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Author: Iryna Ivchenko

Enterprise AI Risk: The 80-95% Failure Rate Problem — Introduction

Posted on February 11, 2026March 8, 2026 by Iryna Ivchenko
AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665630  62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,127✓Minimum 2,000 words for a full research article. Current: 3,127
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise artificial intelligence initiatives fail at rates between 80% and 95%—a staggering statistic that dwarfs failure rates in traditional software development. Despite billions in investment, most AI projects never reach production, and those that do often fail to deliver promised business value. This failure epidemic is not primarily caused by limitations in machine learning algorithms ...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665630 62stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources48%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI52%○≥80% have a Digital Object Identifier
[b]CrossRef48%○≥80% indexed in CrossRef
[i]Indexed56%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access41%○≥80% are freely accessible
[r]References27 refs✓Minimum 10 references required
[w]Words [REQ]3,127✓Minimum 2,000 words for a full research article. Current: 3,127
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665630
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]0%✗≥80% of references from 2025–2026. Current: 0%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (69 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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