We Killed the Spreadsheet. Here’s What We Built Instead.

Long-term planning sounds straightforward until you try it. You're not just predicting next year's revenue. You're building a 10-year forecast that spans more than 40 legal entities, each with over 100 cost centers, and hundreds of expense categories. Why so granular? Because that plan isn't just a finance artifact anymore. It's a dataset that tax, treasury, HR, and the executive team all rely on.

Tax needs to see the split between goods and services, by legal entity and jurisdiction. Treasury wants a cash view. HR needs headcount assumptions. Executives want to understand the strategic trade-offs between growth, margins, investment, and free cash flow. One model has to serve all of them.

So, like many companies, when our business outgrew our tools, we did what finance teams do: we built a massive Excel model. And over time, it became exactly what most large financial models become—a Frankenstein. New tabs piled on, formulas were patched together, and new logic stacked on old logic to keep up with the changing business. It worked, but it was getting harder to maintain, govern, and scale. Something had to give.

From Spreadsheet to Planning Platform

A little over a year ago, we rebuilt our long-term planning model on Snowflake, with Streamlit as the UI layer. That became Snowplan, our internal long-term planning application. The goal wasn't to build a dashboard. It was to build a real planning platform—one that feels familiar to finance users but has Snowflake's scalability, governance, and compute underneath.

In Snowplan, analysts update assumptions through an editable Streamlit interface. Those changes write straight back to Snowflake, the model runs, and the new outputs show up in the app immediately. No more broken formulas. No more saving files and emailing them around. No more guessing which version is the source of truth.

This architecture changed how we plan. Instead of maintaining a giant offline workbook, we have an app that connects to our actual business data sources, with governance built in. Actuals flow in automatically—no more spending hours updating files. Assumptions are versioned. Scenarios can be compared. And different users work in the same platform, at the level of detail they need.

• Individual contributors and associates get fine-grained input pages, assumption management, scenario creation, and version control.
• Directors and managers get visibility into logic and assumption changes, with review and approval workflows.
• Executives see the consolidated P&L, free cash flow, and key scenario views.

This matters because long-term planning is never just a modeling exercise. It's an organizational alignment process. The more time finance spends maintaining the model, the less time it has to actually think through strategy with the business.

Why Building the Model in Snowflake Changed Everything

The most important decision we made was to build the model where the data already lives. Because Snowplan runs on Snowflake, it's natively connected to our raw data sources and governed data models. That means no more manually updating actuals, no more reconciling offline data pulls. The model lives right next to the finance data, permissions, logic, and history it depends on.

This has several big advantages. First, the model can actually scale. A 10-year forecast spanning entities, cost centers, expense categories, headcount, revenue, balance sheet, and free cash flow produces a lot of data—exactly the kind of workload Snowflake is built for.

Second, it's easier to govern. We use Snowflake's role-based permissions and row-level security to control access, so different roles see only what they should see. Executives don't need the same interface as analysts, and analysts don't need to export different versions for every stakeholder.

Third, the same platform can support other planning workflows: headcount planning, equity modeling, treasury cash forecasting, hedging, legal entity forecasting, COGS planning, and M&A scenario analysis. That's the bigger story. Snowplan isn't a one-off planning app. It's becoming a financial planning platform.

How Snowflake CoCo Made Scenario Planning Conversational

Streamlit made Snowplan scalable and usable. Snowflake CoCo made it conversational.

Before CoCo, Snowplan gave us a better way to manage long-term planning. Analysts could update assumptions, run scenarios, and compare outputs. But users still had to know where to go and which assumption to tweak—and how to interpret the downstream impact of those changes.

CoCo changed that interaction model. Instead of clicking through page after page of assumptions, I can just ask a question in natural language. I can ask CoCo to compare two forecast versions and summarize the key drivers. I can ask what changed between the plan we showed the board last year and the one we're preparing now. I can ask about the net impact of those changes, the key drivers of margin expansion or dilution, and which assumptions deserve the most attention.

This is incredibly powerful in executive planning. Because when you're preparing for a board discussion, the real question isn't "Can you get me the latest forecast?" It's "What changed, why did it change, and how does that shift our narrative?" CoCo turns what used to be a manual, multi-hour analysis into a conversation.

The value isn't just speed—it's that finance can keep iterating while the strategic discussion is still happening.

A Real Example: Scenario Planning Around a Potential Tax Change

One of the best examples is scenario planning around a potential tax change. In the past, this kind of question started with meetings. We'd talk to the tax team, define the affected sales, pull data, build assumptions, update the model, review the outputs, create sensitivity tables—and only then decide who else needed to be involved.

