Andrew Rice
Partner, Portfolio Manager and
Chief Operating Officer
Algorithmic Investment Models
Our questions:
- Are investors expecting too much from AI’s predictive power?
- Is AI saving time—or silently creating risk?
- Is AI eroding client trust or giving firms a new way to build it?
For asset managers, the promise of AI-driven insights, automation, and operational efficiency is undeniable—but so are the potential risks that come with it. To help us better understand AI’s impact on our industry, we spoke with Andrew Rice, an expert on artificial intelligence and machine learning, and their applications for investment management.
Rice, a partner and portfolio manager at Algorithmic Investment Models, has been using the technology for over a decade to design proprietary investment systems. He provided candid insights into where AI can deliver value, where it still falls short, and why trust and oversight will be key to its long-term prospects in the industry.
1. AI can boost efficiency—within limits
One of the most obvious benefits of AI for asset management firms is its ability to automate repetitive tasks, freeing up time for marketing teams, financial professionals, and others to focus on strategy, client relationships, and decision-making.
“AI can save you time. You can write faster emails, automate things, and even use AI-driven scheduling tools to ensure regular client engagement,” Rice explained. “Some advisors program AI to track when they last spoke with a client and automatically schedule follow-ups.”
For asset managers, this can mean more streamlined operations, reduced manual workloads, and enhanced client service. AI-powered tools can draft reports, summarize earnings calls, and generate marketing content—all in a fraction of the time it would take a human.
However, Rice is quick to caution against over-reliance on automation.
“The really impactful AI use cases so far aren’t in replacing human judgment, but in removing friction from administrative work. It’s great for summarizing information or keeping track of client outreach, but it’s not at a place where you can fully trust it to run without oversight,” he suggests.
“AI is great for keeping track of client outreach—but it’s not at a place where you can fully trust it to run without oversight”
Key takeaway:
AI may help reduce certain types of administrative burdens, but it has a long way to go before it can replace the judgement of a real person.
2. AI can also be a powerful research tool (but proceed with caution)
“It’s like self-driving cars. Would you feel comfortable sitting in the back seat with no one at the wheel? AI can be useful, but you still need someone paying attention.”
The rise of AI in investing has sparked debate over whether machine learning models can outperform human investors by identifying trends and optimizing asset allocation. According to Rice, AI’s power lies more in organizing and analyzing data than in predicting market movements.
“It’s really helpful for research,” he said. “Think about how much time investment analysts spend reading through earnings reports and transcripts. Now you can ask AI to do that for you.”
However, when it comes to predicting market trends, AI has significant limitations.
“There’s a misconception about how accurate people expect AI models to be. Unlike a language model that keeps refining itself with more data, investment AI is dealing with a market driven by millions of unpredictable human decisions.”
Rice emphasized that asset managers must be cautious about overfitting AI models to historical data, as markets rarely behave the same way twice.
“If we can be right 55% of the time, that’s a great outcome,” he explained. “But investors sometimes expect AI to predict stock movements with pinpoint accuracy, which just isn’t realistic.”
“Investors expect AI to predict stock movements with pinpoint accuracy, which just isn’t realistic.”
Key takeaway:
AI is capable of processing large amounts of information at breathtaking speed—as a complement, not a replacement, to human expertise in understanding the complexities of investing.
3. Trust and transparency is the next big challenge with AI
Trust is the foundation of asset management. Clients need to believe in the integrity of the strategies, data, and professionals guiding their investments. However, AI introduces new challenges to maintaining that trust. Rice pointed to the rise of deepfakes, AI-generated content, and fraud as emerging threats.
“AI almost decreases trust in the digital world,” he said. “Voices can be mimicked, images can be faked, and emails can be generated by AI impersonators. It creates an environment where people are more skeptical than ever.”
For financial professionals, this trust gap presents both a risk and an opportunity. Firms that can demonstrate responsible AI usage, verify data sources, and maintain transparency will have a competitive advantage.
“The AI industry will have to come up with a way to watermark or verify when AI tools are being used to produce content,” Rice suggested. “Otherwise, skepticism is only going to grow.”
“Investors expect AI to predict stock movements with pinpoint accuracy, which just isn’t realistic.”
Key takeaway:
Asset managers should proactively address these concerns by implementing ethical AI policies, ensuring transparency in AI-driven investment strategies, and educating clients on how AI is being used in their portfolio management.
This is just the beginning
Despite its limitations and challenges, AI’s role in asset management is only going to grow. The industry will continue to see advancements in automation, data analysis, and risk management—but with human oversight remaining critical.
“It’s like self-driving cars,” Rice concludes. “Would you feel comfortable sitting in the back seat with no one at the wheel? AI can be useful, but you still need someone paying attention, ready to take control when needed.”
Andrew Rice is a portfolio manager and COO at Algorithmic Investment Models (AIM), where he leads research into new model factors and deep learning approaches for the firm’s proprietary investment systems.
Since joining AIM in 2016, he has played a central role in developing the firm’s graphical analysis software and advancing its AI-driven investment strategies. In addition to managing firm operations, Andrew serves as a lead programmer and a member of the investment committee for the firm’s Decathlon strategies. Prior to his work in asset management, Andrew spent a decade as a quantitative analyst focused on public policy in Chicago.
For more AI insights, read Andrew’s blog posts here.
