Adoption of AI in Investment Management: Key Use Cases & Innovation Drivers
The impact of AI in investment management can be profound, with potential cost base reductions of up to 25-40%. Our analysis has yielded key areas of value creation, such as enhancing investment idea generation, streamlining client reporting, and automating operational workflows.
Investment decision-making has always relied on the right mix of data, analysis, and scalable processes. Today, AI in investment management offers the ability to streamline these workflows and uncover opportunities conventional methods would miss. At Everysk, we’ve been working at the intersection of Agentic AI, Generative AI, and Process Automation, and we’ve seen firsthand how their complementarity delivers outsized benefits for capital markets clients.
Automation and Agentic AI serve distinct but complementary roles: automations execute repeatable, auditable processes with precision, while Agentic AI enables more conversational, goal-oriented analysis. When integrated from the outset, they create workflows that are both efficient and adaptive.
In this blog, we’ll share concrete use cases from our work with capital markets firms, highlighting how combining AI and automation drives measurable impact.
USE CASE: Investment Research Generation
Everysk works with clients who need to analyze information from multiple sources, including internal research, market data, and SEC filings. Using an automation platform to pre-process the information is crucial for providing guardrails within the Agentic AI framework.
At Everysk, we have several off-the-shelf digital robots that provide the necessary pre-processing logic, for example:
- Deep Research robot ingests a markdown (from PDF) and can search its knowledge base to answer specific queries: “Use official SEC EDGAR links to retrieve granular 10K-10Q information for IBM for the latest quarter”
- Everysk Gen AI robots can make the LLM’s output conform to any schema. So instead of free-form text, the Gen AI robot will return summaries in a consistent format that can be reliably searched and used in downstream processing. This makes it more reliable to pass results to another workflow or agent.
The result of the process above is readily available and cleaned data for the Agentic AI. The idea generation process can now begin to return results for prompts such as:
PROMPT: “Using my investment mandate, suggest the top ten best stocks, ranked by growth potential from my buy list, using the latest SEC filings data from EDGAR and my internal research documents.”
USE CASE: Extract Fund Fee Info from Fund Prospectus PDF
Uploading hundreds of PDFs into ChatGPT, Claude, or any other LLM client is a non-viable approach. There are size limitations, and complexity reduction steps need to be performed before the information can be reliably used by an Agent to prevent hallucination. Additionally, the token cost will be prohibitive, but this can also be controlled by efficient workflows.
At Everysk, we utilize our time-tested automation platform to pre-process, store, and simplify the complexity of a batch comprising hundreds of extensive documents, and automatically enable access to them by Agentic AI. A schematic of this pre-processing is presented below:
- PDFs are securely uploaded and stored in the platform.
- Markdown files are generated from the PDFs using OCR.
- Generative AI (RAG) simplifies a document’s information, retaining only relevant sections such as management and performance fees. RAG tokens are also lower cost than other AI tokens, reducing complexity upfront and lowering the cost of later Agentic steps.
- Simplified, final markdowns can be stored in the platform and accessible from the Agentic layer to efficiently retrieve the most relevant information.
PROMPT: “Extract the fee calculation tables and terms from the attached prospectus.”
USE CASE: Pre-Trade Compliance
Everysk automates compliance checks on start-of-day (SOD) portfolios by ingesting positions and codified rules, then testing exposures at scale across thousands of portfolios. Our Agentic AI framework augments this by enabling real-time, what-if checks. Portfolio managers can simply ask: “If I buy another 1,000 shares of Apple, will my portfolio still be compliant?”
To support this functionality, full workflows can become Agentic tools. The agent packages information from the prompt, runs the workflow, and returns results. Any workflow built in a client’s account is automatically available for agentic use.
- LLM identifies the Apple ticker and retrieves the relevant portfolio.
- Portfolios and trades are passed to a pre-trade compliance workflow, which compares the new portfolio against compliance limits.
- The output of the Everysk workflow is passed back to the LLM
- Pre-trade portfolio analytics and reports are sent back to the PM via chat, informing them of the trade’s impact on their portfolio.
This functionality ensures workflows run securely in Everysk, while only suitable outputs are shared with the agentic framework.
PROMPT: “If I buy another 1,000 shares of Apple, will my portfolio still be compliant?”
USE CASE: Portfolio Activity Summary
Our clients benefit from daily deliverables in the form of reports and alerts. Since our inception as a firm, we have placed an enormous focus on being data-centric and persisting daily portfolio data, augmented with risk, liquidity, and compliance analytics in clients’ accounts.
AI in investment management has provided valuable tools to query these raw datasets, providing a qualitative assessment of a portfolio manager’s weekly activity. We use Deep Research robots and Gen AI robots to complement our quantitative calculations with qualitative assessments:
PROMPT: “Summarize key changes in position sizes, exposures, and security level volatility over the past week. Research relevant news on the positions and any developments that may impact risk over the next three months.”
Closing Thoughts: Impact of AI in Investment Management
The examples above demonstrate that relying solely on a commercial LLM is neither scalable nor cost-effective for leveraging AI in investment management. Real impact occurs when AI is embedded within an automation framework, one that ensures data integrity, controls costs, and delivers actionable outputs. At Everysk, we’ve seen that combining Agentic AI with process automation unlocks four critical benefits:
- A secure, centralized repository for documents and portfolio data.
- Pre-processing with OCR and RAG to simplify information and reduce token costs while preserving data quality.
- Customizable workflows that can be API-triggered and exposed as Agentic tools, empowering clients to extend functionality without vendor lock-in.
- Integrated generative and analytical robots that complement automation with flexible, context-aware insights.
By uniting automation with Agentic AI, firms can move beyond one-off experiments and create scalable, auditable, and adaptive workflows that drive measurable efficiency and innovation across capital markets.
Learn how Everysk is enabling firms with powerful AI use cases and automation at everysk.com
