AI-FirstCreativeProductionWorkflow
+60%
Production capacity increase
without adding headcount

Impact
My team went from managing creative requests by email to running a system that anyone at Actinver can use.
Creative Ops — Before vs After
From reactive inbox to a structured production system
Before
Requests via email
No structure, no priority
Manual triage
By one person, urgent invisible
Incomplete briefs
1–3 days back-and-forth
Manual delegation
No ownership tracking
No QA checkpoint
Errors reach delivery
5–8 days average
No visibility for executives
After
Intake form
One entry point
Auto-assignment
By type + complexity
Structured brief
Complete from the start
Stage-based tracking
With ownership
AI validation layer
95%+ error reduction
2–4 days faster
Executive dashboard live
Context
The Problem
When I arrived as Brand Lead, I mapped out the full creative process. The problem was clear: every single request ended up in one person's email.
The deeper issue was organizational. Marketing's output was invisible to executives because there was no system to make it visible. The team was doing a lot of work and nobody could see it.
The Solution
I built a creative operations system with Airtable as the main platform, supported by Slack and Institutional Mailing. The entire process — from intake to delivery — is now structured, automated, and visible.




Execution
I built the entire Airtable architecture and automation logic myself. Then I ran two tracks simultaneously:
Internal — Onboarding sessions for Marketing and Growth teams, documentation shared across the whole company, feedback collection, and two major iterations based on how people were actually using it. v1 was intake and assignment. v2 added the AI validation layer and the executive dashboard after seeing that the biggest gap wasn't speed — it was visibility.
Organizational — I presented the system to executives as a performance visibility tool. That shifted how Marketing was seen — from a reactive service that was hard to measure, to a structured operation with clear metrics.
The system wasn't something we launched once and moved on from. We treated it like a product: versioned, iterated, and improved based on real feedback.
System Rollout
Treated as a product — built, piloted, iterated
Build
Airtable architecture · automation logic · intake form design
Pilot
Small group test · Marketing + Growth · feedback collection
Onboard
Training sessions · documentation shared · cross-company
Iterate
Round 1 structural fixes · Round 2 UX improvements from real usage
System live
Adopted by Marketing, Ops, and Digital Lab
Key Deliverables
Outcome
In the first two months, production capacity went up 60%. Delivery times dropped by 3–4 days. After-hours work caused by reactive overload basically disappeared.
Operations and Digital Lab independently decided to replicate the system for their own teams. No training, no coordination from my side. They just saw what Marketing was doing and built their own version. That's the best validation a system can get.
Beyond capacity and speed, the system gave the team something it never had before: data. That visibility gave us the numbers to demonstrate Marketing's impact to leadership with hard evidence instead of perception.




The Platform
2026 update
The ops system solved the process problem. But I wanted to be more efficient, so I built the next layer — a standalone AI-powered creative platform, migrated to Claude Code.
The first version was prototyped in Figma Make to validate the concept and interface quickly. Once the logic was proven, I migrated it to Claude Code — turning it from a prototype into a real production system.
Once Airtable registers a new approved request, the platform picks it up automatically. It reads the brief, pulls from NanoBanana Pro and Actinver's photo stock, generates the piece grounded in the Actinver brandbook, adapts it for every social media format, and delivers it back to Airtable — reviewed for visual quality and copy accuracy.





Reflection
The team had the capacity — it just wasn't visible or organized. Building the right process unlocked more output than adding headcount would have.
Every asset was designed by a human. AI's job in this system was to catch what humans miss at scale — errors, tone drift, misalignment with the brief — before anything reached the client. Not replacing judgment; extending it.
Showing executives what the backlog actually looked like changed how Marketing was perceived internally. The work didn't change — the ability to see and measure it did.