Category: AI Economics

AI Economics: Risk, Cost, and ROI Research by Oleh Ivchenko

AI Economics: Risk Profiles — Narrow vs General-Purpose AI Systems

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665626  57stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI systems exhibit fundamentally different risk profiles depending on their architectural paradigm. This paper presents a comprehensive economic analysis comparing narrow AI systems—purpose-built for specific tasks—with general-purpose AI (GPAI) systems, particularly large language models and foundation models that have proliferated since 2022. Drawing from 14 years of enterprise sof...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665626 57stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI25%○≥80% have a Digital Object Identifier
[b]CrossRef18%○≥80% indexed in CrossRef
[i]Indexed71%○≥80% have metadata indexed
[l]Academic46%○≥80% from journals/conferences/preprints
[f]Free Access68%○≥80% are freely accessible
[r]References28 refs✓Minimum 10 references required
[w]Words [REQ]3,660✓Minimum 2,000 words for a full research article. Current: 3,660
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665626
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]11%✗≥80% of references from 2025–2026. Current: 11%
[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 (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Structural Differences — Traditional vs AI Software

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665628  34stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,575✓Minimum 2,000 words for a full research article. Current: 3,575
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665628
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

In March 2022, a senior architect at a Fortune 500 financial services firm stood before his team with a troubling admission. His organization had spent $47 million over three years building what they called "the most sophisticated fraud detection system in the industry." The system worked—brilliantly, in fact—catching 23% more fraudulent transactions than their previous rule-based approach. But...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665628 34stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted45%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
[b]CrossRef0%○≥80% indexed in CrossRef
[i]Indexed23%○≥80% have metadata indexed
[l]Academic0%○≥80% from journals/conferences/preprints
[f]Free Access36%○≥80% are freely accessible
[r]References22 refs✓Minimum 10 references required
[w]Words [REQ]3,575✓Minimum 2,000 words for a full research article. Current: 3,575
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18665628
[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 (23 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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Enterprise AI Risk: The 80-95% Failure Rate Problem — Introduction

Posted on 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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