The CFO’s guide to AI transformation for finance: from pilot to scale

In my work at Drivetrain, I have the privilege of talking to many CFOs from a lot of different industries. This has given me a front-row seat to see how some of the best CFOs in the business operate today. And it’s also given me a unique opportunity to learn about their top-of-mind concerns.

Lately, the topic of AI transformation has come up a lot in these conversations. So, I thought I’d share some of my observations and ideas about the topic here.  

Let me start by saying that while a lot of CFOs are working hard to achieve AI transformation, many are still struggling to make AI work for their organizations.

That's because AI is different. Really different. 

Of course, this isn’t the first time technology has disrupted our industry. First, there were paper ledgers, then there was Excel, and then the cloud came along, ushering in a whole new category of technology—financial planning and analysis (FP&A) software. With each of these changes, finance teams had to learn how to do the same job in different ways. 

But AI? It’s more than just upgrading systems. AI is actually rewriting the role of finance itself.  

Of the CFOs I’ve talked to, the ones who have embraced the work that AI transformation requires are able to replace complexity with clarity and hindsight with foresight when meeting with their boards. And their board members are taking notice. They’re quickly realizing that AI-empowered CFOs are indispensable in strategic decision-making.

Most of the CFOs I talk to are pretty excited about the possibilities that AI transformation for finance can offer, both for their careers and the organizations they work for. But a lot of them aren’t sure where to start. That’s what I want to talk about here. 

But first, we need a vision… 

What does true AI transformation for finance look like?

AI transformation is real when it streamlines your processes and delivers decision-ready answers grounded in your company’s reality. AI tools like Drivetrain that use model context protocol (MCP) can help with this. 

MCP is a technical standard that gives large language models (LLMs) like Claude and ChatGPT secure access to your business’s contextual information. It enables the LLM to maintain a persistent understanding of your data and business rules instead of treating each prompt as a new request.  

Here’s what it actually looks like, once you get it set up within the AI tool:

• End-to-end automation: The AI pulls data from the sources you’ve defined, cleans it, compares it to your targets or budgets, and automatically notifies the right people in your organization of the results.
• Real-time alerts: The AI system also continuously monitors your data and flags issues in real time. It will detect any gaps or outliers in the data automatically and instantly alert the appropriate stakeholders to intervene.
• Querying your data with simple English prompts: Whenever you or your finance team has questions about your data, you can ask the AI using plain English prompts and get accurate, structured answers without any SQL queries or spreadsheets.
• Complex data transformations: Similar to asking the AI questions, you can also give the AI instructions on how you need your data to be formatted, and in seconds, your data is formatted in a way that would otherwise have required manual changes.  
• Workflow orchestration using tools: Using tools like n8n, Zapier, or Gumloop allows your team to automate various processes and free up time to focus on strategic work. For example, you could configure the AI system so that every Monday morning, it pulls pipeline data from your CRM, reads targets from a Google Sheet, runs variance analysis, summarizes the story, and posts it on Slack for sales and finance leaders.

What are the common issues holding finance teams back from achieving AI transformation?

While all of these things are possible today, many finance teams trying to achieve this level of automation and intelligence are still stuck at the pilot stage.

Here are some of the challenges CFOs have shared with me.

You need a usable data foundation to build on

Pilots often succeed in controlled environments where there’s a single dataset and a single clean use case. But when you try to expand the scope, you start seeing cracks. 

Your data lives in ERPs, CRMs, and even spreadsheets in some cases, often with inconsistent definitions and weak ownership. This forces AI to spend more effort on reconciling numbers than on generating insight. 

So, in order to start your AI transformation, data is the first thing you’ll need to look at. Fortunately, it doesn’t have to be perfect to get started. I’ll explain how to approach this problem in more detail below. 

Governance frameworks provide necessary guardrails

Many teams lack enough knowledge on how to approve AI for real use. Your team needs to be able to answer questions like:

• What needs review?
• What can run automatically?
• Who’s accountable if something goes wrong?

Define these rules to build your AI governance framework. To scale AI transformation in finance, you need to keep your framework lightweight and practical. Proactively eliminate all unnecessary bureaucracy from your processes.

Getting buy-in requires commitment and consistency 

If your leadership isn’t keen on your AI goals, your pilots are likely to remain underfunded. Your team won’t feel the push required to redesign workflows, incentives won’t change for anyone to align with AI goals, and adoption will remain lackluster. 

To achieve any degree of success, finance leaders need to actively sponsor AI as a productivity and decision-making tool rather than just another tech experiment.

Successful AI transformation begins with curiosity

This is presently one of the biggest constraints for AI transformation in finance. I often see teams using AI the same way they used Excel in 2005. 

