FIIP Studios, a Video Production Solutions studio, brought me in as a consultant to build something that didn't exist yet: a research-backed framework for AI adoption in creative production.
In 2022, generative AI burst onto the scene and clients immediately wanted studios to use it. Studios that couldn't offer AI-assisted services were losing work to competitors who could, or would soon. As far as I could determine, no studio in the Indian market had done this at the time.
Plugging AI tools into existing workflows without understanding where they fit risked destroying the quality and credibility of the people doing the work. Unfortunately, the tools were moving faster than the thinking.
I ran generative workshops with FIIP's designers and directors. I also drew on my own firsthand experience working in the industry. And I assessed the landscape simultaneously across two angles: how other studios were responding to AI adoption, and which tools and technologies were actually production-ready versus experimental.
Generative Workshops
Landscape Assessment
I wasn't looking for best practices. I was looking for failure points: where AI broke in real production contexts, where it created more work than it saved, and where it eroded quality or destroyed timelines. Each failure became the basis for a guideline.
Telling AI to generate everything produced outputs that broke down on details like style and assets that couldn't be used without significant rework. Directing AI to specific tasks produced far more usable results than doing its own whole thing.
Guideline → Embed AI as a copilot inside human-led workflows, not as a replacement for them.
Clients in film production regularly demanded large numbers of options in short timeframes, destroying team capacity and forcing overtime. AI could absorb that volume for low-stakes assets like backgrounds that clients frequently changed anyway.
Guideline → Use AI for voluminous generation in early stages where quantity matters more than finish quality.
Not every part of a project required AI generation, which meant AI usage for assets that didn't need it across an entire production pipeline resulted in consumption of significant render time.
Guideline → Scope AI to specific phases rather than across the entire pipeline.
With most tools (including paid versions), asking AI to fix one single detail resulted in rebuilding everything around it. Adobe's Generative Fill was an exception: it allowed work on specific sections in isolation, which made it the right tool for this framework.
Guideline → Use tools that allow isolated edits without requiring full regeneration.
With the four guidelines established, in Mid-May we were approached for a real client project. FIIP studios saw this as an opprtunity for a live pilot with a paying client, an NDA, and a hard deadline.
Full breakdown of that pilot including the workflow redesign, execution, and delivery, is documented separately as its own case study.
View Case StudyWhat started as a research hypothesis became the studio's standing approach to deciding where, when, and how to use AI on any given project.
↑ 30–40%
Revenue growth from AI-assisted billable services, validated by the live pilot
↓ 2 Weeks
Reduction in production timelines, validated by the live pilot
Adopted
Became a permanent part of Studio approach
Telling AI to "create a video with [INSERT YOUR CONCEPT]" can give you a video, but after a certain point, telling AI to "make it different" is not guaranteed to differentiate your work product from the market. Once AI commits to a direction, it can always produce new outputs that may solve one problem and create three others.
Oversimplified AI approaches erase the labour that builds the next generation of designers. The logic sounds reasonable: junior designers and AI can both handle low-priority tasks, therefore we can free up budget for seniors and leads. The problem is seniors come from junior designers who did low-priority work and built the design thinking that eventually made them worth hiring at a senior level.