Assessing AI Technologies and Tools for Asset Management

As AI becomes ubiquitous across the asset management space, firms are increasingly faced with choosing technology and tools that will maximize their AI capabilities. The right choice can improve efficiency, accuracy, and decision-making, while a misstep could mean major operational and reputational risk. 

So, how can technology and operations teams assess their readiness for AI across their investment operations?

Lior Graf, FundGuard’s resident AI expert has outlined five key questions that asset managers, asset servicers and other providers might consider to guide their selection process.

1. Do You Have the Data Infrastructure in Place to Support AI?

AI models are only as powerful as the data they’re trained on. Firms must ensure that all data is accessible, pre-processed, clean, and loaded into well-organized repositories. Having data spread across legacy systems can limit its usefulness, especially if a firm’s cloud capabilities are minimal or underpinned by decades-old systems. Without a fully cloud-native solution, integrating modern AI tools and leveraging machine learning (ML) or large language models (LLM) will be significantly more challenging.

Put simply: Assess if your firm’s data is easily accessible and ready for processing. If not, consider data management and cloud-native solutions to make AI integration smoother.

2. Does Your Current Tech Infrastructure Support AI Now and in the Future?

Firms should evaluate if their infrastructure can accommodate AI models both now and as they evolve. A modern, cloud-native, best-of-breed tech stack is ideal for supporting AI at scale. This setup makes it easier to use data for ML or LLM by allowing seamless access to cloud services and enabling pre-processing, transformation, and loading of data for model training.

Ask Yourself: Is your tech stack equipped to scale with AI advancements, or is a more robust, future-proofed solution needed?3. Is Data Quality and Reliability Guaranteed?

High-quality, reliable data is the foundation of accurate AI modeling. Ensuring data quality can be far more efficient with cloud-native technologies, which can streamline data ingestion, validation, and processing. On-premise or “cloud-enabled” solutions, which are not truly cloud-native, can present more challenges due to limited automation and real-time access to updated data.

Consider: Are your data management processes designed for consistency and efficiency? Is your firm’s tech stack helping or hindering these efforts?

4. What Are the Typical Integration Points with Your Existing Systems?

When implementing AI, firms need to consider integration points with their existing platforms. Whether it’s databases, operational software, or external systems, data must flow smoothly for transformations and further analysis. Having a modern infrastructure allows firms to learn from data in real-time, feeding updates directly into AI models to improve and refine them.

Bottom Line: If data is siloed or handled manually, this could slow down AI-driven decision-making and limit a firm’s ability to adapt to real-time data changes.

5. How Will AI Models Be Managed and Maintained Once Deployed?

Managing and maintaining AI models can vary depending on the type of AI—traditional ML models versus large language models (LLMs). Today, firms have access to various ecosystems and platforms to support different use cases. For traditional ML, platforms like Databricks offer compatibility across cloud providers like Azure and Google Cloud. For LLMs, services such as Azure OpenAI or AWS Bedrock allow firms to leverage public cloud offerings effectively.

Review: What are your maintenance needs, and do you have a plan to support your models once they’re deployed?

Final Thoughts

Adopting AI isn’t just about selecting the best tools; it’s about ensuring that the foundational elements—data infrastructure, quality, integration, and maintenance—are in place. By carefully assessing these aspects, firms can set themselves up for long-term success with AI.

Ready to Unlock the Full Potential of AI for Your Firm?

FundGuard can help your firm create the modern infrastructure needed to harness the power of AI. We believe that the path to technology transformation must start in the back office, and that investment accounting as a real-time data source should be the foundation of all the work that goes on across your investment operations.

Contact us today to discover how FundGuard’s cloud-native, AI-powered investment accounting platform can transform your operations and future-proof your business. Are you ready to join the transformation?