They ask it to recreate the same reports and variance tables. But they fail to ask how AI can help transform existing processes and squeeze out manual effort.

Without this curiosity, AI gets boxed into your old workflows. That’s why you should encourage your teams to never wait to be told what’s possible and instead spend time testing, breaking, and iterating workflows.

Core strategies to support a successful AI transformation for finance

I consider the following two strategies essential to any successful AI transformation for finance.

Use quick wins to build momentum

Quick wins provide a solid foundation and give your team that initial morale boost to get them through this change. Here’s what you can do to score a few:

• Build a prompt pack for your team: Standardize prompts to automate the most frequent tasks. This could be a prompt to check expenses against company policy or review budgets against historical data to identify overspending and unrealistic assumptions. The prompt pack will quickly show your team the value AI brings to the table. Download our ready-to-use prompt pack for CFOs to get started today. 
• Experiment with familiar technologies first: If you show your team a complex workflow with multiple triggers, they’ll feel overwhelmed. Start your AI transformation for finance with technologies that your team may have already used or can get used to pretty quickly, such as ChatGPT, Claude, and Gemini. 
• Automate simpler workflows: AI tools like n8n, Zapier, and Gumloop can help you automate those simple, annoying processes, like pulling the latest FX rates from a reliable source and updating them in your budgeting and forecasting tool.
• Use AI copilots and agents already available in your existing tools: Many modern FP&A platforms and finance tools ship with copilots and agents. Start there. When AI is available where your team already works, adoption is faster, and experimentation is easier.

Foster a culture of curiosity and experimentation

In Drivetrain’s 2025 report on the state of AI in FP&A, we found that while finance teams are experimenting with AI, they’re doing it informally, mostly out of curiosity. More than half of those we surveyed (53%) said they’d spent less than five hours building their AI skills in the past month. This is a problem because upskilling is critical for AI transformation. 

It’s important to not only allow your team to experiment. You need to encourage it. When they aren’t afraid of “doing it wrong,” they’ll take more initiative. Start by giving them clear problems to tackle and skip any rigid instructions. Then measure simple outcomes early—these may include hours saved or clearer variance explanations.

Let these small, visible wins stack up for a while. This will help justify the expense of the tools you’re using and at the same time, change your team’s perception of AI. Instead of worrying about their jobs being replaced by AI, your team will experience its advantages firsthand—how it can help them do their jobs better. Over time, this snowballs and improves buy-in organically.

Where should CFOs start on AI transformation?

We’re through the basics. Now, let me walk you through the core of your AI transformation process. Here’s a four-step process you can follow.

Step 1: Get your data ready

Data is food for your AI tools. If you feed it junk, it will just bloat the algorithm and produce poor output. That’s why the first step in our process is to build the right data foundation.

Start by unifying data across finance, sales, HR, ops, and any other systems you use. This gives AI complete and non-conflicting data to operate on. While you don’t need a perfect data lake on day one, you do need a clear source of truth that agents and MCP-style workflows can reliably pull data from.

The key here is pace. Fix data incrementally as new use cases emerge instead of going for one major overhaul. Every AI workflow you deploy should leave the data slightly better than it found it. This way, you’ll be able to scale data readiness without turning it into a multi-year program or instant overwhelm.

Step 2: Evaluate the right tools

There’s more to look for than fancy features. No doubt those features are important, but so is ease of use. Prioritize platforms that:

• Plug into your existing workflows
• Integrate with your current tech stack
• Don’t require excessive training

Anything that requires extensive effort or overhaul makes adoption harder. If you’ve already made an attempt to automate FP&A processes with AI and failed, it’s quite possible that complexity was the reason for failure.

Your best bet is to focus on tools that align with your existing infrastructure. Avoid choosing a platform that requires a complete overhaul of processes just to accommodate it.

Step 3: Empower your team

Don’t limit your AI transformation’s scope as an IT initiative. The challenges with this transformation are as much a people problem as they are a technology problem. It pays off to invest in building your team’s confidence and AI literacy as you work on this transformation. 

Before you invest in any platforms, lay out a plan for how your team will gain momentum once it’s deployed. This could be training on prompting skills or educating the team about emerging AI tools and agent frameworks.

The platform you choose can also give your efforts a tailwind. Take Drivetrain, for example. We make deliberate enablement efforts from our end with bi-monthly AI-for-all hackathons, weekly demo sessions where anyone can show what they’ve built, and hands-on masterclasses with external experts focused specifically on finance workflows.

