Live · Survey Story
Urban Arts × AI Workshop / Apr 2026
Pre-Workshop Pulse · 14 of 14 Responses
14
Voices.
One
Picture.
A snapshot of where Urban Arts stands, what it fears, what it wants — and what it's already quietly doing — at the edge of the AI shift.
Source · 14 anonymized responses
Window · 13–24 Apr 2026
Departments · Programs, Ops, Dev, Leadership
Scroll to begin
Chapter 01 · Where we say we are
Mostly
Aware.
Barely activated.
Asked to pick a maturity stage, the team was almost unanimous — and self-aware about it.
Self-reported maturity stage
10Awareness
3Activation
1Not sure
10 of 14 say "interest is growing, but real adoption is minimal." 3 say pilots are underway.
vs. closest peer / competitor organizations
4Significantly behind
4Slightly behind
4About the same
1Slightly ahead
1Not sure
8 of 14 see the org as behind peers. Only 1 feels ahead.
"We only talk about it as if it's a Programs team creative challenge to figure out — but it's an organizational-wide one."
— a team member, on AI policy
"Balancing the wish to stay relevant and identify efficiencies… while acknowledging the valid, broad ethical concerns impacting the communities we serve."
— a team member, on the tension
Chapter 02 · The vision gap
Strategy clarity, on a 0–10 scale:
a 1.6.
Eleven of fourteen team members rated the long-term AI vision and strategy as a 2 or below. A small handful saw something more — but most see fog.
0 · Non-existent
5
10 · Fully documented & communicated
Each dot = one team member. Average marked in coral.
Chapter 03 · The foundations
The will is there.
The rails aren't.
Across seven readiness dimensions, the team rates culture & openness highest and IT security & governance lowest. The appetite outruns the infrastructure.
7 components × 14 responses · darker = weaker, lighter = stronger
Very poorly
Needs improvement
Adequate
Strong
Excellent
Average score per dimension (1–5 scale)
"Culture is willing. Documentation is willing. Security is the floor we'd be building on — and it's the lowest-rated component on the list."
— signal from the data
Chapter 04 · What worries us
Bias.
Then — "where do we even start?"
When asked to pick the org's top 3 concerns, the team didn't hesitate. Bias dominated. But right behind it sat a quieter, more telling worry.
10 of 14 flagged Bias. 8 flagged "We don't know where to start." 6 flagged Accuracy.
And the open-text concerns nobody else named
student pushback
environmental impact
impact on students' futures
decision-making erosion
community ethics
no operational policy
"Uses of AI that remove decision-making and critical thinking — and the impact on students' future livelihoods."
— a team member, on what they really worry about
Chapter 05 · What we bring
Skill level today, on average:
basic.
No one rates the team as advanced. Three say intermediate. The rest sit between novice and basic — a starting line, not a deficit.
Level 1
Novice
3
Level 2
Basic
8
Level 3
Intermediate
3
Level 4
Advanced
0
The mode is "Basic." The headroom is enormous — and a workshop is exactly what the doctor ordered.
Chapter 06 · What would actually help
Don't lecture.
Give us a playbook.
When asked what would most accelerate adoption, the team converged on three things — and none of them are abstract.
"Training. A use-case playbook. Approved tools. We need permission and a path — not another think-piece."
— synthesis of 14 responses
Chapter 07 · Where AI could win us time
Operations.
Insights.
In a tie.
Two areas dominate the team's thinking on competitive advantage. They are practical, daily, and measurable.
In their own words — workflows the team would most like to fix
Salesforce data entry
Invoice processing & AP
Grant reporting
Budget vs. actuals
Forecasting
Quarterly slide decks
Attendance tracking
Time allocation reports
Student progress tracking
Mailing list / mail merge
Admissions emails
Donor acquisition & tracking
Donor communication & stewardship
Recruitment / outreach
Curriculum delivery
HR & timesheets
Templates & documentation
Data collection & sharing
Workflow processes
Chapter 08 · The plot twist
"Awareness" — but you're
already doing it.
Despite a team that mostly self-rates as "Awareness," the open-text answers tell a different story. Pilots, prototypes, and quiet experiments are already in motion across Urban Arts.
In Programs
AI Tutor
In development for the game design program — both Anthropic and CoPilot prototype variants in flight.
In Programs
AI Literacy
Lessons developed and 1 PD delivered. A formal Programs AI policy statement was published last year.
In Operations
AI Note-takers
In active use across virtual meetings — quietly transforming meeting recall and follow-through.
In Operations
Calendly Auto-flow
Automating registrations and follow-ups for info sessions — small but real.
Founders Lab
AI Accelerator
Driven by the AI Accelerator Retreat. AI is now used and discussed in classes — game design uses legacy AI; broader AI is taught across.
In Programs
Playlab Exploration
Exploring AI EdTech tooling and the differences between AI types — pre-implementation, post-curiosity.
Across the team
ChatGPT & Midjourney
Used by individuals — ad hoc, undocumented, no shared process. Latent capability waiting for shape.
Leadership
AI Tutor — Org-wide
"We are building an AI tutor to integrate into our programs." A clear strategic intent stated from the top.
Across the team
Content Creation
A team member already uses AI for content creation — quietly, day-to-day. The fastest, lowest-friction adoption path.
9 distinct AI initiatives surfaced — in an organization that mostly calls itself "Aware."
Chapter 09 · The leadership signal
Nobody disagrees.
Half lean in.
Asked whether leadership is willing to redesign roles, processes, and decisions when AI offers a better way — the response was striking. Zero pushback. Real openness.
0Strong Disagree
0Disagree
7Neutral
5Agree
2Strong Agree
7 of 14 already lean positive — exactly half the room. 7 are neutral: open, watching, persuadable. None push back.
"We are building an AI tutor to integrate into our programs."
— from leadership · one of two "Strong Agrees" in the dataset
Chapter 10 · What this means for the workshop
Three
things the
data is saying.
01
The runway is open.
No one disagrees that leadership is ready to redesign for AI. That's a rare gift — most rooms have at least one "no." We can build with confidence the change won't be blocked at the top.
02
You're more activated than you say.
Nine live AI initiatives surfaced from a team that calls itself "Aware." The work today isn't to start — it's to stitch. Surface the pilots. Pick the strongest. Make it the case study.
03
Don't sell vision. Hand them a playbook.
Training, a use-case playbook, and approved tools were the top three asks — by a wide margin. The team isn't asking for inspiration. It's asking for permission and a path.