Telecom company

Business problem and objectives  

Our client, a large telecom company, needed to implement a Responsible AI framework for two specific use cases: Authorization to Operate (ATO) and Fraud Detection. 

The primary objective was to develop a comprehensive framework that outlined Responsible AI best practices and standards from ideation to decommissioning. Additionally, the client wanted to implement a Governance Framework that assessed scalability and integrated platform and custom changes for both legacy and new solutions. 

Key challenges

The client faced several challenges in implementing Responsible AI, including ensuring adherence to correct processes, aligning diverse teams and stakeholders, and establishing governance-compliant procedures. These tasks required attention to detail, thorough documentation, and leveraging platform capabilities for ethical deployment and management in production environments.  

The complex nature of these challenges emphasized the need for a strategic approach to address the inherent complexities of Responsible AI implementation. 


To tackle the challenges, we conducted a thorough analysis of the existing platform and identified areas related to governance, metadata management, model registry, and logging. Based on this assessment, we designed a recommended architecture that was not tied to any specific platform, thus reducing the risk of future challenges associated with platform changes or upgrades.  

We developed Standard Operating Procedures (SOPs) to cover various aspects, including monitoring, scalability, ML development, and reusability. Additionally, we operationalized and automated the governance framework in collaboration with our client’s operations team, ensuring seamless integration with existing processes and workflows. 

Impact created

Introducing a Responsible AI framework, checklists/scorecards, and SOPs allowed the client delivery team to swiftly enhance their Responsible AI practices.  

Streamlining the onboarding process for new projects resulted in a 45% reduction in assessment effort by establishing entry and exit criteria.   

Additionally, setting up a Responsible AI Center of Excellence (CoE) led to a 40% boost in use case efficiency by adopting industrialization techniques at scale. 

Use automation to scale up services and manage growing demand

The Big Picture

A consulting company, providing services to card issuers, acquirers, and retailers across the globe, wanted to systematically scale up its services to manage fast growing demand. Its approach of delivering bespoke consulting engagements on an individual customer basis involved heavy manual data processing, limiting its reach to service and engage all players in the market.

Transformative Solution

To solve this problem, it was necessary to build a scalable solution out of its standard services by bringing data, consulting, domain expertise, and visualization all together. This would allow for the creation of a visually-engaging delivery platform for consultants across the globe with predetermined recommendations ready to offer their clients.

A solution suite of scaled data products was developed to service end-clients based on the consulting company’s in-house data and technologies. It was designed with scale and a wide range of global clients in mind, and leveraged process automation to reduce the dependencies on manual effort for refreshes.

Each solution in the suite was developed in a phased manner, refining the product features and outputs at each stage. Phases included design and development, go-to-market, enhancement with additional features, and operationalization. The solution enabled the client to scale up and engage with multiple markets and end-clients in a short time-frame, through data products based on an in-house data repository.

The Change

As a result, the company saw multiple benefits:

  • Greater data-driven, standardized insights and recommendations to end-clients across different engagements.
  • Powerful visualizations, helping clients understand and dig deeper into their own data to uncover new trends and insights.
  • Faster go-to-market times (60%-80% time savings across different solutions).
  • 100% adoption by the consultants to deliver all solutions to more clients, therefore increasing their coverage within the market.
  • Scalable approach to quickly expand the company’s product portfolio.
  • Design thinking and visual storytelling were leveraged to create engagement for consultants during delivery.
  • Automation of manual tasks wherever possible.