---
title: "Why 85% of AI Projects Fail"
description: "5 source-backed AI claims from Why 85% of AI Projects Fail, with key statistics, context, and the original AI Adopters Club source."
url: "https://kbanc.com/claims-library/why-85-percent-of-ai-projects-fail"
source: "https://aiadopters.club/p/scaled-framework"
date: "2025-03-12"
topics: ["strategy", "implementation", "business"]
generated: "2026-08-31"
---

# Why 85% of AI Projects Fail

By Kamil Banc | March 12, 2025

## Claims

1. **Most AI Projects Fail** (source summary): More than 80 percent of AI projects fail, wasting billions of dollars in capital and resources.
2. **Leadership Drives Failures** (source summary): SPR research attributes 65 percent of project failures to poor leadership practices.
3. **Data Quality Barrier** (source summary): NewVantage Partners research found 92.7 percent of executives identify data quality as a significant AI barrier.
4. **IBM Predictive Maintenance Win** (source summary): IBM implemented predictive maintenance AI that reduced equipment downtime by 20 percent through robust digital infrastructure.
5. **Augmentation Beats Automation** (Kamil's interpretation): Kamil Banc argues framing AI as augmentation rather than automation dramatically reduces team resistance to adoption.

## Evidence

### Quote
> "The question isn't whether AI will transform your industry—it's whether you'll lead or try to catch up." - Kamil Banc

### Key Statistics
- **85%**: Estimated share of AI projects that fail, according to research cited from Tom's Hardware, despite billions in investment.
- **76%**: Share of 'AI champions' who received promotions within 18 months, per the article's career opportunity data.
- **13%**: Percentage of organizations fully prepared for AI implementation in 2024, per Cisco's AI Readiness Index.
- **92.7%**: Executives identifying data quality as a significant barrier to AI success, per NewVantage Partners research cited by Ataccama.

## Context
The article synthesizes findings from multiple research sources—including RAND Corporation, Vanson Bourne, Cisco, and NewVantage Partners—to diagnose why AI initiatives fail: misaligned goals, poor data quality, overwhelming complexity, and weak leadership. Kamil Banc, a practitioner writing for the AI Adopters audience, translates these failure points into the SCALED Framework (Simplify, Confident, Automate, Lead, Evaluate, Digital-Ready), pairing each pillar with action steps, templates, and case studies from companies like Siemens and IBM. The framework is operationalized through a 30-day action plan that moves readers from process simplification to measurement. While the framework itself represents the author's practitioner synthesis rather than peer-reviewed methodology, it is explicitly grounded in the cited research and aimed at professionals seeking to become organizational AI champions.

## Source
- Original: [Why 85% of AI Projects Fail](https://aiadopters.club/p/scaled-framework)
- Cite: kbanc.com/claims-library/why-85-percent-of-ai-projects-fail

## Primary Evidence
- [Tom's Hardware](https://www.tomshardware.com/tech-industry/artificial-intelligence/research-shows-more-than-80-of-ai-projects-fail-wasting-billions-of-dollars-in-capital-and-resources-report) (tomshardware.com; supports claim 1)
- [SPR research](https://spr.com/the-importance-of-leadership-in-it-project-management/) (spr.com; supports claim 2)
- [Ataccama](https://www.ataccama.com/blog/ai-readiness/) (ataccama.com; supports claim 3)
- [Neuroject](https://neuroject.com/ai-in-project-management-case-studies/) (neuroject.com; supports claim 4)