While working on the AI Adoption framework, FIIP Studios were approached for a real client project in Mid-May. Seeing this as an opprtunity for a live pilot with a paying client, an NDA, and a hard deadline, I was tasked to design a workflow and support delivery.
I could test the framework I'd been building so far with the studio.[Refer to AI Adoption case study] My framework applied to a live project with a paying client could both test it and act as proof.
Sample background generated for client. The scene depicted a couple at the beach.
Like most agency-style projects, the director doesn't arrive with a finished brief. Our Pre-Viz Production had to start against an incomplete brief, knowing new ideas from ongoing story workshopping would keep arriving and shifting the goalposts mid-project.
The studio also sat idle whenever the director couldn't articulate their vision clearly, and creative changes — especially the late ones — required a near-complete restart, because nothing prevented the goalposts from moving after work was already underway.
The as-is workflow.
The new blueprint intentionally shifted the technical burden to the studio, and just as importantly, it fixed when the client was allowed to change their mind.
The toolset was deliberately simple. This was a pilot meant to validate the process before justifying investment in dedicated production-management or automation software, so the studio used tools already on hand:
Briefing: Google Docs, for capturing and updating the director's brief
Shot tracking: Google Sheets, for tracking shot status across scenes
Review: Frame.io, for client and director review and approval
Through a tone alignment session, the studio internalized the director's vision upfront and reduced perpetual client involvement during production. Critically, the blueprint introduced a scope lock at the scene level: shot design for a given scene only began once the concept for that entire scene was confirmed. Once locked, the client could no longer shift the goalposts on that scene — only minor in-scene changes (props, small details) remained open for review.
This is what made it viable to scope AI to specific assets without requiring constant director sign-off.
The to-be workflow.
We opened with a facilitated session to internalize the director's vision before any production began. This was the highest-risk moment in the workflow as it required the director to trust the studio with something they hadn't fully thought of yet.
The project manager and design lead worked through each shot to determine what AI could reliably produce versus what required skilled labour. This decision was made per shot rather than applied globally.
Adobe Photoshop's Generative Fill had launched in beta two weeks prior, so the design lead had to develop evaluation criteria in real time. Junior designers mass-produced screens using AI while sketch artists handled characters, so juniors would build design thinking skills rather than learn tools AI would eventually replace.
If compositing the AI outputs took as long as doing the work manually, the framework failed as we would have been better off skipping AI entirely. Final assets were reviewed against the established creative direction and exported as layered passes for the edit team.
The Edit Team assembled the layered passes into a final cut. The client-facing deliverable was the edited previz video itself, reviewed and approved by the director as the final step.
The pilot delivered 15 days ahead of schedule, despite the process being built in real time. The QA held. The compositing was easier but doing the work without AI gave us more control.
FIIP adopted the previz workflow as a permanent service offering.
↑ 30–40%
Revenue growth from AI-assisted billable services
↓ 2 Weeks
Reduction in production timelines
Adopted
The Previz workflow became a permanent offering
AI still needs human judgment to be production-ready. The outputs were usable but not without a considerable amount of compositing and minor image manipulation. Our Designers had to assess every AI-generated asset before it moved forward, and that expectation had to be managed with the client early.
Also, the juniors and interns used AI as a deliberate choice to develop their judgment by evaluating outputs and learn why something wasn't working.