From Filing Cabinet to Finance Partner: Five Takeaways

For many production finance teams, doing more with less is the baseline now. That was the message from Carl Vorwerk, Head of Production Finance at Wheelhouse Entertainment, during our recent webinar, From Filing Cabinet to Finance Partner: Why Production Software Must Evolve.

Carl joined Wrapbook’s David Valentino and Jasper Lipton to talk about automation, visibility, and control. They discussed a situation most production finance teams will recognize: smaller teams, tighter budgets, and stakeholders who expect answers immediately.

Time and again, the conversation turned to AI, and it comes up in most of the five takeaways that follow. You can watch the full recording below.

1. Friction points are opportunities, especially for AI

Carl pointed out that automation has been part of production finance for a long time. At its core, Excel is a form of automation. What’s changed is the effort. A spreadsheet with macros that once took him days to build, one that takes a downloaded report and breaks it apart every way he needs, now takes about 15 minutes with AI.

The tools he builds are practical: simple web apps and macro-driven spreadsheets that take data from one place, manipulate it, and pass it along to whoever needs it. He’s also customizing tools for his field team, line producer, and production manager, so their work gets easier as well as his own.

His advice for getting started is to look for points in your workflow where something could run more smoothly. Then, explain the issue or pitch an idea to an AI tool and see what it comes up with. If it uses terms you don’t know, like API or MCP, ask it to explain them to you. If you’re not sure how to begin, tell the tool you’re a production accountant, describe what you do and the problems you run into, and ask how it would approach making one task more efficient. Carl also recommends picking one tool and going deep on it, including asking it to teach you how to use it.

For finance leaders: ask your team which recurring reports eat the most hours, and give them room to build a tool for the first one.

2. Fix your data foundation before you build on it

Automation only helps if the data underneath it is sound. David pointed out that one place accuracy can break down is in the manual glue between systems. Field teams, office teams, and a B crew on another location come in with different levels of experience and their own ideas of how a process should run, so data ends up in different places, or in the same place but in different formats. When data moves from one format or system to another, errors are bound to happen, and automation can compound them fast.

The practical step is to settle how data gets entered before you automate anything: one place for each type of data, the same formats, and the same terms for everyone entering it. That’s what lets the people building on top of the data trust what comes out.

For finance leaders: before building on top of your data, check that every team entering it, in the field and in the office, uses the same standards.

3. Build in checks, then let go of re-reviewing

Accountants tend to hold onto data until the last second, and letting go can be a constant battle. Carl’s answer is to spend the time up front building a tool he trusts, instead of re-reviewing the same report every time it runs. The trust comes from checks built into the process. And if you’re using AI, make sure you verify results before you sign off on them.

Both Carl and David offer ways to build those checks. Before you rely on a workflow, ask AI to list everything that could go wrong with it, which Carl says it has become good at. Then split the work into steps, as David suggests: one step drafts the output, a second checks it, and a third packages it for its audience. And know your fundamentals well enough to recognize when something looks off.

For finance leaders: ask your team which reports they re-review every time they run, and whether a built-in check could take that over.

4. Manage who sees what, and keep the context

Carl said his role has moved out of the weeds and into managing data: who sees what, and when. He spends real time in permission settings, working out who needs access to which tool or report, so that once it’s set up he can step away. David framed the goal as the right data reaching the right person at the right time, which becomes even more important when a production company has multiple productions running across multiple EINs.

Context matters too. Someone looking at a budget without context is missing why the numbers look the way they do, and David noted that instant access removes the window to explain them. The result can be a flurry of emails for accounting and wasted cycles for the studio team. So when numbers go to people who weren’t on set, send the context with them. And remember that your experience, such as knowing a storm forecast will upend a location shoot, is something AI doesn’t have.

For finance leaders: review who has access to what whenever a production joins the slate, and decide when numbers are ready to be seen.

5. Stay curious and retest your tools

Carl came to TV and film from live theater and taught himself the rest, starting with basic questions such as “Whether a grip is exempt or non-exempt.” He thinks curiosity matters even more now because the tools change so quickly. His advice: if something doesn’t work, try again in a month.

Both Carl and David build learning into their routines. Carl has AI research a topic he doesn’t know and turn it into a podcast for his walk home, so he can apply it the next day. David has a weekly digest of larger finance news framed for entertainment, because what big banks are working through tends to reach production finance within months. The takeaway: give yourself a recurring time to learn one new thing, and watch adjacent industries to see what’s coming.

For finance leaders: give your team time to experiment, and revisit the tools that didn’t work the first time.

Wrapping up

The teams keeping pace with tighter budgets and faster turnarounds share a few habits. They turn friction points into opportunities, fix their data foundation before they build on it, add checks before they let go of review, manage who sees what without losing the context, and stay curious about tools that keep changing.

Watch the full conversation above to hear Carl and David go deeper on each of these practices, including their answers to audience questions.