With CoCo in Snowplan, the process is much smoother. I can ask CoCo to summarize the potential tax change. Then I can ask it to create a new forecast version assuming the change goes through. That immediately leads to the kind of back-and-forth we'd normally have in a meeting: Is this tax passed on to customers, or absorbed as a margin hit? What percentage can realistically be passed through? Which sales are affected? What's the impact on revenue, gross margin, operating margin, and free cash flow?

Because the analysis runs on Snowflake tables, CoCo can identify which sales are affected, output the financial impact, and show the key metrics behind those numbers. It can also build sensitivity tables showing how operating margin dilutes under different pass-through rates.

Just as important, it flags risks and caveats. For example, a first-order model might not include the extra indirect costs of supporting filings, maintaining compliance datasets, or meeting new reporting obligations. That kind of reminder is exactly what a good finance partner raises before anyone treats a scenario as a conclusion.

CoCo can even help draft next steps—like an email to tax colleagues summarizing the analysis, key assumptions, open questions, and decision points. The system isn't just giving us a number. It's helping us frame the problem, identify the right experts, and move the process forward.

At that point, it's not just AI-assisted modeling anymore. It's AI-assisted planning.

Why This Matters for Finance Teams

Finance teams are constantly asked to answer strategic questions faster than traditional planning cycles allow. What if we accelerate growth? What if we open a new office? What if cloud costs improve by 25 basis points? What if compensation inflation runs higher than expected? What if a tax or regulatory change affects some of our sales? What if we reallocate investment across functions?

These aren't hypotheticals. They're questions executives throw out in real time. The problem is that traditional planning tools and giant spreadsheet models weren't designed for that level of iteration. They were built to produce a plan, not to support an ongoing strategic conversation.

By building Snowplan on Snowflake with Streamlit, we created a planning platform that scales with business complexity. Adding CoCo made it conversational. This combination changes what finance can actually do. Instead of spending time updating actuals, maintaining formulas, reconciling scenarios, or manually comparing versions, finance can focus on what really matters: challenging assumptions, aligning executives, evaluating trade-offs, and shaping long-term strategy.

Why Trust Is So Important in AI-Driven Planning

For finance, conversational planning only works if the numbers are trustworthy. That's why architecture matters. CoCo isn't generating a forecast out of thin air, disconnected from business context. It's interacting with the same governed data, assumptions, and logic that power Snowplan. When it compares versions, explains drivers, or creates scenarios, it's relying on the Snowflake data models and planning logic we already use.

Every scenario is versioned. Every change can be reviewed. Access control follows the app's existing role model. Analysts and executives can compare before and after, understand what changed, and roll a scenario forward or back as needed.

This is a critical distinction. We're not asking financial leaders to trust a black box. We're using AI to operate a well-governed planning platform where data, business logic, permissions, and outputs are visible, explainable, and auditable.

From Planning Tool to Strategic Platform

The most exciting part of Snowplan is that it's already outgrown its original use case. Once we moved the model to Snowflake, the architecture became reusable. The same foundation now supports—or can support—multiple financial planning workflows: headcount planning, equity modeling, treasury cash forecasting, hedging, legal entity forecasting, COGS planning, and M&A scenario modeling.

That's the payoff of building a platform instead of a one-off app. Each new planning workflow reuses the same governance foundation, connects to the relevant data sources, and exposes a finance-friendly Streamlit interface. And with CoCo, each one is easier to query, adjust, and explain in natural language.

I think more finance teams will follow a similar path: first, move the model to where the data lives. Second, build an intuitive app layer for users. Third, use AI to make the planning process conversational.

The Real ROI: Time Back for Judgment

The real ROI of Snowplan isn't that finance becomes more technical. It's that we get more time for judgment. Long-term planning shouldn't be about maintaining a giant workbook. It should be about helping the company understand where it's going, helping leaders decide where to invest, how to balance growth and profitability, what risks are emerging, and which trade-offs matter most.

Snowflake and Streamlit gave us a platform that makes long-term planning scalable, governable, and connected to real-time data. CoCo is helping us make it faster, more interactive, and more strategic. That's the shift. Finance can spend less time updating models and tweaking assumptions manually, and more time iterating with the executive team on the company's long-term strategy.

For FP&A teams, that's the real value of AI in planning. It's not replacing finance. It's removing the manual work that slows finance down—and letting finance do what it's supposed to do.