Table of Contents
Key takeaways
- At Unilever, forecasting has become an operating rhythm that aligns leadership around the assumptions shaping the business.
- Associate Director of Finance Keith Masiulis sees the space between sales and finance as a translation problem, where promotions and trade spend need to connect clearly to margin, ROI, and growth.
- Unilever rebuilt a trade-close process that relied on roughly 100 Excel files, reducing a two-to-three-day workflow to just two to three hours.
- The new process gives leadership more time to focus on how to close a business gap instead of reconciling competing figures.
- At Unilever, AI supports a controlled co-building process where finance teams can set the design, test the output, and refine the model as they go.
- Keith sees the next generation of finance leaders bringing together financial fundamentals, data fluency, and change management.
Unilever is a giant, operating across countries, markets, retailers, categories, and a staggering range of SKUs. Commercial teams make constant decisions about promotions, media, coupons, and trade investment throughout the month, which means finance needs to understand the margin, ROI, and wider financial impact behind those choices fast.
Keith Masiulis sees those decisions from both sides. As Associate Director of Finance for North America Operations and Customer Development at Unilever, he works in the space where commercial activity and financial performance come into view together.
Sales and finance may begin with the same growth target, but then turn to different numbers, systems, and questions. Time spent reconciling those views can pull attention away from critical business gaps and the decisions needed to close them.
When finance and sales speak different languages
Having a shared growth target doesn’t guarantee a shared view of how to reach it. Sales and finance teams can look at the same opportunity through different systems, data, and assumptions, then arrive at a decision with numbers that don’t immediately connect.
Keith’s team starts by understanding the baseline behind each number, the bridges between commercial and financial views, and the assumptions underneath them. The numbers may not be identical, but leaders can see how they connect and use meeting time to decide how to move the business forward.
The cost of waiting on the close
Trade close had become one of the most complex processes in Unilever’s finance function. It ran across roughly 100 Excel files, with multiple financial application refreshes and manual hierarchies connecting the pieces. A three-person team could spend two to three days completing each refresh, while a new SKU, category, allocation, or management unit could surface late and delay reporting.
Keith and his team used Pigment’s Modeler Agent to rebuild the process in three days, bringing the files and their relationships into one controlled environment. The result is what the team calls zero-day close: a same-day trade close that gives the business a full financial view without the usual wait for corrections and adjustments.
What once took two to three days now takes two to three hours, giving finance a timely view of the business and more room to focus on what the numbers mean.
Confidence in the numbers
When customer development teams request incremental trade funds, they now come prepared to explain the opportunity: the growth they expect it to generate, the financial impact, and the assumptions behind it. The information is at their fingertips, and senior leaders expect them to know the business inside and out.
That expectation grew out of the work behind zero-day close. As Keith’s team rebuilt the process, it found ways to make the data more granular and accurate. Those changes reshaped Unilever’s view of accruals, profit, and the balance sheet across its business units.
The future of finance is data
Finance fundamentals remain central to Keith’s view of the work. Teams still need to understand the accounting principles, controls, and business context behind the numbers. But as AI takes on more of the work of building and operating financial processes, data becomes the connective tissue between that expertise and the decisions the business needs to make.
Keith sees the finance team of the future as one that can work comfortably across finance, data, and the systems that bring the two together. The work begins with business questions: what leadership needs to understand, which assumptions matter, and how the information should connect. AI can help build and operate the process around that decision flow, while finance gives it the structure, context, and direction.
Change management is part of the role, too. Keith’s team has seen demand grow as people across the business bring new ideas to the table and ask for new views. Building something useful is only the first step; scaling it means bringing the people who will rely on it into the process.
Build, don’t critique
Keith’s advice for people entering finance is straightforward: build something.
The tools now at a finance team’s fingertips make it possible to test an idea, build a beta, and put it in front of the people who will use it. That changes the value of a good instinct. An observation about what could work better can become a tangible output that a team can react to, improve, and make its own.
Keith saw that momentum take hold with zero-day close. Once the team saw how quickly it could reshape a process that had been in place for more than a decade, new ideas and requests followed. The work gave people a reason to keep challenging what came next.
