Table of Contents
Key takeaways
- At Avalara, finance is expanding its role from month-end close and compliance into a more strategic partnership with business leaders.
- In a complex business, planning needs to move beyond static spreadsheets to make scenario analysis faster and more flexible.
- Avalara used AI for about 90% of its initial build, giving the finance team a view of the platform before user acceptance testing (UAT).
- Seeing the model take shape helped the team challenge legacy processes and focus on the drivers behind key business decisions.
- For Raymond Yue, SVP of Finance, the most valuable benefit of planning transformation is spending less time fighting data and more time analyzing scenarios with business partners.
- AI can strengthen finance analysis, but human experience and judgment still determine the right call.
Avalara’s finance team plans for a business shaped by changing tax regulations, a growing mix of products, and different go-to-market motions. Those variables affect how the company thinks about performance, where it sees risk, and the decisions leaders need to make next.
For Raymond Yue, Avalara’s SVP of Finance, that has changed the role finance plays. Month-end close and compliance remain fundamental, but finance is now expected to also be a part of the work of understanding what’s changing, how it affects the business, and which decisions leaders need to make.
The challenge is building a finance function that can keep pace with the business without losing the clarity and judgment leaders need to make decisions.
The business behind the numbers
Close and compliance remain core finance responsibilities. But for Raymond, they’re no longer the limits of the role. As the business grows more complex, finance has to connect the details of performance to the decisions stakeholders need to make.
That means working more closely with leaders across the business – not only to explain the numbers, but to identify the assumptions and drivers behind them, then show what those inputs mean for the choices ahead.
In practice, that means finance has to bring enough context to help leaders test a scenario and understand the consequences of a decision. That work depends on finance, product, and go-to-market teams being able to start from the same data and definitions.
Getting everyone to the same meaning
At Avalara, planning has to account for changing regulations, multiple products, and different go-to-market motions at once. Finance, product, and go-to-market teams need shared data and definitions to understand what those changes mean for the business.
Having a shared foundation gives teams a way to examine the drivers behind performance and the assumptions shaping a plan, then test the scenarios that follow.
For Raymond, that called for a planning platform that could bring scenario analysis out of static spreadsheets and give teams more flexibility as the business changed. That’s what led Avalara to Pigment.
Building with more room to rethink
Raymond had been through seven or eight planning implementations before he joined Avalara. He knew the familiar pattern: finance documents its requirements, hands them off to a vendor, then waits until UAT to see how the planning process works in practice. That approach can leave teams with a short window to evaluate the platform before go-live, and even less room to ask whether the process behind it still makes sense.
With Pigment, Avalara had visibility from the start. The company used Pigment’s Modeler Agent for about 90% of the initial build, allowing the finance team to follow the model’s progress and rethink the processes taking shape around it.
The work took about 16 weeks in total, even within Avalara’s multi-product, multi-segment business and private-equity reporting environment. Building the model collaboratively gave the team a chance to test the process as well as the platform. If an inherited workflow added complexity without helping the business move faster, they could change it before it became part of the new model.
No more fighting the data
Before Pigment, much of the planning work at Avalara still took place outside of the planning platform. Teams would work through analysis in Excel, then import it back into the system, creating an extra handoff before the numbers could be used in planning. From there, finance needed to extract a large volume of data and turn it into pivot tables before bringing their completed analyses to business partners.
Because of that complex process, more time was spent wrangling the data than leveraging it.
With Pigment, finance can move from preparing analysis to exploring it as a part of strategy. Instead of pulling data into a separate spreadsheet before a conversation, Financial Planning & Analysis (FP&A) teams can work from a shared dashboard and test scenarios in real time as different assumptions change the plan. That creates space for finance and business partners to work through the implications of changing assumptions together during planning meetings.
Ultimately, that changes finance’s role in the room. Instead of bringing a retrospective view of what happened, the team can help business partners work through what could happen next, as well as the choices each path creates.
You can’t be an observer
For Raymond, keeping up with AI means testing it on real finance questions – seeing where it can help develop a model, explore an issue, or make routine work easier. He sees value in giving people room to experiment with the tools, share what they learn, and understand where AI can help them work more effectively.
At Avalara, people are encouraged to try AI in their own work and share what they learn. In finance, those experiments are prompting a closer look at how work moves through a process and where AI agents could help reshape it.
For Raymond, AI can turn experimentation into a better way of working. As finance teams use AI tools to explore more questions and take repetitive work out of the process, they can bring greater curiosity and stronger partnership to the business at the speed the business requires.
