AI End User Optimization

Client Project Briefing

AMS-AI-End User

Learn how we helped a leading global asset manager unlock the potential of AI end user optimization for enterprise transformation success. Meeting the requirements to establish a baseline set of prompting standards, practical use‑case scenarios, and underlying best practices, we created a framework that empowered teams to better understand, utilize, and enable their internal AI agent. This foundation not only improved confidence in adoption but also ensured that AI was applied responsibly, consistently, and with measurable impact. By embedding standards and scenarios into everyday workflows, the organization gained clarity in decision‑making, efficiency in data interpretation, and resilience in adapting to evolving demands. The result was a pathway to greater accuracy, productivity gains, and sustainable performance across the enterprise. This case highlights how disciplined alignment of AI practices with organizational needs can accelerate transformation and strengthen human capabilities in a rapidly changing environment.

Client

A leading global asset management firm with a diverse portfolio and significant influence across financial markets. Known for scale, innovation, and disciplined investment strategies, the organization manages complex assets worldwide while driving sustainable growth. Its global reach and operational rigor make it a benchmark for industry leadership and transformation.

“Working with AMS has been a pleasure. The results of this program helped our team better understand and utilize their AI agent, resulting in significant adoption of the tool.”

Talent Development Manager, Leading Global Asset Manager

Challenge

A leading global asset manager faced mounting pressure as AI adoption accelerated across the industry. While the promise of intelligent automation was clear, the organization struggled with fragmented approaches at the end‑user level. Teams lacked consistent standards for prompting, had limited visibility into practical use‑case scenarios, and operated without a shared set of best practices. This created uncertainty in how AI should be applied responsibly, raised concerns about accuracy and compliance, and left employees unsure of how to integrate AI into daily workflows.

The absence of a unified framework hindered confidence, slowed adoption, and exposed capability gaps that widened as technology advanced. As expectations grew for faster decisions, sharper data interpretation, and seamless collaboration, the organization recognized that without clear guidance, its workforce risked falling behind. The challenge was not simply technical—it was cultural, operational, and strategic, demanding a disciplined approach to alignment and readiness.

Solution

Leveraging AMS’s Client‑Centric Engagement Models, we began by gaining a deeper understanding of the organization’s unique challenges and end‑user needs. This discovery process enabled us to craft a customized solution built on iterative design cycles, ensuring that feedback and refinement were embedded at every stage. Central to the approach was the development of a best‑practice training program, designed to be deployed globally and at scale.

This program equipped teams with consistent methods of AI application, standardized processes, and shared prompt libraries, creating alignment across diverse geographies and functions. By combining disciplined design with scalable training, the solution ensured that all end users could confidently adopt AI in a uniform, responsible, and productive way, driving accuracy, efficiency, and sustainable performance across the enterprise.

Benefits

The program delivered measurable benefits by reducing fear and uncertainty around AI usage, while ensuring internal compliance and governance standards were consistently upheld. Adoption and usage of the AI agent increased in a productive, disciplined manner, enabling teams to integrate it confidently into daily workflows. As comfort grew, the client began to collaborate and ideate on ways to further optimize the agent, establishing an improvement team dedicated to ongoing iterations and best‑in‑class use cases.

This success not only strengthened accuracy and productivity but also fostered a culture of safe, ethical AI application. By embedding shared practices and prompt libraries, the program helped teams feel at ease with AI’s presence in their work, transforming apprehension into confidence and driving sustainable organizational performance.