Why Every AI Pilot Needs a Deadline (and How to Measure Success)
Open-ended AI pilots are business killers. They drain budgets, frustrate teams, and rarely deliver results.
You know the story. Six months ago, someone said "we need AI." The pilot started with enthusiasm. Now it's month eight, the budget has tripled, and nobody can explain what success looks like.
The Problem: AI Projects That Never End
Most Australian businesses approach AI pilots backwards. They buy tools first, ask questions later. No clear goals, no timeframes, no measurement criteria.
This creates the perfect storm:
Teams experiment without direction
Costs spiral out of control
Leadership loses confidence in AI
Real opportunities get missed
The worst part? When these projects finally die, they poison the well for future AI initiatives.
Why Open-Ended Projects Fail
Without deadlines, AI pilots become science experiments. Teams tinker endlessly, chasing perfect solutions that never come.
Consider what happens in most businesses:
Week 1: Excitement and big plans
Month 2: "We're making progress"
Month 4: "We need more time to get it right"
Month 8: "Maybe this isn't working"
Meanwhile, your competitors who set clear boundaries are already seeing results.
The absence of time pressure removes urgency. Without urgency, there's no focus. Without focus, there's no result.
The 90-Day Rule Changes Everything
Smart businesses give AI pilots exactly 90 days. Not 89, not 91. Ninety days.
This timeframe forces discipline:
Teams focus on achievable goals
Resources stay controlled
Results become measurable
Decisions happen quickly
Ninety days is long enough to see real impact. Short enough to prevent scope creep.
How to Structure Your 90-Day AI Pilot
Start with these non-negotiables:
Choose One Specific Problem
Pick something measurable and repeatable
Avoid "let's see what happens" projects
Focus on business processes, not technology features
Set Clear Success Metrics
Define what "working" means in numbers
Establish baseline measurements before you start
Agree on minimum viable improvement
Provide Proper Training
Invest in upfront education for your team
Set guardrails and usage policies
Give people time to learn properly
Measure Weekly
Track progress against your success metrics
Address problems immediately, don't wait
Celebrate small wins to maintain momentum
Real Examples That Work
Invoice processing automation shows this approach in action. One business identified their problem: AP staff spent 20 hours weekly on manual invoice entry.
They set their 90-day goal: Reduce manual processing time by 75%. Week by week, they measured improvement. By day 60, they'd hit their target.
Total investment: Four days of training, $500 in software licenses. Result: 15 hours weekly saved, permanent process improvement.
Small focus, clear metrics, definite timeline. It works.
What to Measure (And What Not To)
Focus on business outcomes, not technology metrics:
Measure These:
Time saved on specific tasks
Error reduction percentages
Cost savings per month
Employee productivity gains
Ignore These:
How "cool" the technology feels
Number of AI features used
Complexity of implementation
Comparison to competitor tools
When Your Pilot Succeeds (Or Fails)
At day 90, you have three options:
If you hit your targets: Roll out to more people immediately. Success breeds success.
If you partially succeed: Extend for one more 90-day cycle with adjusted goals. But only once.
If you fail completely: Stop immediately. Learn from the failure. Try something different.
No endless "almost there" extensions. No moving goalposts. The deadline is sacred.
Making Your Next AI Project Count
Before you start another AI pilot, ask these questions:
What specific business problem does this solve?
How will we measure success in numbers?
Can we achieve meaningful results in 90 days?
Who owns the outcome?
If you can't answer all four clearly, you're not ready to start.
Your AI investments deserve better than hope and crossed fingers. Give them structure, deadlines, and measurement. Give them the respect of clear expectations.
The businesses winning with AI aren't the ones with the fanciest tools. They're the ones with the clearest deadlines and the strongest discipline to stick to them.