TLDR: FactSet Research emphasizes the transformative potential of modular, API-driven AI agents in establishing a new operating system for finance. These agents are designed to enhance operational excellence, risk management, and client engagement through flexible, ‘LEGO-style’ architectures. FactSet’s Intelligent Platform offers various AI-powered solutions, while PwC’s research indicates significant efficiency gains and a shift towards strategic analysis in finance functions adopting AI agents.
FactSet Research is at the forefront of a significant shift in the financial industry, championing modular AI agents as the cornerstone of a new operating system for finance. According to a recent publication by FactSet Research Systems Inc. on August 27, 2025, these API-driven AI agents are poised to redefine operational excellence, risk management, and client engagement. The core concept revolves around a flexible, modular ‘LEGO-style’ architecture that facilitates scalable data connectivity and advanced explainability within financial systems.
FactSet’s Intelligent Platform is a testament to this vision, integrating AI-powered task automation with conversational AI to deliver trustworthy and transparent insights from both structured and unstructured data. This platform aims to liberate financial professionals from routine tasks, allowing them to focus on strategic initiatives. Key offerings include the FactSet Pitch Creator, which automates the banking pitchbook creation process by dynamically generating and inserting codes into Microsoft Excel, thereby boosting productivity and information discoverability. The Portfolio Commentary tool leverages AI to automatically generate attribution summaries, providing executive summaries, subperiod analysis, market reviews, and impactful securities analysis. The FactSet Transcript Assistant utilizes conversational chatbots to extract actionable insights from earnings call transcripts, enabling custom queries and seamless data export.
Further enhancing financial workflows, FactSet’s PMHub offers AI-powered insight generation for confident decision-making, with tools like Portfolio Assistant and Security Explanation. The Internal Research Notes feature a Draft Assistant for content suggestions and a Topic Assistant for auto-tagging, improving research efficiency. The Signals tool provides timely, relevant alerts via API or FactSet Workstation, supercharging research by recognizing themes, identifying sentiments, and extracting answers from various text content. FactSet also offers an Open Platform for AI development, providing curated data and powerful chatbots to accelerate innovation.
FactSet is committed to responsible AI deployment, emphasizing robust governance and security measures. The company utilizes secure, private instances of Large Language Models (LLMs) and employs Retrieval Augmented Generation (RAG) methods to combine generative AI models with its trusted, fact-based data, thereby mitigating data hallucinations. All user prompts and responses within FactSet GenAI products are confidential, isolated, and protected, ensuring data security and privacy. Access is strictly governed and limited to authorized FactSet employees for accuracy evaluation and service improvement. Generative outputs are clearly labeled with linked references to sources, promoting transparency, auditability, and explainability.
This transformative shift is not exclusive to FactSet. PwC’s research, published on July 17, 2025, highlights that finance functions are at a tipping point, facing escalating demands for actionable insights amidst peaking cost efficiencies and stretched capacity. PwC advocates for a reimagined finance operating model where AI agents are not merely tools but enablers of the future of work. Their ‘AI Agent Survey’ reveals that 79% of executives are already adopting AI agents in their companies, though only 34% are currently utilizing them in accounting and finance.
AI agents enable a new finance operating model by acting intelligently, autonomously, and in teams. Each agent can be assigned a specialized role, such as an accountant, FP&A analyst, or compliance specialist, equipped with relevant skills and datasets. These agents can be orchestrated into workflows to perform tasks like reconciling invoices with purchase orders or consolidating cash positions and forecasting inflows/outflows. They learn from past actions, inputs, reviews, and exception handling, continuously improving their performance and developing new solutions.
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PwC’s analysis across over 40 finance processes indicates that AI agents can deliver substantial value. In procure-to-pay, AI-driven invoice extraction and PO matching can slash cycle times by up to 80%, enhance audit trails, and redirect human effort towards vendor strategy. For treasury operations, agents can pull and consolidate cash balances, predict near-term inflows and outflows, flag potential issues, and recommend transfers or investments. This frees treasury teams to focus on capital allocation strategies and intercompany fund optimization. The benefits include up to 90% time savings in key processes, up to 60% of team time redirected to insight work, and up to 40% improvement in forecasting accuracy and speed. Adopting a modular architecture for AI agents is crucial for scalability and cost-effectiveness, allowing for the reuse of code and frameworks across various workflows.
