The future of finance workflows starts with AI agents

Finance teams today face a deluge of paperwork and repetitive documentation tasks. In fact, a 2025 survey found that 79% of finance leaders say their teams are “swamped” with manual work. This includes preparing complex financial documents like confidential information memorandums (CIMs) for M&A deals or detailed credit notes for loan approvals, tasks that traditionally require gathering data from multiple sources, crafting narratives, and endless revisions for consistency. Yet despite heavy investments in automation, many organizations still struggle to reduce this workload. 

(For a deeper dive into advanced document automation, see our guide on agentic based workflows.) 

Enter AI agents: a new generation of AI tools that collaborate like a virtual team, each specialized in a part of the workflow. These agents can pull in data, generate content, cross-verify facts, and format outputs, all while you, the finance expert, retain oversight and control. 

AI agents alleviate this burden of a high workload by automating key parts of complex documents, freeing up teams to focus on higher-value analysis instead of paperwork.

In this article, we’ll explore how AI agents are revolutionizing finance document workflows, with a focus on two high-impact use cases: automating Confidential Information Memorandums and credit notes. You’ll learn the pain points these AI solutions address, how they work in practice, and the benefits they deliver in speed and accuracy. Let’s see how a process that once took days or weeks can now be done in hours, or even minutes, without compromising quality.

What Are AI Agents in Finance Workflows?

AI agents are specialized AI components designed to perform specific tasks and work together in a coordinated workflow. Unlike a single general-purpose chatbot, a multi-agent system assigns different roles to different AI agents, for example, one agent might extract data from financial records, another drafts a section of text, a third checks compliance or calculations, and a fourth formats the document. 

This mirrors how a human team would divide work, ensuring that each aspect of the document gets expert attention. According to PwC’s 2025 AI Agent Survey and related reports, organizations that deploy multiple coordinated AI agents across workflows report significant productivity gains. For example, companies are seeing improvements in efficiency ranging from 20% to 50% in areas such as finance, IT, and marketing. PwC emphasizes that the orchestration of multiple specialized AI agents working as integrated teams delivers more value and operational speed than relying on a single general-purpose AI system. This approach enables continuous performance improvements and helps enterprises unlock the full potential of AI technology with built-in human oversight and control.

Complex finance documents have many moving parts, and breaking the process into specialized subtasks allows for greater accuracy and control.

Traditional document automation (like basic templates or macros) had its benefits, but static templates often fall short for nuanced financial documents. Reports like CIMs or credit memos require context, narrative flow, and precise data, which generic automation tools can’t easily provide. 

AI agents, on the other hand, combine automation with intelligence. They can adapt to your organization’s data and style, apply complex rules, and even incorporate real-time information. 

Crucially, finance professionals maintain oversight: you can define the logic each agent follows and review outputs, ensuring compliance and consistency with corporate standards. This balance of automation and human control is key in regulated industries like finance.

By deploying AI agents in document workflows, companies can accelerate output without sacrificing accuracy or compliance. The following sections explore how this works in practice for two time-consuming finance documents.

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Automating Confidential Information Memorandums (CIMs)

A Confidential Information Memorandum (CIM) is a detailed document used in mergers and acquisitions or fundraising to present a company to potential investors/buyers. 

It’s essentially the “story” of the business – covering everything from financial performance and market analysis to management team and growth prospects. CIMs are hefty; typically 30–150 pages long, and crafting one is a major project. M&A experts spend significant time writing and polishing CIMs, and buyers spend significant time reading them. 

Traditionally, preparing a CIM means weeks of gathering data (financial statements, operational metrics, market research), then writing and revising each section to ensure a coherent narrative. One advisory firm notes it can take 2–8 weeks just to produce an initial CIM draft for a client, and that’s after collecting all the necessary information! It’s a cumbersome process involving analysts, managers, and multiple iterations to get the story and numbers perfectly aligned.

