# Overcome the 95% Failure Rate and Launch GenAI Pilots that Deliver

> Why GenAI pilots fail when designed for demos instead of operations, and how to connect use cases to workflows, controls, evidence, and measurable value.

[Canonical HTML page](https://trustcyber.ca/insights/genai-pilots-measurable-value/)

- Author: Junior Williams
- Type: Insight brief
- Published: 2025-10-24
- Modified: 2025-10-24
- Topics: GenAI, AI implementation

## What this examines

The article argues that GenAI pilots fail when they are designed for demos rather than operations. The article recommends starting with discovery, process mapping, data readiness, human-in-the-loop workflow design, governance, and value metrics before technical buildout.

## Why it matters

The source is a practical implementation guide. It emphasizes business outcomes, adoption, total cost of ownership, data security posture, and maintainability over novelty or marginal model performance.

## Key ideas

- Many enterprise GenAI pilots fail because they lack operationalization, change management, and data readiness.
- Use-case qualification should score business value, AI addressability, data availability, implementation effort, people readiness, and time to value.
- Human-in-the-loop design should define who reviews, decides, and owns quality.
- Data posture means knowing what data exists, where it lives, who can access it, and what policies apply.
- Success measurement should baseline current performance, tie model metrics to outcomes, use controls, and track adoption separately.

## Resources

- [Read the original on LinkedIn](https://www.linkedin.com/pulse/overcome-95-failure-rate-launch-genai-pilots-deliver-junior-williams-obywc/)