From pilots to agents: How the second wave of AI is transforming asset management

An inflection point

Asset management is in the midst of an inflection point with Generative AI.  Traditionally, the industry has favored caution – a stance that can often clash with the uneven results of initial GenAI pilots.  However, macroeconomic conditions that once buoyed asset management have shifted, impacting the bottom line. There is now an imperative for the industry to lay the groundwork for more durable, forward-looking, integrated strategies. While technology spend in the industry has grown, 60-80% of that is dedicated to maintaining legacy systems rather than revenue-generating transformation. Efforts around AI will need to reflect not only technical feasibility, but also alignment with the investment goals, operational models and the governance standards expected in institutional finance.

History shows that transformative technologies go through a period of refinement before they soar. The automobile was invented in 1885 but was viewed as an unreliable novelty until the introduction of the Ford Model T around 20 years later. Horses – firmly embedded as core to humanity’s transportation, trade, and culture for 3,000 years – were supplanted in just 20 years, relegated to  novelty. At the turn of the 21^st century, we saw the internet boom that promised to reinvent finance, enterprises and our daily lives seemingly end in a bust of failed experiments. Left in its wake was the general idea that having a website was equal to having a business model, which left some businesses scrambling for what to do next. Yet just a few years later, the second wave of internet advancements (e.g., broadband rollout, data center virtualization, cloud computing) geared towards enterprises finally did help deliver the transformative change that has led to technology enterprises comprising around 40% of the S&P 500.

We are seeing a similar pattern with GenAI in asset management. The first wave of GenAI pilots, typically isolated proofs–of–concept using generic LLMs, created excitement but with broadly variable ROI. With this second wave, we are now in the “soaring” inflection point of Generative AI, which has been refined with enterprise value in mind. AI Agents embedded in real workflows can autonomously execute tasks, coordinate with other agents, integrate into existing systems, and replicate and enrich complex work products. Crucially, they are designed to still ultimately operate under human oversight and within guardrails.

From isolated pilots to integrated AI agents

GenAI’s introduction to asset management came in two forms:

1. Broad, off-the-shelf copilots and chatbots implemented across the enterprise, which enabled some productivity gains but did not necessarily transform the day-to-day.
2. Use cases specific to business functions, but often conducted in fragmented pilots with teams operating in isolation and within the confines of legacy processes and tools.

Agentic AI offers the opportunity to reinvent and enhance the investment process by embedding intelligence directly into the fabric of investment operations. AI Agents are not based on static algorithms; they are autonomous digital co-workers that can perceive, plan, and act within defined bounds. These agents carry out multi-step tasks (e.g., monitor data feeds, analyze patterns, draft a report) without needing a human prompting it at each step. They can also call APIs, query databases, or trigger alerts. In practice, this means an agent can, say, continually scan news and market data and proactively alert the team when a relevant risk signal emerges, as opposed to a passive dashboard that is only useful when someone actually remembers to view and derive insights from it. Importantly, unlike black-box LLMs, AI agents offer traceable, auditable, sourced outputs, and can be regulated with access controls and human-in-the-loop (HITL) oversight – important for industries like asset management.

Use cases with direct investment relevance: Where is this technology proving its worth?

• Early warning and monitoring: Agentic AI is elevating risk management from periodic check-ups to continuous oversight. Consider regulatory compliance and operational risk: instead of solely relying on manual reviews or waiting for quarterly reports, AI agents can constantly audit transactions and communications in the background. They flag anomalies or potential non-compliance issues as they unfold, providing risk teams with the tools to intervene early. For instance, an agent monitoring portfolio trading patterns might catch an unusual spike in exposure to an emerging market currency and instantly alert risk officers if it breaches set limits.
• Research and analysis acceleration: Perhaps the most immediate impact of GenAI agents is in boosting the productivity of research teams. Across equity, fixed income, and multi-asset teams, analysts spend an inordinate amount of time gathering information: reading financial statements, transcribing earnings calls, scouring economic data, before they can even begin to generate insight. Generative AI agents can extract financial metrics, summarize earnings calls, and collect macroeconomic indicators, sourced research reports, and credit assessments in seconds – enabling users to devote more attention to insights and decision-making.

