Por qué el 80% de los pilotos de IA fracasan—y cómo estar en el 20%

The KPI Problem

Most AI pilots fail not because of technology limitations, but because success was never clearly defined. Teams build impressive demos that don't connect to business outcomes.

The 3-Question Framework:

1. What metric moves? Before writing code, identify the KPI: response time, error rate, cost per transaction, conversion rate.

2. What's the baseline? Measure current performance for 2-4 weeks. Without this, you can't prove improvement.

3. What's the threshold for success? Define the minimum improvement that justifies the investment (e.g., "20% reduction in manual processing time").

The Pilot Trap

Many teams fall into the "perpetual pilot" trap:

• Build a proof of concept ✓
• Demo to stakeholders ✓
• Get approval to "keep exploring" ✓
• Never ship to production ✗

The fix? Time-box ruthlessly. Every pilot has a 4-6 week window. Either it hits the KPI threshold and moves to production, or it gets killed.

Real Example

A fintech client was building an AI-powered lead scoring system. Their first attempt had no clear KPI—just "better leads."

We redefined the goal: Increase SDR-to-booked-call conversion from 8% to 15% within 6 weeks.

The result? 19% conversion. Shipped to production in 4 weeks.

Action Items

Before your next AI project:

• [ ] Define the specific KPI
• [ ] Establish a 2-week baseline measurement
• [ ] Set a go/no-go threshold
• [ ] Time-box to 6 weeks maximum

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