Step 4: Measure and evangelize ROI

Measuring the success of AI finance projects is more than just reporting. It’s also important to evaluate AI in finance for real value—building the case for funding future AI initiatives and, at the same time, driving leadership buy-in. That’s why tracking ROI is non-negotiable whenever you implement an AI platform in any capacity, even during the pilot phase.

How to evaluate AI in finance for value delivery? 

Here’s a solid approach for evaluating and demonstrating the value of AI in finance:

1. Track time saved per cycle, accuracy improved, faster turnaround on insights, better decision quality. Then share your results with stakeholders. 
2. Openly celebrate wins to make your team feel validated and prove to your leadership that AI can drive great results with the correct implementation approach.

True AI transformation for finance is about achieving  steady, structured progress

Going full-throttle on your approach from day one makes it far more likely that you’ll stall out at the pilot stages like so many other enterprise companies today.

True AI transformation is the result of a well-conceived approach and consistent ongoing effort. By changing one workflow at a time and focusing on clean data, powerful tools, people enablement, and quick wins, you’ll see a slow but steady, successful transformation. 

Once you’ve laid a solid data foundation and your team feels confident about using AI, it’s time to start experimenting with more advanced technologies. Here are a few to consider:

Model context protocol (MCP) 

The core concept behind MCP is that it provides a common language (the rules and format) for AI tools to communicate with your data. To use MCP, you install an MCP server on your computer—an application for whatever you want the AI to access.

Once installed, instead of answering questions based on a single file you’ve uploaded to your AI tool, the MCP can use all the data files you’ve given it access to—models, documents, definitions, assumptions, and other data. This additional context allows AI tools like Claude and ChatGPT to generate more effective responses. 

Currently, Claude Desktop supports MCP out of the box. Other AI tools may require more technical workarounds, but there are plenty of tutorials online to walk you through the basic process. 

Claude artifacts

Claude Artifacts are outputs generated when you want Claude to build something, instead of just requesting a response to a question. 

A normal response from Claude would involve AI-generated text, but with Artifacts, you get a working output, which could be a table, a structured analysis, a small calculator, or a draft report. 

Your team can use Artifacts to create a first usable draft of a model or tool, then edit and validate it before final use, effectively reducing the total time required for a given task.

Vibe coding

Vibe coding is AI-assisted software development. Platforms like Lovable, Replit, and Emergent can help you build lightweight tools using chat-style prompts. Just explain what you want to build, and the tools get to work. 

If you want to build custom apps like transaction classifiers or data cleaners, Lovable is the way to go, but there are other tools you can explore if you want to.

Agent frameworks

Tools like Langchain and Vertex AI let you build AI agents that can follow a sequence of steps on their own instead of answering one question at a time. 

You can configure the AI agent to pull data from a specific source, apply logic, check results, and then take the next action. 

Here are some use cases where your team might want to apply an agent framework:

• A close assistant who pulls GL data and runs variance checks
• A contract reviewer that scans agreements and flags risky clauses
• A forecasting agent that refreshes assumptions automatically when new data comes in

A simple flywheel to scale AI across finance teams

If all of this seems like a lot to take in, just start with this simplified roadmap:

• Start with one workflow: What’s one manual workflow that you or your team truly hate? That’s where you start. Reimagine how you can use AI to automate, simplify, or accelerate that workflow.
• Measure ROI: Run that workflow for a quarter and review your ROI. This could be in terms of time gained back, improved forecast accuracy, faster decision-making, or something else, depending on which workflow you automated.
• Share and scale: If your efforts yielded remarkable results, make them known. Popularize your success with AI by sharing the results with your team and leadership. This momentum builds confidence and helps get buy-in for your next AI experiment.

Drivetrain can help

If you’re ready to start your AI transformation now, I encourage you to take a look at Drivetrain. Our customers have access to a pretty amazing suite of native AI capabilities that are easy to use and provide a lot of value fast. You can learn more about these in the video below, but here’s a quick rundown of what you can do with Drive AI:

• Explore your data more deeply with the AI Analyst. Just type in your question and get an instant response.
• Generate baseline models from your ERP, CRM, HRIS data in one click with our AI modeling tool, then customize them any way you need. 
• Use the AI Transforms tool instead of spending time reformatting your data to fit your model. Just tell the AI what you want and get model-ready data in seconds. 
• You can stop worrying about your data pipeline, too. Our anomaly detection system continuously monitors your data and metrics and sends instant alerts when it detects an issue. 

We also offer an MCP server—the first one of its kind built specifically for finance. This allows you to interact with all your data in Drivetrain to provide robust, reliable responses to your queries in real time.

If you want to learn more about how Drivetrain can help you get (and stay) on the leading edge of AI in finance, book a demo with us today.