Every week there seems to be another announcement: a new agent, a new copilot, a new cowork tool. The names are different, the vendors are different, and if you have been trying to follow along, the whole thing probably feels more confusing than useful. I understand that feeling. I track this space closely and even I have had moments this month of not being entirely sure what a given product actually does versus what the press release says it does.

But something is happening underneath all the noise that is worth paying attention to. These tools are becoming genuinely capable. Not capable in a “look what AI can do in a demo” way. Capable in a “I used this on a real client project over the weekend and it saved me several days of work” way. The shift from AI as a drafting assistant to AI as something that can execute real workflows on your behalf is not coming. It is already here.

This edition is my attempt to cut through the branding and give you a practical map of what exists right now. I reviewed six of the most relevant agentic tools for finance teams, built a framework for comparing them on the dimensions that actually matter, and put together a full task design guide for getting reliable results. If you have been curious about this category but not sure where to start, this is the edition to read.

In the last 60 days, every major AI company shipped something they are calling an agent, a copilot, or a cowork tool. The problem is that these names are used inconsistently across vendors. Microsoft has Copilot Cowork. Anthropic has Claude Cowork. OpenAI has Agent mode. Perplexity built something called Perplexity Computer. An open-source project called OpenClaw uses none of these terms and does something different from all of them.

These names are so confusing and tell you almost nothing about what the tool actually does.

So I put together a structured comparison of the six most relevant agentic tools for finance teams, current as of March 2026. Before getting into the comparison, here is the framework I used to evaluate them. It cuts through the branding and gets to what actually matters for finance work.

1. Deployment: where does it run?

This determines where your data goes. Cloud-based tools process your files on vendor servers. Local tools keep data on your machine or within your own IT environment.

2. Data access: what can it actually reach?

Some tools access files on your local drive directly. Others only work with cloud storage. Others require you to upload or paste content manually.

For finance work this matters more than most people expect. Reconciliation files, contract PDFs, expense exports typically live on local drives or shared drives, not in cloud storage an AI tool can reach.

3. Integration: what external tools can it connect to?

4. Output: what does it actually deliver?

There is a real difference between a tool that produces text you copy-paste versus one that creates finished files in your system.

Here is a structured comparison of 6 “agentic” tools.

And a structured document with details you can download below

Last weekend, I spent about six hours on a project that would normally take a week. A client CFO needed help with a contract dispute: two-plus years of history, multiple overspent POs, and a folder of documents with no naming convention and no organization. The task was to map everything to specific SOWs and projects, reconcile roughly 1,500 line items against source files, and produce a clean summary with a linked presentation.

I used Claude Cowork. The actual file work took about 20 minutes. The other five-plus hours were spent understanding the structure, deciding what needed to be reviewed, and figuring out how to direct the agent.

That ratio is the point. The bottleneck was no longer the execution. It was the thinking.

Projects like this don't come up every day. But every CFO runs into them: a dispute that needs documentation fast, a diligence request with a tight deadline, a close package pulling from fifteen disorganized folders. These tools are now capable of handling the execution layer of that work.

Knowing what they can do before you need them is the whole game.

If you are already using LLMs regularly, this is the moment to look at agentic capabilities seriously. This is not the "promising but unreliable" stage of AI. These tools complete real multi-step work, handle recurring tasks on a schedule, and deliver finished outputs you can use directly. The gap between what most finance teams are doing with AI today and what is now possible is significant.

Before you or your team starts using these tools, four new risk types are worth knowing about. 

Adding agentic tools to your finance workflows should also trigger the AI policy update!

These tools are capable of handling work that used to take days. The difference between a useful agentic workflow and a problematic one usually comes down to how the task is designed before the agent starts. In the subscriber section, we go through what a well-scoped agentic task looks like, what to include every time, and a full worked example for a monthly business review report.

If this edition does one thing, I hope it saves you a few hours of research. These tools are worth your time. Not all of them, and not all at once. But pick one that fits how your team works and try something real with it. That is how this becomes useful.

This newsletter is for you, and we want to make it as valuable as possible. Please reply to this email with your questions, comments, or topics you'd like to see covered in future issues. Your input shapes our content!

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Until next Tuesday, keep balancing!

Anna Tiomina
AI-Powered CFO