Outcreating Investment Research with an AI-Powered Fund Research Co-Pilot

Our Client

The client is a global leader in investment consulting and wealth management, serving institutional investors, asset managers, and wealth management organizations across more than 130 countries. The firm delivers research-driven investment insights that help clients make informed portfolio and wealth management decisions — insights that, downstream, shape the retirement outcomes of the pension holders, plan participants, and individual savers those institutions ultimately serve. 

As demand for investment research continued to grow alongside a widening global retirement savings gap, the firm needed a scalable way to increase report production while preserving the rigor, accuracy, and consistency that institutional clients and regulators expect.

Industry Trends

Investment consulting and wealth management firms are under growing pressure to produce more research, faster, for a client base that is itself under pressure: institutional investors and individual savers alike are contending with a retirement savings shortfall that is expected to grow eightfold by 2050. Leading research organizations are responding by: 

• Adopting data-to-text automation to convert structured investment data directly into analyst-ready research narratives. 
• Applying domain-specific generative AI to synthesize insights across asset classes, funds, and markets at greater scale. 
• Preserving human-in-the-loop review to protect research quality, regulatory compliance, and client trust as automation expands. 
• Standardizing research production into repeatable, scalable operating models rather than bespoke, analyst-by-analyst workflows. 

Challenges

As demand for investment research continued to grow, the organization needed a scalable way to increase report production while maintaining the rigor, accuracy, and consistency expected by institutional clients. Key challenges included:

• Growing research demand required higher output without compromising quality.
• Limited analyst bandwidth and increasing pressure on research teams.
• Long report creation cycles impacted responsiveness and productivity.
• Need for consistent analysis across multiple asset classes and markets.
• Strict compliance requirements and internal governance standards for research publication.
• Increasing operational costs associated with manual report generation.

The organization required an AI-powered solution that could automate repetitive research tasks while preserving the quality, control, and domain expertise essential to financial analysis — expertise that institutional investors and wealth managers depend on to make sound decisions on behalf of savers working to close their own retirement gap.

LTM's Solution

LTM implemented an AI-powered Fund Research Co-Pilot designed to automate research generation while maintaining analyst oversight and regulatory compliance.

Intelligent Research Automation : The solution leveraged data-to-text automation to transform structured investment data into analyst-ready research reports. Capabilities included:

• Automated report drafting
• Investment insight synthesis
• Fund performance analysis
• Standardized research output generation

Personalized Insight Generation : AI models synthesized data from multiple sources and generated contextualized research narratives tailored to client needs. The solution combined:

• Domain-specific prompting frameworks
• Financial research intelligence
• Personalized insight generation
• Consistent narrative construction

Human-in-the-Loop Governance : To ensure quality and compliance, analysts reviewed and refined AI-generated drafts before publication. This approach enabled:

• Regulatory alignment
• Research quality control
• Expert validation
• Reduced operational risk

Scalable Research Architecture

• The platform was designed to scale across asset classes, investment products, and geographies with minimal reconfiguration. 

• Cross-functional collaboration between data scientists, financial analysts, and solution architects ensured the solution remained technically robust, business relevant, and compliant with industry standards.

Business Benefits

The Fund Research Co-Pilot transformation delivered measurable business value while modernizing the investment research operating model:

• 40% improvement in research efficiency, enabling higher report throughput and increased profitability.
• ~75% reduction in research generation costs through AI-driven automation of report creation workflows.
• 58% faster report creation, reducing turnaround time from 12 hours to 7.2 hours.
• Established a streamlined 1→1→1 operating model, transforming a single data source into an automated workflow and standardized output.
• Increased analyst productivity by eliminating repetitive research and reporting tasks.
• Enabled research teams to focus on higher-value investment analysis, client advisory services, and strategic insights.
• Scaled research production across asset classes and markets without compromising quality or compliance standards.
• Improved consistency, governance, and quality assurance through AI-assisted workflows combined with human-in-the-loop validation.

Conclusion

By combining AI-powered research automation with human expertise, LTM helped the client transform its research production lifecycle into a scalable, efficient, and compliant operating model. The Fund Research Co-Pilot enabled faster insight generation, reduced operational costs, and significantly increased research throughput while maintaining the rigor and quality expected by institutional investors. 

Ready to outcreate investment research for your enterprise?