How to Scale Agentic AI in Investment Management

How to Scale Agentic AI in Investment Management

Only 27% of asset managers report substantial business impact from AI investments, even as 78% are now piloting agentic AI systems¹. The disconnect is architectural. Agentic AI implementation challenges emerge when firms deploy agents on legacy platforms. These systems can't bridge data silos, partner ecosystems, and proprietary workflows, forcing manual intervention at every step.  

In our conversations with institutional clients about AI pilots that haven't scaled as expected, we've noticed a consistent pattern: it's easy to create AI prototypes, but much harder to get them into production at the desired quality level. The breakdown typically happens when the AI needs to complete workflows across fragmented systems. Operations teams must manually move data between platforms or reconcile conflicting outputs. 

Disconnected systems and siloed data platforms hinder agentic AI initiatives from fully realizing the efficiency gains across the investment lifecycle. At SimCorp, our focus is bringing agents into one ecosystem: whether built by us, curated partners, or clients themselves, making them interoperable, easily accessible, and consistently governed. 

What do AI agents require from investment platforms?

The shift from conversational AI to autonomous agents exposes an infrastructure gap. While generative AI-based chat interfaces respond to isolated prompts, agentic systems must translate asks into executable workflows across multiple systems, requiring synchronized data models and unified authorization.  

The distinction between conversational AI assistants vs autonomous AI agents matters: AI assistants require human prompting at each step, while agents can execute multi-step workflows with less frequent human intervention. In both cases, humans remain central, but agents shift people from executing tasks to overseeing outcomes and making strategic decisions. 

When a portfolio manager asks an agent to optimize for ESG compliance, that request triggers a chain of operations. The agent must pull current positions, check regulatory requirements, run simulations, generate rebalancing recommendations, and update reporting. If those functions live in separate systems with incompatible data models, the agent will deliver poor-quality or incomplete responses. Without a unified platform, someone must manually bridge the gap.  

McKinsey estimates agentic agents could affect 25-40% of an asset manager's cost base, with technology and operations functions seeing efficiency gains exceeding 20%². But firms only capture these gains when agents can execute complex workflows seamlessly. In practice, we’ve seen firms struggle with this: an agent generates rebalancing recommendations, but someone must manually export the data, upload it to the order management system, and reconcile any differences. The efficiency gains evaporate in the handoffs. 

Platform architecture determines AI ROI 

The industry conversation focuses on which AI models to deploy. But our experience with clients suggests that when AI pilots don't scale, the issue is rarely the model's capabilities. Success depends on whether the platform architecture can support autonomous execution.  

How does a unified platform enable autonomous AI workflows? 

SimCorp consolidates the investment lifecycle into a single environment. Our approach centers on embedding intelligent agents natively into SimCorp One to ensure that AI functions within the same secure, compliant, and fully auditable environment our clients already rely on. This "intelligence from within" philosophy supports human expertise rather than attempting to replace it. Accelerating insights, optimizing actions, and strengthening controls require position data, risk models, compliance rules, and reporting tools to operate within one system. Agents must access what they need when they need it. 

How SimCorp One enables AI agents from multiple sources to work together  

SimCorp One provides the shared infrastructure where agents built by SimCorp, our partners, and our clients can interoperate within a unified framework. Because they all run on the same platform, three types of agents can share data and coordinate workflows seamlessly. 

• SimCorp agents enhance productivity and analytical depth across the investment lifecycle. SimCorp One Copilot lets portfolio managers compare historical performance, create visualizations, and build custom dashboards without navigating complex menus or remembering system commands. 
• Partner agents extend and complement SimCorp's capabilities in specialized domains. Our recent alliance with Axyon AI, for example, enriches SimCorp's predictive analytics and risk modeling. Additional integrations will provide clients with direct, immediate access to market-leading external innovations, delivered securely within their existing SimCorp One environment. 
• Client agents represent an emerging frontier. A growing number of institutions are developing proprietary AI, such as models that forecast issuer rating movements or contagion risk. SimCorp One will empower these client-built agents to feed their output directly into the portfolio management workflows, without compromising intellectual property or data sovereignty. 

What is decision provenance and why does AI governance require unified audit trails? 

Portfolio managers ask us the same question during AI implementations: "How do I know this answer is correct?" The question becomes more urgent as AI workflows span multiple systems: internal models, external partners' engines, and client algorithms. When multiple AI systems generate a single answer, who is accountable for the quality? 

Decision provenance is the complete record of how an AI system reached its conclusion. For agentic AI governance, this isn't optional. Establishing decision accountability at scale requires unified audit capabilities that fragmented approaches cannot provide. 

Trust in agentic systems won't come from limiting autonomy; it will come from making autonomous decisions transparent and reversible across the entire ecosystem. When an agent recommends portfolio rebalancing after coordinating multiple AI systems, portfolio managers need full visibility into the decision process and the ability to modify underlying logic for future executions. 

SimCorp One builds decision provenance into the orchestration layer. Every autonomous action generates structured records that capture not just what happened, but why it happened: which data sources were queried, which partner systems contributed analysis, which constraints applied, and which intermediate results shaped the final recommendation. When workflows span the ecosystem, the complete decision trail remains accessible, with each contribution independently traceable.  

Recommendations touching client assets must be explainable to regulators and auditable by compliance teams. Firms that build transparency and accountability into their architecture can deploy agents faster and scale them more confidently. 

SimCorp One's observability framework extends beyond traditional system monitoring to include agent activity. Our agent orchestration framework provides real-time visibility into agent decisions, data sources accessed, and systems activated, creating comprehensive audit trails across the ecosystem. This enables clients to confidently integrate agentic workflows into daily operations; with the same traceability and reliability standards they expect from platform operations.  

These questions reveal whether your platform can support agentic AI: 

1. Can an agent access all the data it needs to complete a workflow without manual data transfers? 
2. When an agent spans multiple systems, can you trace every step of its decision process? 
3. Can you deploy a new agent—internal, partner, or proprietary—without custom integration work? 

If the answer to any of these is no, you have an infrastructure problem hindering the potential ROI of your AI investments.  

 

A unified platform removes the barriers that prevent AI pilots from becoming production systems. Firms capturing value from agentic AI aren't necessarily running more sophisticated models. They're running those models on platforms designed for agentic execution, where AI handles routine workflow steps while humans focus on decisions requiring judgment and strategic oversight. 

In investment management, the AI strategy gets the attention. Platform capabilities determine the outcome. 

Footnotes: 

1. EY 2025 study on AI in investment management 
2. McKinsey 2025 study on AI adoption in asset management