company noticed that they had exhausted unsystematic AI investments ignoring the genuine initiatives that would have driven good value. The organization failed to productionalize most of the PoCs due to a weak strategy and inefficient infrastructure.
Challenges
  • Ill-preparedness for AI deployments
  • Data trapped in silos and difficult to integrate with external sources
  • Lack of profound infrastructure
  • No unified AI roadmap defined

Driving Impact with Unified and Streamlined AI Initiatives 

Solution
Upon analyzing our client’s AI proposition, we suggested streamlining their AI efforts through a MLOps-driven AI roadmap. For optimizing AI initiatives of the Fintech client, we worked on the five major areas of concern - 
  • MLOps
  • Cloud computing and storage
  • AI-driven decision making
  • Data Governance and Data Management
  • Architecture Modernization
We helped the client to identify areas of short-term and long-term AI investments with a focus on key strategic initiatives and detailed competitor analysis. We emphasized prioritizing their AI initiatives based on the timeline required, evidence of ROI, ease of deployment and level of AI adoption.
We augmented the client’s in-house team with a proven AI roadmap and helped them align their AI efforts and resources in the right direction. They are now equipped to explore more use cases for their digital transformation efforts. 
Results
  • Organization-wide streamlined efforts and AI strategy
  • Profound AI foundation helping in the execution of several AI projects
  • Costs saved by avoiding duplication of work
  • Modernization of legacy architectures and infrastructure 
  • Solid competitive advantage

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