The use cases and applications are growing, but the pattern is clear: agentic AI assists users with finding alpha signals and risks earlier, potentially creating more bandwidth for humans to focus elsewhere.

From prototype to production: Integration, oversight, and architecture

If Agentic AI is the engine, the operating model is the chassis required to realize value. To truly go from pilot to production, asset managers must focus on the critical enablers: workflow integration, human oversight, and data architecture. These are the fundamentals that largely determine whether AI remains in pilot purgatory or becomes transformative infrastructure.

Workflow integration: The highest ROI from AI will come when it is embedded into existing employee workkflows. For example, if a portfolio manager works in Excel, an AI agent should be able to align with that environment. This requires close cross-functional collaboration between teams to redesign processes.

Human oversight and accountability: A governance framework is essential. This means defining which actions require human approval or human QA. Every AI output should be auditable, traceable and sourced. AI Agents should have appropriate access controls to data and internal systems, similar to how access controls and privileges are set for human users today: with data minimization and privacy standards enforced and with high-impact actions requiring additional sign-off. This helps to mitigate risks and provides transparency for both regulators and teams alike that AI is augmenting the decision-making process, not itself making judgments.

Data architecture and infrastructure: Agentic AI’s effectiveness is only as good as the data it can access. Siloed data systems severely limit AI’s value – agents cannot form a holistic picture to make informed decisions. The new wave of AI adoption is forcing a long-needed investment in unified data architecture: e.g., common data lakes or warehouses, standardized data taxonomies, and real-time data pipelines.

Additionally, connectivity infrastructure is key: agents need secure access to internal systems and external APIs. For example, a portfolio monitoring agent might need to pull data from an internal risk system, query an external news API, and write an alert in an email. Ensuring those connections are reliable and governed (with proper permissions) directly affects whether AI delivers value. AI agents can analyze swaths of structured and unstructured data simultaneously to find patterns that humans would miss, but it can only do so if the data is there and accessible. Firms that treat data as a strategic asset, by cleaning and linking it, will see the success of AI initiatives.

What’s next: From edge case to industry standard

After years of AI promises, why should asset management leaders believe that this wave will deliver? In short, because we are seeing the pieces come together with agentic AI. The ecosystem around the technology is more mature, the implementation playbook is better understood, and common patterns have emerged from positive ROI case studies. Generative AI itself has advanced dramatically in just the past two years. Today’s models, architectures, and tools are far more capable of understanding finance-specific content and performing complex reasoning than the LLMs of yesterday. The urgency is also greater now. Asset managers face margin pressures due to changing macroeconomic headwinds.

AI agents are helping redefine what high-performance teams can accomplish. The firms likely to succeed are those that view AI not as a bolt-on experiment but as a strategic capability: something to integrate across desks, asset classes, and functions.

Moody’s Agentic Solutions exemplifies this new era of AI-powered workflows. It’s where our decades of data and risk experience meets cutting-edge AI orchestration, empowering financial teams to achieve deeper insights in a fraction of the time.

Join Omar Khan and other industry leaders as they discuss the real world applications of Agentic AI in the asset management space at FoAM.

Interested in learning more?  Find out how we can help with Moody’s Agentic Solutions.

About the author:

Omar Khan is a Senior Director of GenAI at Moody's, where he shapes Generative and Agentic AI solutions and partners with executives in Financial Services and Insurance to translate innovations in AI into measurable business impact. Leveraging Moody’s proprietary data, advanced technology and award-winning insights, Mr. Khan works with financial services institutions to deploy AI-powered workflow automations that support decision-making, reduce complexity, and perform with precision in regulated, high-stakes environments.

Mr. Khan spent his early career consulting with Private Equity firms on transformational initiatives and operational optimization.  He has dedicated the last  7 years to leveraging the application of AI to drive transformation within Financial Services.  Prior to Moody's, Mr. Khan was a founding member of an AI company in the insurance space, growing it to a $1 billion valuation by spearheading its North America Go-to-Market and Product Strategy, and establishing and leading its Partnerships and Business Intelligence practices.

Mr. Khan has championed pragmatic environmentalism as a volunteer for Trees New York, The Big Reuse, NYC Parks, and as a member of Transportation Alternatives. He holds of Bachelor of Science in Finance from New York University - Stern School of Business