How Airlines Can Use AI to Turn Long-Term Planning into a Conversation

The Planning Problem Every Airline Knows

Long-term planning is a beast for airline finance teams. It's not just one department's headache; it spans dozens of routes, hundreds of cost centers, and thousands of expense categories. A ten-year forecast has to juggle fleet purchases, fuel price swings, labor contracts, route profitability, and regulatory changes—all at once. And it's not just about getting the numbers right; it's about getting them timely and explainable.

At Snowflake, we hit the same wall, though in a different industry. We needed a ten-year forecast covering 40-plus entities, each with over 100 cost centers and hundreds of expense categories. The model wasn't just for finance anymore. Tax wanted a breakdown by goods vs. services, legal entity, and jurisdiction. Treasury wanted cash flow. Workforce planning wanted headcount assumptions. Executives wanted to see trade-offs between growth, margins, investment, and free cash flow.

Our first response? A giant Excel model. You know how that goes—tabs multiply, formulas get patched, logic layers on logic. It worked, but it was a nightmare to maintain, govern, and scale. We were spending more time fighting the spreadsheet than actually planning.

From Spreadsheet to Planning Platform

About a year ago, we rebuilt our long-term planning model on Snowflake, using Streamlit as the user interface. We called it Snowplan. It wasn't a dashboard—it was a real planning platform. Analysts could update assumptions in an editable interface, and those changes would write directly back to Snowflake, where the model ran and returned new outputs instantly. No more broken formulas, no more file version confusion, no more guessing which spreadsheet was the source of truth.

That architecture flipped the whole planning process. Instead of maintaining a huge offline workbook, we had an app connected to our actual data sources, with governance built in. Actuals flowed into the model automatically, saving hours of manual updates. Assumptions were versioned, scenarios could be compared, and different roles worked at their own level of detail in the same system.

Individual contributors got fine-grained input pages and scenario creation. Directors and managers could see changes to logic and assumptions, then approve or reject. Executives saw consolidated P&L, free cash flow, and key scenarios. Long-term planning is never just a modeling exercise—it's about aligning the organization. The less time finance spends maintaining the model, the more time they have to run strategy with the business.

Why Building the Model in Snowflake Changed Everything

The most important decision was to put the model where the data already lived. Because Snowplan runs on Snowflake, it connects naturally to raw data sources and governed data models. We stopped manually updating actuals and coordinating offline data pulls. The model sat right next to the finance data, permissions, and history.

That gave us three big wins. First, the model could truly scale. A ten-year forecast spanning entities, cost centers, expense categories, headcount, revenue, balance sheet, and free cash flow generates a lot of data—exactly what Snowflake handles best. Second, governance became easier. Role-based permissions and row-level controls meant each role saw only what they should. Executives didn't need the same interface as analysts, and analysts didn't need to export separate versions for every stakeholder. Third, the same platform could support other planning workflows: headcount planning, equity incentive modeling, treasury cash forecasting, hedging, legal entity projections, cost of goods sold planning, and M&A scenario analysis.

That's the bigger story. Snowplan wasn't a one-off planning app—it was becoming a financial planning platform.

CoCo Turns Scenario Planning into a Conversation

Streamlit made Snowplan scalable and usable. Snowflake's CoCo made it conversational. Before CoCo, users still had to know where to go, which assumption to adjust, and how changes would ripple downstream. CoCo changed that.

Now, instead of clicking through pages of assumptions, I can ask in natural language: Compare these two forecast versions and summarize the main drivers. What changed between the plan we showed the board last year and the one we're preparing now? What's the net impact, what's driving margin expansion or dilution, and which assumptions should we watch closely?

That's powerful in executive planning. When preparing for a board discussion, the real question isn't "Can you give me the latest numbers?" It's "What changed, why, and what does that mean for our narrative?" CoCo compresses what used to be manual analysis into a conversation. And it lets finance iterate in real time while the strategy discussion is still happening.

