How AI and Automation Are Reshaping Record-to-Report

As digital transformation continues to sweep through finance and accounting, the record-to-report (R2R) process is undergoing rapid change. To understand how companies are navigating this evolution and where AI fits in, we sat down with Michael Gilmartin, BlackLine’s VP of Financial Close Solutions, to discuss emerging trends, challenges, and opportunities in the space.

Some key topics touched on include:

1. Finance leaders are excited about AI, but many don't know how it will help them and their teams yet

2. AI and traditional automation each have their place in F&A processes, and it’s important to understand this to maximize efficiency

3. Leaders should focus on establishing a foundational approach to enable the use of AI, including getting data in order.

Q: What are some trends you're seeing that are starting to shape the R2R process in this digital era?

A: Technology is obviously having a major impact. Everyone is talking about AI, and while it's being considered in a lot of areas, I’d say it hasn’t quite transformed the R2R process … yet. Companies are still trying to figure out how to scale and apply AI within their organization. The technology is available, but most teams are unsure how to leverage it effectively or how to integrate what their vendors are offering.

We’re just now seeing prior generations of technology, like solutions that go beyond Excel spreadsheets, become the norm. Over the last couple of years, adopting solutions like BlackLine has become best practice rather than the exception. More companies are making those decisions. But when it comes to AI, I think it's still having more impact on the market conversation than the process itself.

Q: Are you seeing any resistance to AI and automation, or are most people just curious at this point?

A: It’s mostly curiosity. A lot of people I talk to still haven’t used tools like ChatGPT. They’ve heard of AI and feel they should be asking about it, but they don’t fully understand what’s available or how it can be applied. So yes, they’re asking their vendors, us included, about it, but there’s a gap in hands-on experience.

Q: So they’re leaning on vendors like BlackLine to guide them?

A: Exactly. Over the past couple of years, there’s been a broad recognition that companies need to adopt technology in the R2R space. Then, when AI started getting more attention, especially early last year, it introduced a lot of confusion. Some companies actually paused their transformation efforts to figure out what AI meant for them.

Now, I think we’re reaching a point of stabilization. Organizations understand that while AI has promise, it will take time to embed and scale it effectively. In the meantime, they’re looking to solutions like BlackLine, especially those with AI already embedded, to help lead the way.

Q: Are they looking for specific benefits like improved speed, accuracy, or control?

A: They usually frame it in terms of automation. But often, they’re asking about applying AI in the wrong places. One common example is using AI for matching logic. It’s the same default use case people asked about when RPA and machine learning were gaining traction.

But matching is typically based on a stable set of rules and consistent data—AI doesn’t add much value there. It’s not the best use case.

Journal entry automation is another one. People think AI might help there, but again, if you already have a rule set and clean data, that can be handled through traditional automation. AI doesn't add much to that process.

Q: Interesting. Are there other areas where AI is not well-suited, and others where it is?

A: Where AI does shine is in areas that require heavy lifting, where it needs to evaluate complex patterns or anomalies that a human couldn’t detect, even with Excel. Our Journals Risk Analyser (JRA) solution is a good example. It analyzes historical journal entries, identifies risky patterns, and flags anomalies based on combinations of values that are statistically unusual.

For example, it might detect that a specific company code and cost center combination has only appeared three times in a million entries. And it doesn’t just flag it. It explains why it might be risky and gives you a confidence score for that reasoning. It’s doing the kind of analysis that simply wasn’t feasible or possible before.

Q: That sounds like a strong scalability use case. Is this how BlackLine helps customers scale, by doing things that humans simply can’t?

A: Absolutely. With JRA, you're dealing with large volumes of data and complex combinations of fields. AI can surface unusual patterns instantly, which would take humans hours or days, if they even knew to look for them. It’s not just flagging anomalies; it’s providing actionable insights in seconds.

Q: So once something is flagged, a human steps in to analyze further?

A: Yes. The system flags the item and gives potential explanations. It also guides the user on how to investigate further. That’s where the human adds value, by making decisions based on the insight the AI provides.

Q: Are there any success stories that come to mind?

A: I’ve worked with hundreds of customers, and I’ve been one myself, so I’ve seen both successes and failures. Success doesn’t hinge on industry or company size. It comes down to internal leadership and how committed the organization is to change.

The companies that succeed with BlackLine take a top-down, strategic approach. They use the full platform, align it with their broader objectives, and commit to continuous improvement. On the other hand, those who treat it as a point solution or fail to align it with strategy tend to struggle.

Q: That aligns with our shift toward end-to-end solutions. It sounds like AI helps drive that holistic view by taking care of the data processing, so teams can focus on analysis.

A: Exactly. It moves teams closer to that “Nirvana” where accountants are reviewing exceptions and making informed decisions. The AI and automation handle the noise, so people can do what they’re really good at.

In the manual world, teams spend too much time just getting to the point where they can do their job. We’re removing that friction so they can actually spend time on their core responsibilities, like exception handling and deeper analysis. And yes, it frees up time for other strategic tasks that add value to the business.

Q: What advice would you give a CAO or CFO who’s exploring AI and automation?

A: That’s a great question, and there are a few layers to it.

I often have conversations with controllers, CAOs, or CFOs who are eager to leverage AI as much as possible. But many of them haven’t even adopted a foundational solution like BlackLine yet. They're still working in a manual, spreadsheet-driven world. And now they’re trying to jump straight to AI.

The issue isn’t that the technology isn’t available—it’s been available for years. The real barrier is adoption. For one reason or another, organizations haven’t taken the steps to modernize their core processes. So when we’re advising them, we try to bring the conversation back to reality: yes, advanced technologies like AI are exciting, but you have to start with the basics.

You need a solid foundation to enable the use of AI. That means getting your data in order. Many companies realized this when AI became a hot topic. They saw the potential, but also realized their data wasn’t clean or structured enough to support it.

That’s where solutions like BlackLine come in. Tools that focus on data integrity, harmonization, and centralization become even more critical. Because even the most advanced AI can’t produce good outcomes from bad data.

Before you can leverage AI effectively, you need to implement strong governance models and systems that ensure consistency and accuracy in your data. Only then can AI deliver meaningful results.

Q: So it’s kind of like painting a room—you can’t just start rolling on paint. You need to prep the space: clean the walls, lay down drop cloths, apply primer, and tape the edges.

A: Exactly. AI is like the final coat of paint. It can make things look amazing, but only if you’ve done the prep work. For finance leaders, the key question isn’t just, “How do we use AI?” It’s, “Is our data structured and clean enough to support the processes we want to enhance with AI?” That’s where the journey really begins.