AI in finance has an 80% problem

The first time you use AI in FP&A, it feels like magic.

Upload a couple CSVs to Claude or ChatGPT, then ask for a BvA, board summary, or dashboard. A minute later, you have something that looks genuinely impressive. You’ve gotten 80% of the way there in a fraction of the time it used to take.

But in finance, that last 20% is everything.

Do the numbers tie? Did the mappings and definitions hold across systems? Can you trust the process will work again next month?

This is the state of AI in FP&A in 2026: the models are powerful, the prototypes are impressive, and the gap to production comes down to an auditable, context-aware data layer.

Here’s what that means.

Models are getting better, but finance teams need certainty

LLMs are probabilistic by nature, meaning they make their best guess at what the user is looking for. That’s a big part of what makes them so powerful. Ask for a meeting summary, and they can read through the transcript, identify key themes, and surface the takeaways you’re most likely to care about. It may not be perfect, but it’s accurate enough to be useful.

“Accurate enough to be usable” is a high bar in finance, though. Something like 99%+. Every number in your board deck needs to be defensible and traceable to its source of truth. In other words, it needs to be deterministic—the same inputs need to generate the same answer, every time.

That’s why data and context matter so much. In their recent Agents for financial services announcement, Anthropic pointed out that "AI agents are only as good as the data and context they can access."

To produce work you can trust, AI tools need to be rooted in your actuals, forecasts, mappings, definitions, permissions, business logic, and source-system data.

How do I give AI models my business context safely and securely?

One new way to give AI models context is MCPs. Instead of exporting CSVs and uploading them to a chat window, MCPs let models query live systems directly.

MCPs are a meaningful step forward—we just released our own. They’ve also made the “vibe code your own FP&A tool” path start to look tempting. Just upload your spreadsheet models to Claude, hook it up to the Netsuite MCP, and keep iterating until you have something that works.

For one-off analyses, that might be good enough. For production-grade FP&A, it’s not quite there.

A NetSuite MCP helps Claude access NetSuite. A Salesforce MCP helps Claude access Salesforce. But those systems don’t share mappings, definitions, or business logic. Financial data might live in NetSuite, sales data might live in Salesforce, and both systems might be talking about the same booking, revenue stream, or expense category in different ways.

You end up with multiple versions of the truth, which makes working with cross-system data far more difficult.

Aleph connects the dots across systems, creating a common language that finance teams and AI tools can both rely on.

Without that layer, the scattered data problem finance teams have dealt with for years becomes a scattered MCP problem. The LLM has access to more systems, but it’s still unaware of how those systems map together, which permissions apply, and what logic finance uses to make the numbers meaningful.

Could Claude help reconcile some of that itself? Yes…to a certain extent. The more steps a model has to take to clean, map, normalize, and structure your data, the more room there is for small hallucinations (read: errors) to compound. And because the model doesn’t have its own finance database, that context has to live somewhere else: a markdown file, a set of one-off instructions, or a patchwork of configs someone has to maintain.

Instead of worrying about all of that, finance teams can use their favorite AI tools to build, analyze, and automate on top of live, governed data via Aleph without rebuilding the context layer themselves.

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Your stack should get better as the models get better

AI isn’t going anywhere. And despite decades of predictions to the contrary, neither are spreadsheets. In fact, they’re stronger than ever, with the world’s biggest AI companies investing billions into spreadsheet add-ins and integrations.

That’s exciting for us because it complements what we’ve been building. As Claude and ChatGPT get better, Aleph gets better: Claude for Excel gets more useful when it can reference live Aleph data instead of hardcoded exports. Aleph gets more powerful as the models get better at spreadsheet work, analysis, and workflow automation.

The most critical ingredient, though, is trust. Any dashboard, forecast, or board summary AI helps create needs to be verifiable. Teams need to be able to drill into every number, trace it back to the source of truth, and know a workflow will reliably produce the same result next month.

That kind of repeatability is what any production-grade FP&A system demands. Aleph gives finance teams a way to harness the latest AI models while keeping the outputs governed, traceable, and repeatable.

Build an AI-powered finance function on a solid foundation

It’s an exciting time to be a finance pro. AI is showing up the tools teams already use, making everyday FP&A work faster, smarter, and more flexible. This generation of finance teams will be the most valuable in history.

But the best version of that future is not AI floating above the business with no grounding in the numbers. It’s AI connected to the data, definitions, permissions, and workflows finance already trusts.

That’s what Aleph is built for: the governed finance context underneath the AI tools your team already uses.

Claude, ChatGPT, Gemini, and the next wave of AI tools bring the intelligence. Aleph brings the live data layer, mappings, audit trails, and traceability that make the output usable in real FP&A work. Together, they give finance teams more leverage without giving up trust.

Maximize the upside of AI in finance while minimizing your risk. Schedule a 1:1 demo today.