Pain Points

The manual CIM workflow is painful for several reasons. Data is often scattered across spreadsheets and a secure data room; maintaining consistency between the data and the written narrative is challenging (e.g. if a financial metric updates, many textual references and charts must update too). 

Ensuring a cohesive story – that the qualitative description matches the quantitative facts, requires constant cross-checking. There’s also pressure to make the CIM polished and professional, since it forms investors’ first impressions. All this means a CIM can consume many person-days of effort, often under tight deal timelines. 

As one M&A guide put it, “a well-written CIM” should streamline the process for buyers – yet getting to that final polished draft is anything but streamlined for those writing it.

How AI Agents Help

AI agents can transform the CIM creation process by handling much of the heavy lifting in hours rather than days. Imagine an AI “deal team” that assembles your CIM:

Together, these specialized agents can draft a CIM in hours instead of weeks. 

Human teams remain in control – guiding narrative tone, reviewing data points, and making judgment calls – while the repetitive drafting and cross-checking are automated.

To test this template out for yourself, sign up for free at Thinkeo!

Automating Credit Notes

Another high-impact application of AI agents is in creating credit notes (credit memorandums) in banking. These memos contain credit analysis for evaluating loan applicants or projects. Contents include borrower background, financial statement analysis, risk evaluation, market context, collateral, and a recommended decision/rating. Traditionally, underwriters spend days compiling them, which slows loan approvals.

Pain Points

Preparing a credit analysis memo is a labor-intensive process. Analysts must pull data from numerous sources: financial statements (often spread across PDFs or accounting systems), market research reports for the industry outlook, credit bureau reports, and sometimes news or public records about the borrower. 

They then have to summarize the business or project (including who the management team is and their track record), analyze financial ratios and cash flows, evaluate collateral, and identify key risks (like market risks, operational risks, etc.). Finally, they need to format all this into a cohesive report and often calculate a credit score or recommendation. It’s not uncommon for underwriters to spend several days compiling a single credit memo end-to-end. All the manual data entry and narrative writing not only takes time but is prone to errors or omissions. 

And when volume is high (consider a bank processing dozens of credit applications), these delays can slow down decision-making, frustrating both lenders and borrowers.

How AI Agents Help

AI agents automate gathering, analysis, and drafting tasks:

Instead of days, drafts can be ready in 30 minutes. AI not only accelerates production but improves consistency, standardization, and accuracy.

To test this template out for yourself, sign up for free at Thinkeo!

Benefits of AI-Agent Workflows for Finance Teams

• Dramatic Time Savings: Shrinks multi-day tasks into hours or minutes; frees staff for strategy and decision-making.
• Improved Accuracy & Consistency: Automated updates ensure all references and numbers are correct; prevents human error.
• Greater Insight Through Data Integration: AI combines multiple data sources for richer analysis.
• Scalability of Expertise: Embeds senior analysts’ expertise into workflows, enabling junior employees to deliver senior-level outputs.
• Auditability and Control: Always shows data sources; allows finance leaders and auditors to trace logic and ensure compliance.
• Adaptability: Workflows can be easily updated for new structures, metrics, or regulatory changes.

A New Era for Finance Document Workflow

CIMs that once took weeks can be compiled in hours, while credit notes shrink from days to minutes. Yet professionals remain in control, applying judgment and insight where it matters most. Early adopters are already seeing freed-up staff capacity, more accurate documents, and scalable workflows.

Is your finance team ready to leverage AI agents and leave the grunt work to your team of AI agents? Now is the time to explore these solutions and stay ahead of the curve.

At Thinkeo, we empower finance teams with no-code AI agent orchestration, build workflows for CIMs, credit notes, board reports, and more. Or try out our ready-made templates to get started faster, pre-filled with structure, agents, and best practices. Our goal isn’t to replace expertise, but to amplify it, enabling delivery of high-quality documents in a fraction of the time.

To test out Thinkeo, sign up for free here.

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