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Integrating AI into Investment Processes: Insights from SimCorp's Survey

In a world where technology is constantly evolving, the investment sector is at a crucial juncture, particularly concerning the integration of artificial intelligence (AI). A recent survey by SimCorp, explored in the 2025 InvestOps report, reveals that 75% of buy-side operations leaders recognize the potential benefits of AI. However, there remains a significant gap in understanding how to implement this technology effectively within their investment processes.

Understanding the Benefits of AI

AI holds the promise of revolutionizing investment practices by enhancing investment analysis, decision-making, risk management, data management, and client engagement. Despite the enthusiasm among industry leaders—nearly 75% affirming the advantages—there is a collective sentiment that more guidance is required to leverage AI tools proficiently. As one respondent aptly noted, "An AI tool can uncover risks that might remain unknown to us," highlighting the transformative potential that AI holds in risk assessment and management.

Nonetheless, a cautionary observation arises: 16% of the surveyed leaders felt unprepared to utilize AI, while a meager 9% claimed they were very prepared. This discrepancy underscores the necessity for education and structured frameworks to facilitate the AI integration journey.

Data Infrastructure Challenges

As the SimCorp report delves deeper, it becomes evident that many buy-side organizations grapple with their data infrastructures. Almost half of the respondents reported that their data management systems consist of both in-house and third-party solutions, contributing to significant data challenges. With 67% prioritizing the creation of standardized data models and 65% planning to consolidate systems for a unified data layer, it becomes clear where attention must be focused.

"Data is the 'key' to the front office, yet many firms struggle with fragmented and inconsistent data sources," states Laura Kayrouz, a Senior Partner at Alpha FMC. This raises an essential point: without a robust data governance framework, the integrity and consistency of data remain in jeopardy, necessitating a comprehensive audit to identify gaps and redundancies.

Enhancing Operational Efficiency

The quest for operational efficiency is paramount. Respondents identified improving data management and operations for multi-asset investment strategies as a top initiative. The primary challenge lies in the inability to visualize multi-asset management holistically, with 60% of respondents indicating difficulties in achieving a comprehensive view of their investments.

Management of multi-asset class portfolios mandates a system architecture featuring a unified data layer, offering real-time insights across public and private markets. Such capabilities facilitate instant responsiveness to changes in any sector of the investment lifecycle.

Marc Schröter, Chief Product Officer at SimCorp, emphasizes the urgency: "Investment managers must invest in data strategies to support their decision-making capabilities. Failing to do so in a diversified portfolio landscape could lead to unnecessary complexities and risks operational silos that hinder scalability."

The top three methodologies to measure AI tool effectiveness in the investment process—data cleaning efficiency (46%), enhanced data visualization (42%), and accelerated insights (41%)—reflect a strong appetite for improvement through technology.

The Future of Investment Management

As we look forward to 2025, it's clear that operational efficiency and seamless data integration enable firms to thrive in an increasingly data-driven market. The findings from SimCorp’s InvestOps report illuminate the criticality of technological advancement, particularly in the realm of ESG investing, projected to be the most significant avenue for innovation in North America and APAC.

Ultimately, the integration of AI into investment processes hinges on knowledge-sharing, strategic prioritization in data handling, and adherence to foundational governance frameworks. As the investment landscape continues to evolve, embracing AI not just as a tool but as a transformative partner will dictate the future success of firms in navigating the complexities of modern finance.

By prioritizing a robust data architecture and a focus on continual learning, buy-side organizations can leverage AI to revolutionize their investment processes, ensuring they remain at the forefront of the industry. It's an exciting time for investment management, but it will require a concerted effort to harness the full potential of AI effectively.