The One Where No One is Ready
Why your AI pilot isn't launching (and the 15-minute fix that gets you unstuck)
You know that Friends episode where Ross is slowly losing his sanity because everyone's supposed to be ready for the museum gala in 20 minutes?
Joey's been staring at his two identical shirts for an hour.
Rachel's tearing apart her room hunting for "the good earring."
Chandler's settled into the chair and refuses to move.
Ross keeps checking his watch: "We're supposed to be there in 15 minutes!"
Joey responds: "Okay, I just have to pick a shirt."
"You've been doing that for 20 minutes!"
"Right, but now I have to switch, because this one makes me look like a waiter."
That's your AI pilot right now.
Why This Hits Close to Home
Last month, I sat through my third "readiness review" for the same content generation pilot.
Same conversation. Same concerns. Same "we're almost there" energy.
The prompts needed "one more optimization pass." The dataset needed "final validation." The review process needed "stakeholder alignment."
Meanwhile, our content team was burning 15 hours a week on research that AI could handle in 2.
Sound familiar?
Here's what I've learned: Most AI pilots don't fail because the technology isn't ready. They fail because perfectionism masquerades as due diligence.
And perfectionism at the pilot stage is just procrastination with a PowerPoint deck.
The Three Traps That Kill Momentum
1. The 95% Accuracy Trap
The Problem: Teams demand production-level accuracy from experimental workflows.
What I Hear: "We can't launch until hallucinations are under 5%."
The Reality: Your current manual process probably has a 20% error rate that nobody measures.
We tracked our content research accuracy before introducing AI. Manual research had citation errors, outdated stats, and missed key insights about 18% of the time. AI research? About 12% error rate after basic quality gates.
We launched.
2. The Infinite Prep Trap
The Problem: No clear definition of "ready to test with real users."
What I Hear: "Just one more sprint to clean the data/refine the prompts/train the team."
The Reality: Perfect preparation is the enemy of valuable learning.
I use the "Good Enough Grid" now:
Data Quality: 80% complete, representative examples ✓
Prompt Performance: Works for 70% of common use cases ✓
Team Training: Key users understand basics + escalation paths ✓
Measurement: Simple success metrics defined ✓
Four checkmarks = ship it.
3. The Ownership Fog Trap
The Problem: Everyone's responsible, so no one's accountable.
What I've Seen: Three weeks of Slack threads debating who owns prompt updates while the pilot sits dormant.
My Solution: RACI for AI Pilots (yes, I'm bringing back corporate acronyms, but this one actually works):
R - Responsible: Marketing Ops owns day-to-day execution
A - Accountable: CMO (me) owns go/no-go decisions
C - Consulted: Data team validates quality gates
I - Informed: Finance gets weekly cost/value updates
Write it down. Share it. Move on.
My 15-Minute Launch Decision Framework
When teams tell me they're "almost ready," I run this quick diagnostic:
Minute 1-3: The Reality Check
What's the worst-case scenario if we launch this pilot next week?
What's the worst-case scenario if we don't launch for another month?
Which risk is actually bigger?
Minute 4-8: The Value Validation
If this pilot saves us 2 hours per week, what's that worth annually?
How many weeks of prep time would that justify?
Are we past that threshold?
Minute 9-12: The Rollback Test
If the pilot goes wrong, how quickly can we revert to the old process?
What would "going wrong" actually look like in practice?
Can we contain the damage to internal workflows only?
Minute 13-15: The Launch Decision
If we can contain downside risk AND the potential value justifies the prep investment, we ship.
No exceptions. No "one more sprint."
Your AI Pilot Production-Readiness Checklist
Stop here. Print this. Use it as your gate for every pilot:
✓ Data Readiness
Input data represents 80%+ of real-world scenarios
Known edge cases are documented (don't need to be solved yet)
Sample outputs reviewed by subject matter expert
✓ Prompt Performance
Baseline prompts frozen for evaluation consistency
Success rate >70% on common use cases
Escalation triggers defined for edge cases
✓ Measurement Foundation
Success metrics defined (speed, quality, cost, satisfaction)
Logging captures all inputs/outputs for analysis
Weekly review cadence scheduled with specific owners
✓ Human Integration
Clear handoff points between AI and human review
Exception handling process documented and tested
Key users trained on basics + escalation
✓ Risk Management
Rollback plan tested and documented
Budget caps and monitoring in place
Incident response owner assigned
✓ Governance
RACI chart shared with all stakeholders
Data privacy and compliance requirements addressed
Legal/procurement sign-off obtained
6/6 = Ship it. 4-5/6 = Address gaps then ship. 3/6 or fewer = Not ready.
Real Example: How I Unstuck Our Content Pilot
Three months ago, our content research pilot was stuck in "almost ready" limbo. The blocker? Our content team wanted 95% source accuracy before they'd trust AI research.
Here's how I unstuck it:
Week 1: I audited our manual process. Found 18% error rate in citations and sources.
Week 2: Set new success criteria: "Better than manual" (12% error rate) instead of "perfect."
Week 3: Launched with a simple rule: All AI research gets 5-minute human fact-check before publication.
Results after 30 days:
40% faster research process
12% error rate (better than manual)
Team confidence up significantly
3 workflow improvements identified for next iteration
The pilot we "weren't ready" to launch became our template for more AI implementations.
PIVOT!
Why "good enough to learn" beats "perfect on paper"
The biggest strategic mistake I see CMOs make? Treating pilots like production deployments.
Pilots exist to prove value and surface problems—not to deliver flawless performance. Every week you spend in "almost ready" mode is a week you're not learning what actually matters for your specific team, data, and workflows.
Perfect preparation is procrastination in disguise.
The Script for Moving Forward
Next time your team says "we need another week," try this:
"What specifically would need to go wrong for us to regret launching this pilot next week? And what's the probability of that actually happening?"
Then:
"What's the cost of delaying another month versus the cost of learning something isn't quite right?"
Usually, the delay costs more than the risk.
Central Perk Coffee Break
5-Minute Action: Unstuck Your Stalled Pilot
Right now, pick your most "almost ready" AI initiative.
Set a timer for 5 minutes. Answer these questions:
What's the one thing blocking launch this week?
Can you solve it in 2 hours or less?
If not, can you launch without solving it perfectly?
What would you learn from a week of real-world testing?
If questions 2 or 3 are "yes," schedule the launch for next week.
Done.
What's Next
The One With the Thanksgiving Flashbacks
Next week, we're diving into something most marketing teams get wrong: learning from AI failures.
You know those Friends Thanksgiving episodes where they keep flashing back to previous disasters? Monica's burned turkey. Ross's divorce announcement. The time they were late to dinner because of [insert crisis here].
Each flashback reveals a pattern. A missed lesson. A way things could have gone better.
That's exactly what should happen with your AI experiments—but usually doesn't.
Most teams launch a pilot, see mixed results, then move on to the next shiny object without extracting the real lessons. They repeat the same mistakes. Make the same wrong assumptions. Get stuck in the same places.
Next week's issue will cover:
How to run proper AI post-mortems that actually improve future pilots
The four questions that surface hidden lessons from every experiment
Building organizational memory that compounds (so you don't re-learn the same lessons quarterly)
My template for turning pilot failures into strategic advantage
Because the goal isn't to avoid mistakes—it's to make better mistakes faster.
Until then, go get unstuck.
---Lisa
P.S. If this helped you finally ship something that's been "almost ready," forward it to another marketing leader who's stuck in prep mode. They'll thank you when they're actually learning instead of just planning