AI in Asset Management: Key Trends and Outlook for 2025

Artificial intelligence in asset management has steadily grown over the past several years as even formerly slow adopters are seeing the value and necessity in using it. Today, investment professionals acknowledge that AI tools don’t replace human intelligence — they enhance it.

By integrating AI tools and technologies into asset management, firms and investment managers can eliminate manual, repetitive tasks and focus on strategic, alpha-generating priorities.

AI enables analysts to uncover opportunities even amidst volatility, operate with higher agility, perform data analysis at scale, and assess risk more accurately. As adoption becomes more widespread, these capabilities will become even more essential to maintain a competitive advantage.

AI in asset management is projected to grow at a CAGR of 26.92% from 2025 to 2032, largely driven by increasing data volumes in the financial sector and a growing demand for enhanced efficiency, cost reduction, risk management capabilities, and the ability to deliver personalized investment strategies. Asset managers are realizing the value in investing in market intelligence platforms with AI search capabilities,  to accelerate research and maintain competitive strategies. 

Below, we take a look at some of the top AI trends transforming the asset management sector.

Generative AI Adoption

Now integrated into nearly every industry, generative AI has quickly become a must-have for any financial institution that aims to be on the leading edge.

In asset management, genAI can be used to automate tasks such as data entry, report generation, and compliance monitoring, freeing up time for higher-value strategic tasks. It can also help generate insights by identifying patterns and market trends that could be difficult to spot for a human — enabling better investment decisions and more effective risk management. 

Equity research in the AlphaSense platform predicts that generative AI will not replace human analysts in the next five years, but there will be more collaboration between human expertise and AI in decision-making processes.

AlphaSense offers several generative AI features that can augment asset managers’ workflows:

• Smart Summaries — genAI summarizations that allow you to quickly capture company outlook and track trends across industries
• Generative Search — genAI chat that answers your research questions in seconds, helping you get up to speed on any company or topic in seconds. Each answer also comes with direct citations to premium platform sources.
• Generative Grid — applies multiple genAI prompts on many documents at the same time to quickly provide organized answers to research questions at scale in an easy-to-read table format. 

Robo-Advisors Enable Personalization at Scale

Robo-advisors are becoming an increasingly important tool for customer acquisition within the wealth management industry, offering a low-cost, algorithm-based approach to asset allocation and financial advice. These AI systems can evaluate a user’s risk tolerance, expenditure patterns, and financial aspirations to provide personalized investment recommendations and ongoing portfolio management. 

The advantage of robo-advisors is that asset management firms can scale their services to previously underserved customer segments in this space, for whom a traditional human advisor may have been out of budget or otherwise inaccessible.

The challenge for firms will be balancing the availability of robo-advisors while balancing the value of their human advisors’ perspectives. This doesn’t pose quite the same challenge as it does in other industries, however. Investors trust and want to work with human advisors, and though they may incorporate robo-advisors in their workflow, they are unlikely to fully make the switch. 

Quantamental Insights Gain Momentum

Quantamental insights combine AI algorithms with human knowledge and experience. For asset managers, it’s the ideal scenario for adding AI to the investment decision process without sacrificing quality or the unique human perspective that can only be provided by a specific person or team.

In practice, quantamental research for asset management means using AI to mine and analyze vast amounts of data for insights, while also depending on the knowledge of particular companies, expert commentary, and other anecdotal insights that are essential for informing decisions.

Risk Management and Fraud Detection

AI-powered tools excel at risk management because they can identify potential risks and anomalies in real-time. Machine learning algorithms can detect irregular trading patterns, market disruptions, and other factors that might pose risks to investments long before a human would notice. Similarly, AI helps with fraud detection by spotting unusual activities that might indicate fraudulent behavior. 

Incorporating AI specifically for fraud detection and risk management has been highly beneficial for asset managers. By identifying subtle market shifts, predicting potential downturns, and recommending strategies to minimize risk, AI helps asset managers gain a competitive edge and ensures they win and keep client trust. 

At the same time, AI is not a silver bullet for fraud detection — though it can identify fraudulent patterns based on historical data, it’s less adept at catching new fraud tactics, which underscores the importance of human and AI collaboration.

Regulatory and Ethical Considerations

The increased reliance on AI has unsurprisingly raised regulatory and ethical questions. As AI systems play a larger role in investment decisions, transparency and accountability become crucial. Regulators require asset managers to justify the use of AI models and ensure that they do not introduce biases or manipulate markets. 

