Challenge 

The investor relations team of an alternative asset manager with more than $1 trillion in AUM was exploring the feasibility of using generative artificial intelligence (GenAI) to transform workflows associated with processing investor requests. The team recognized the need to reduce processing time, the manual effort involved, and the rate of human error. 

Navigating this technological leap forward in a strategic, value-maximizing way brought additional complexity for which the team required expert support. 

How We Helped 

CrossCountry Consulting managed the transformation, serving as the conduit between investor relations and technology teams during the design, development, and deployment of the GenAI model Claude by Anthropic. This work included: 

  • Determining how current processes operate and understanding the needs and pain points of existing workflows that could be elevated by AI. 
  • Developing business requirements to incorporate into the AI model and tracking their progress in the build. 
  • Conducting system integration testing (SIT) and user acceptance testing (UAT) of Claude on sample populations. 
  • Working with the technology team to fix any issues post-go-live, as well as developing enhancements. 
  • Providing periodic status updates to leadership on the benefits of Claude. 
  • Supporting the offshoring of the operations team. 

“By integrating AI technology into the investor relations/investor services (IR/IS) workflow, the company achieved significant operational improvements, particularly in external investor communications,” CrossCountry’s project leads noted. “One of the most impactful outcomes was the complete elimination of missed communications due to human error or manual processing. As a result, all investor inquiries are consistently received, promptly addressed, and escalated when necessary – enabling faster prioritization and response to critical situations.”  

Results 

The newly implemented AI model delivered significant benefits in the form of: 

  • 20% average time reduction when logging an investor request. 
  • Increase in staff capacity and productivity to focus on more value-added tasks. 
  • Reduction of knowledge transfer loss and training time. 
  • Elimination of duplicative processing and missed investor requests/emails. 
  • Improvement of AI functionality through testing and providing continuous feedback. 

“The automation of manual tasks and the removal of duplicative work among team members helped free up valuable team capacity, allowing staff to pivot to higher-value activities,” detailed project leads.  

With a successful AI implementation completed, the company enhanced its enterprise AI maturity and has a constructive transformation playbook to reference for future AI deployments in other areas of the business.