Designing a Scalable Intake & Enablement System for Educational Media

Organization: ArtCenter College of Design
Role: Associate Director (Internal Product & Operations)
Duration: 2 years

Organizational Context (ACO → Heavin)

ArtCenter Online (ACO) operated as a content-first initiative with high headcount and low delivery efficiency.

• Requests were handled reactively, with no consistent intake, prioritization, or success criteria

• Content strategy emphasized volume over outcomes

• Despite a team of ~12, the group struggled to deliver usable, high-impact assets

After three months, leadership dissolved ACO and formed Heavin Studio, a lean educational media production group.

• Team reduced to three

• Mandate shifted from producing content → scaling educational impact under clear constraints

• Objective: increase output, reduce waste, and create a sustainable operating model without adding headcount

Core Problem

Demand for educational media exceeded production capacity.

Requests routinely lacked clarity around:

• purpose

• format

• distribution

• level of effort

As a result:

• Low-complexity work consumed high-cost production time

• Central production became the default for all requests

• Many completed assets were underutilized or unused

Product Approach

Rather than expanding headcount, we focused on reducing ambiguity, segmenting demand, and shifting work left.

I helped design and iterate an intake-driven prioritization system that changed how work entered the organization and how decisions were made before production began.

Redesigning the Intake & Production System

Operating model for intake, prioritization, and request routing
Requests were triaged through structured intake and discovery before being routed to central production, self-service enablement, delegation, or deliberate decline. Post-delivery review informed ongoing prioritization logic.

System Components

1. Intake as a Decision Gate

Problem
Recordings were requested without a defined use case or distribution plan.

Decision
Require requesters to define:

• goal of the asset

• intended audience

• distribution channel

Outcome

• Reduced wasted production time

• Improved alignment at kickoff

• Higher likelihood of asset adoption

2. Discovery & Scoping Layer

Problem
Faculty knew what content they wanted, but not the most effective format or level of effort.

Decision
Introduce a structured discovery call to:

• validate demand

• explore production models

• prioritize work against available capacity

Outcome

• Clearer scope before production

• Fewer late-stage changes

• Central capacity focused on highest-impact projects

3. Self-Service Enablement (“Studio in a Box”)

Problem
A high volume of low-complexity requests required in-person support from a very small team.

Decision
Shift repeatable work to a self-service model by:

• repurposing unused COVID-era equipment

• running faculty workshops

• creating templates and guidelines

• providing light consultation instead of full production

Outcome

• Central team effort reduced by ~92% per request

• ~12 hours → ~1 hour of involvement

• ~12× increase in production efficiency

• Freed capacity for high-impact work

Changing Default Decision-Making

Request routing mix over time
Central production shifted from the default path to one of several deliberate outcomes, with increasing use of self-service, delegation, and explicit decline as the system matured.

System-Level Outcomes

Efficiency gains following intake, prioritization, and enablement changes
Production output increased while cost per delivered asset and cycle time declined following the introduction of structured intake, prioritization, and self-service enablement.

Indexed metrics normalized to 2022 baseline.

Operating Model

• Three-person core team

• Daily syncs to:

  • track progress

  • reprioritize work

  • resolve blockers

• Oversaw student workers across production and post-production

• Continuous refinement of intake and routing logic based on usage and outcomes

Results (Year One)

• 80+ educational media productions delivered

• Significantly higher throughput than the prior, larger team

• Clear prioritization framework adopted across stakeholders

• Scalable model achieved without additional headcount

Impact Summary

Overall

• Output scaled ~13× in two years, while cost and cycle time both fell

• Improvements achieved without adding headcount (team reduced from ~12 → 3)

• Scaling came from system design, not increased effort

Throughput & Output

• Annual deliverables grew from 6 (2022) → ~40 (2023) → ~80 (2024)

• Represents a ~13× increase in completed work

Cost Efficiency

• Cost per asset (indexed) dropped 100 → ~30 → ~15

• ~85% reduction in relative cost per deliverable

• Driven by intake gating, reduced rework, and self-service enablement

Delivery Speed

• Cycle time reduced ~90% overall

• Shifted from 6–12 months → 8–10 weeks → 3–4 weeks

• Enabled faster iteration and higher stakeholder trust

Demand Routing & Capacity

• Central team ownership dropped from 100% → ~50% of requests

• Remaining demand absorbed via:

  • ~20% self-service

  • ~20% delegated to partner teams

  • ~10% declined or deferred

• Central production became intentional, not default

Team Leverage

• Low-complexity request effort reduced ~12 hours → ~1 hour

• ~92% reduction in per-request effort

• Freed capacity for higher-impact, evergreen work

Net Effect

• More output, lower cost, faster delivery — simultaneously

• Created a repeatable operating model adopted across the organization