At the same time, 62% of asset management firms identify the absence of clear regulatory guidelines as a top challenge in AI adoption, according to a survey conducted by Wipro Limited.

As firms embrace AI, regulatory scrutiny around AI has intensified significantly. Ensuring AI implementations are not only innovative but also fully compliant with the latest regulatory standards will be a growing challenge for companies. Ensuring that AI systems are explainable and trustworthy is essential for maintaining both regulatory compliance and client confidence.”

– Wipro Limited Press Release, October 2024

Incorporating AI into asset management workflows also introduces ethical concerns, such as algorithmic bias — which could lead to unfair investment decisions — and overreliance on automation — which could lead to neglect of human oversight and reduced accountability. As such, it’s critical that asset managers are trained to work in tandem with AI, which means embracing efficiency gains while upholding trust and transparency with clients.

Incorporating AI-Driven Market Intelligence Platforms 

Traditional investment research processes are becoming impossible to execute at the scale required by big data and fast-moving markets. Asset management firms need a way to transform their processes to be highly automated and intelligent — in other words, they need to get faster without sacrificing quality.

Artificial intelligence is proving to be a leading solution to this end. AI-based market intelligence platforms enable firms to make smarter investment decisions at scale with key capabilities like:

• Top-Tier Data Sources: Access to content and datasets not easily found on search engines, social media, or other public sources — including regulatory filings, broker research, private company data, expert call transcripts, and premium news.
• Generative AI: GenAI-based features like Generative Search, Generative Grid, and Smart Summaries source information directly from premium market research sources in our platform, and deliver synthesized insights from those sources in digestible formats, in seconds
• Sentiment Analysis: AI tools using natural language processing (NLP) to extract sentiment from documents allow you to assess more nuanced meaning behind text.
• Dashboards and Visuals: Dashboard views and visual charts, graphs, and tables make presentations more approachable and quickly readable to a wider audience.
• Intelligent Search: Algorithms and other AI technologies make every search smarter. The tool can recognize your intent and deliver highly relevant results.
• Automated Alerts: Automation delivers highly customized alerts so you never miss an update about companies, industries, or topics you care about.
• Predictive Data Analytics: AI algorithms can deliver predictive insights to power accurate forecasts and smarter decisions for the future.

Related Reading: How AI is Shaping the Future of Investment Research

Get the Competitive Edge with AlphaSense

It’s clear that AI is the future of asset management, facilitating unprecedented operational efficiency, personalization, and risk assessment — and helping firms serve their clients better than ever before. 

But concerns about balancing human judgment and artificial intelligence are real and especially pertinent in industries where financial decisions are being made. That’s why the AlphaSense platform was uniquely designed to provide asset managers with best-of-both-worlds capabilities.

On the platform, you’ll have access to cutting-edge AI capabilities, as well as a wealth of human knowledge in the form of broker research, expert calls, and more.

Qualitative analysis features like sentiment analysis and Smart Synonyms™ maintain the meaning behind results you find on AlphaSense. At the same time, capabilities like generative AI, automated alerts, and sophisticated algorithms accelerate the speed at which you do research and drive optimization of key workflows.

Key areas of your investment research process you can level up with AlphaSense include:

• Thesis Validation: Search across and review multiple financial content sources in one place and easily extract quantitative data from qualitative sources.
• Top-Down Research: Leave CTRL+F behind and conduct thematic searches across broker research, transcripts, SEC filings and more — all in one place.
• Bottom-Up Research: Easily find the “why” behind a company’s financials with the ability to explore how key topics are discussed over time.
• Earnings Analysis: Identify shifts in market commentary with sentiment analysis in event transcripts, and use Generative Grid to conduct industry read-throughs in seconds.

Learn more about the AlphaSense impact by reading client success stories:

• Singapore-based firm YH2 Capital Management leverages AlphaSense to streamline internal intelligence and eliminate knowledge gaps
• ODDO BHF uses AlphaSense to remove blind spots and overcome information overload
• Boutique wealth management firm Summitry LLC uses AlphaSense Expert Insights to unlock differentiated insights and increase efficiency

Find out why 80% of the top asset management firms trust AlphaSense to give them the competitive edge.

Start your free two-week trial today.