The Missing Opportunity in Using Copilot or Claude in Finance and FP&A

About a year into the AI journey, almost all companies and finance teams are using some form of AI capability. Most are using Copilot or Claude Desktop, given how easily they integrate with Excel. And while these are great tools and certainly provide benefits to finance, the current approach is just the tip of the iceberg: it misses most of the benefits and value-add that AI can deliver.

Pretty much all the use cases with these tools involve the same approach: loading data from an XLS file and having the AI generate financials, a summary, a projection, a dashboard, report or presentation. Access to these capabilities is easy given the XLS add-in and general integration into the MSFT stack (PPT mostly).

I have nothing against Copilot or Claude Desktop, both are very useful and powerful tools that deliver many benefits. However, the current use cases run into 2 issues that keep users from capturing the real benefits that AI can provide:

1. Data Availability

The typical use case assumes that data already exists in XLS form, ready to upload to AI. But where does that data come from? In most companies, the data sits in a reasonably complex finance infrastructure that may include one or multiple databases, often a combination of multi-dimensional and relational data stores as part of the EPM infrastructure. There are data loads, mappings, calculations, business rules etc. Getting this data into a clean XLS file is not a trivial task. It often requires a lot of manual work: sprawling XLS files with vlookups, pivot tables—which is exactly where all the efficiency, risk and control challenges are. Unless we automate this part of the process— the access and retrieval of data— any benefit we gain from AI will remain marginal. We will fail to capture the real opportunity in the long run.

2. Data Complexity

The second issue is one of depth: there is only so much detail a standard XLS file can hold. Spreadsheets will work fine for simple modeling and reporting. But the real meat and opportunity in FP&A isn’t the “WHAT”— it’s understanding and explaining the “WHY”. We need to understand the drivers of performance and the causes of variances, not just report what happened. And to do that effectively, we need to slice and dice the data along available dimensions and attributes, understand the rules or calculations (drivers of cost allocations, for example, or changes in HC, comp and benefits to understand impact on compensation). Often, we need to drill down to transaction-level detail to truly uncover the root causes and get appropriate insight. This requires access to data that is well beyond the scope of a standard XLS file. Furthermore, the standard XLS file is static; it won't account for the fact that the drivers of performance shift with changing market conditions, so the data we need changes month to month. Without AI that can understand and access data in our entire finance ecosystem, pulling the relevant data points as needed, any upside we gain from AI will be very limited.

These are the reasons and drivers through which created whyFA.ai. Unlike other multi-purpose AI tools and agents, it connects to your finance infrastructure and is able to access data in a network of data stores. It is able to understand the information flow and logic, including data mappings, calculations, rules etc. By accessing data where it actually resides, the agent is able to tap into that rich information and provide deep and meaningful insight into the drivers of business performance, far beyond what an XLS file uploaded to an AI tool of your choice can contain.