GenAI is reshaping investment workflows faster than most firms can adapt. The release of Claude for Financial Services is the latest step in applying GenAI in the investment industry. Its focus on domain knowledge and specialized workflows distinguishes it from generalized frontier LLMs and raises important questions about how financial workflows will evolve, how tasks will be divided between humans and machines, and which skills will be needed to succeed in the future of finance.
Financial firms are contending with the most significant overhaul of technology capabilities in a generation. AI-driven digital transformation is reshaping job roles and investment processes, prompting professionals to reconsider the boundaries between human and machine cognition, while firms work to upgrade their technology stacks and human capital to remain competitive.
Amid this shift, firms and professionals must reevaluate the skills needed for success. Projecting how AI will change workflows and job roles is challenging given the pace of technological progress and uncertainty around transition pathways. Even so, this assessment is necessary for strategic planning, both for industry leaders and for individuals considering their career paths.
CFA Institute continually monitors and interprets AI developments and provides guidance and education to help financial professionals navigate the changing landscape and build the career skills they need to succeed. To advance this mission, we are embarking on an ambitious project to analyze the structural implications of AI for the investment profession. We will explore scenarios for how AI will affect professional practice, judgment, trust, accountability, and career paths, building on our research to date.[1]
In this context, two questions often arise: Will AI replace human professionals? And what is the relevance of the CFA Program in a future environment where AI can perform most technical tasks?[2]
As we’ve noted elsewhere, we believe the future will be defined by the complementary cognitive capabilities of humans and machines, characterized by the “AI + HI” paradigm and the continued importance of professional competence. To understand what this combination looks like, it is first necessary to assess the current extent of AI adoption in investment workflows, before identifying possible transition pathways to future scenarios characterized by differing mixes of human and machine interaction.
Current Landscape
Early last year, CFA Institute published a survey-based study, “Creating Value from Big Data in the Investment Management Process: A Workflow Analysis.” In it, we analyzed the extent of technology adoption across different workflow tasks performed in categories of job roles including advisory, analytical, investment and decision-making, leadership, risk, and sales and client management.
A key takeaway of this work is that investment professionals adopt a multihoming strategy, in which they use multiple platforms and/or technologies to complete a task. In the Analytical job role category, three example workflows—valuation, industry, and company analysis, and preparing research reports—illustrate this pattern.
The table shows the proportion of respondents that use different technologies for each of these tasks. Unsurprisingly, traditional tools like Excel and market databases continue to be the most heavily used, but respondents also report integrating tools such as Python and GenAI alongside traditional software. For example, while 90% of respondents expressed using Excel for valuation tasks, 20% also indicated using Python in this workflow. For analytical roles, GenAI was most used to assist in the preparation of research reports, cited by 27% of respondents.[3]
Source: Wilson, C-A, 2025, Creating Value from Big Data in the Investment Management Process: A Workflow Analysis: https://rpc.cfainstitute.org/research/reports/2025/creating-value-from-big-data-in-the-investment-management-process.
GenAI in Practice: A Workflow Example
Let’s consider conducting industry and company analysis, where, at the time our survey was conducted in 2024, 16% of respondents acknowledged using GenAI in this workflow. Our Automation Ahead content series, in the installment RAG for Finance: Automating Document Analysis with LLMs, provides a concrete example of how GenAI can enhance this workflow..
The case study is supplemented with Python notebooks in our RPC Labs GitHub repository. It shows how RAG can extract executive compensation and governance details from corporate proxy statements across portfolio companies and present the results in a structured table, one of several tasks performed in this workflow.
Such a task is traditionally manual and time-intensive, with the effort required largely driven by the number of portfolio holdings. With GenAI, the process can be scaled efficiently with only marginal additional compute, freeing the analyst from manual data extraction and preparation of a tabular comparison.
With the tasks of data extraction and information presentation outsourced to the GenAI model, the analyst can focus on data interpretation rather than preparation. Instead of crunching the numbers, the analyst focuses on evaluating the output by interrogating the model, checking data validity, understanding the limitations of the analysis, correcting errors, supplementing the output with additional information or insights from other sources, all toward the goal of identifying potential governance risks across portfolio holdings.
Far from eliminating the need for a human analyst, this example shows how greater value can be unlocked from human input by providing more time and capacity for critical thinking and decision-making. It also illustrates the limitations of AI (such tasks have imperfect accuracy scores), and the enduring need for human oversight and judgment.
Evolution
Agentic AI has emerged as a powerful tool that can further enhance workflows and deepen the human-machine interaction. These tools build on some of the limitations of RAG and incorporate chain-of-thought reasoning and external function calling (see our article, “Agentic AI For Finance: Workflows, Tips, and Case Studies“). AI agents expand the scope of tasks machines can perform and may shape the future direction of human-machine interaction.
Source: Pisaneschi, B., 2025, Agentic AI For Finance: Workflows, Tips, and Case Studies: https://rpc.cfainstitute.org/research/the-automation-ahead-content-series/agentic-ai-for-finance.
In many ways, this evolution simply extends the multihoming strategy, combining multiple tools and platforms into a single user interface. Claude for Financial Services reflects this approach, connecting with market databases and traditional platforms like Excel to produce reports and analyses for the user. In this way, AI functions as an application layer on top of other software tools, interfacing with the human analyst who retains oversight and accountability.
Professional judgment remains essential to test assumptions and validate data sources and references. Moreover, effective use of these tools also depends on strong foundational knowledge in finance and investing, enabling analysts to trust and own model outputs and maintain a reasonable basis for investment decisions.
Professionals will also need soft skills that cannot be outsourced to machines, including relationship-building and exercising duties of loyalty, prudence, and care, grounded in ethical values.
Going forward, CFA Institute will conduct in-depth research on workflows and skills as AI reshapes the investment profession. While the mix of tasks and the skills needed to perform them will undoubtedly continue to evolve, and in ways we may not foresee, we expect the AI+HI principle to remain the foundation of ethical professional practice and sound investment management.
We invite practitioners to share their thoughts in the Comments section on the skills and workflow shifts you are observing.
[1] Our research inventory on AI includes:
AI in Asset Management: Tools, Applications and Frontiers
AI Pioneers in Investment Management (2019)
T-Shaped Teams: Organizing to Adopt AI and Big Data at Investment Firms (2021)
Ethics and Artificial Intelligence in Investment Management: A Framework for Professionals (2022)
Handbook of Artificial Intelligence and Big Data Applications in Investments (2023)
Unstructured Data and AI: Fine-Tuning LLMs to Enhance the Investment Process (2024)
AI in Investment Management: Ethics Case Study (2024); AI in Investment Management: Ethics Case Study Part II (2024)
Creating Value from Big Data in the Investment Management Process: A Workflow Analysis (2025)
Synthetic Data in Investment Management (2025)
Explainable AI in Finance: Addressing the Needs of Diverse Stakeholders (2025)
Automation Ahead: Content Series (2025)
[2] See for example Tierens, I., 2025, AI Can Pass the CFA® Exam, But It Cannot Replace Analysts
[3] An interactive version of this data is available on our RPC Labs GitHub repository: https://github.com/CFA-Institute-RPC/AI-finance-workflow-heatmap