A Real Example: Scenario Planning Around a Tax Change

One of the clearest examples is scenario planning around a potential tax change. In the past, we'd start with a meeting. We'd discuss the issue with the tax team, define affected sales, pull data, build assumptions, update the model, review outputs, run sensitivity tables, and then decide who else needed to be involved.

With CoCo in Snowplan, the process became much smoother. I could ask CoCo to summarize the potential tax change. Then I could ask it to create a new forecast version assuming the change goes through. That immediately led to the kind of back-and-forth you'd have in a finance meeting: Is this tax passed on to customers, or absorbed as a margin hit? What percentage can realistically be passed on? Which sales are affected? How does it impact 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 drivers behind the numbers. It can create sensitivity tables showing how operating margin dilutes under different pass-through percentages.

Just as important, it can flag risks and caveats. For instance, a first-order model might not include the extra indirect costs of supporting compliance or new reporting obligations. That's the kind of question a good finance partner raises before treating a scenario as a conclusion.

CoCo can even help generate next steps—like drafting an email to the tax team summarizing the analysis, key assumptions, open questions, and decision points. The system isn't just giving a number; it's helping frame the problem, identify experts, and push the process forward. That's AI-assisted planning, not just AI-assisted modeling.

Why This Matters for Airline Finance Teams

Finance teams are often asked to answer strategic questions faster than traditional planning processes allow. What if we accelerate growth? What if we open a new hub? What if fuel costs improve by 25 basis points? What if wage inflation runs higher than expected? What if a tax or regulatory change hits certain routes? What if we reallocate investment across functions?

These aren't theoretical—executives ask them in real time. Traditional planning tools and massive spreadsheets weren't designed for that level of iteration. They're built to produce a plan, not to support an ongoing strategic conversation.

By building Snowplan with Streamlit on Snowflake, 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, coordinating scenarios, or manually comparing versions, finance teams can focus on the real work: challenging assumptions, aligning executives, evaluating trade-offs, and shaping long-term strategy.

Trust Is the Foundation for AI-Driven Planning

For finance teams, conversational planning only works if the numbers are trustworthy. That's why architecture matters. CoCo isn't generating predictions in a vacuum—it interacts with the same governed data, assumptions, and logic that power Snowplan. When it compares versions or explains drivers, it relies on the data models and planning logic we already use.

Every scenario is versioned, every change is reviewable, and access controls follow the same role model. Analysts and executives can compare before and after, understand what changed, and roll forward or back as needed. The key difference: we're not asking leaders to trust a black box. We're using AI to operate a governed planning platform where data, logic, permissions, and outputs are visible, explainable, and auditable.

From Planning Tool to Strategic Platform

Snowplan has already outgrown its original use case. Once the model moved to Snowflake, the architecture became reusable. The same foundation now supports—or can support—multiple planning workflows: headcount, equity incentives, treasury cash forecasting, hedging, legal entity projections, COGS planning, and M&A scenarios. That's the advantage of building a platform, not a one-off app.

Each new workflow reuses the same governance foundation, connects to relevant data sources, and exposes a finance-friendly interface. With CoCo, every process becomes easier to query, adjust, and explain in natural language.

I believe 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 planning conversational.

The Real ROI: More Time for Judgment

The real ROI of Snowplan isn't making finance teams more technical—it's giving time back to judgment. Long-term planning shouldn't be about maintaining a giant workbook. It should help teams understand where the business is heading, where to invest, how to balance growth and profitability, which risks are emerging, and which trade-offs matter.

Snowflake and Streamlit gave us a platform that makes long-term planning scalable, governable, and connected. CoCo makes it faster, more interactive, and more strategic. Finance teams can spend less time updating models and adjusting assumptions, and more time iterating with executives on long-term strategy.

For FP&A teams in airlines and beyond, that's the real value of AI in planning. It doesn't replace finance—it removes the manual labor that slows them down, so they can focus on what they should be doing.