Category: AI Economics

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

AI Economics: ROI Calculation Methodologies for Enterprise AI — From Traditional Metrics to AI-Specific Frameworks

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18617078  57stabilfr·wdophcgmx
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Return on Investment (ROI) calculation for artificial intelligence projects presents unique methodological challenges that traditional IT investment frameworks fail to adequately address [2]. Drawing from fourteen years in enterprise software development and seven years of AI research, this article presents a comprehensive analysis of ROI calculation methodologies specifically designed for ente...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18617078 57stabilfr·wdophcgmx
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[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted88%✓≥80% from verified, high-quality sources
[a]DOI53%○≥80% have a Digital Object Identifier
[b]CrossRef24%○≥80% indexed in CrossRef
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[l]Academic35%○≥80% from journals/conferences/preprints
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[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]3,691✓Minimum 2,000 words for a full research article. Current: 3,691
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18617078
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥60% of references from 2025–2026. Current: 8%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
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AI Economics: TCO Models for Enterprise AI — A Practitioner’s Framework

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18616503  63stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Total Cost of Ownership (TCO) analysis for enterprise AI systems presents unique challenges that traditional IT TCO frameworks fail to address adequately. This paper presents a comprehensive TCO model specifically designed for AI implementations, drawing on my fourteen years of enterprise software experience and seven years of AI research at a leading technology consultancy. I propose a four-ph...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18616503 63stabilfr·wdophcgmx
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[s]Reviewed Sources37%○≥80% from editorially reviewed sources
[t]Trusted89%✓≥80% from verified, high-quality sources
[a]DOI58%○≥80% have a Digital Object Identifier
[b]CrossRef37%○≥80% indexed in CrossRef
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[r]References19 refs✓Minimum 10 references required
[w]Words [REQ]3,515✓Minimum 2,000 words for a full research article. Current: 3,515
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18616503
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]7%✗≥60% of references from 2025–2026. Current: 7%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Economic Framework for AI Investment Decisions

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18616115  63stabilfr·wdophcgmx
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[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI66%○≥80% have a Digital Object Identifier
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[w]Words [REQ]4,212✓Minimum 2,000 words for a full research article. Current: 4,212
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise artificial intelligence investments present unique economic challenges that traditional capital budgeting frameworks fail to adequately address. This article develops a comprehensive economic framework specifically designed for AI investment decisions, integrating uncertainty quantification, option value analysis, and dynamic portfolio optimization. Drawing from fourteen years of sof...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18616115 63stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources28%○≥80% from editorially reviewed sources
[t]Trusted93%✓≥80% from verified, high-quality sources
[a]DOI66%○≥80% have a Digital Object Identifier
[b]CrossRef62%○≥80% indexed in CrossRef
[i]Indexed41%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access55%○≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]4,212✓Minimum 2,000 words for a full research article. Current: 4,212
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18616115
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥60% of references from 2025–2026. Current: 5%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
[g]Code—○Source code available on GitHub
[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Risk Profiles — Narrow vs General-Purpose AI Systems

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665626  55stabilfr·wdophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
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[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access70%○≥80% are freely accessible
[r]References30 refs✓Minimum 10 references required
[w]Words [REQ]3,682✓Minimum 2,000 words for a full research article. Current: 3,682
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥60% of references from 2025–2026. Current: 9%
[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 (58 × 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 55stabilfr·wdophcgmx
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[s]Reviewed Sources17%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI23%○≥80% have a Digital Object Identifier
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]9%✗≥60% of references from 2025–2026. Current: 9%
[c]Data Charts0○Original data charts from reproducible analysis (min 2). Current: 0
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (58 × 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 by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665628  34stabilfr·wdophcgmx
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[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
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[t]Trusted46%○≥80% from verified, high-quality sources
[a]DOI0%○≥80% have a Digital Object Identifier
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[l]Academic0%○≥80% from journals/conferences/preprints
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[w]Words [REQ]3,600✓Minimum 2,000 words for a full research article. Current: 3,600
[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%✗≥60% 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

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18665630  58stabilfr·wdophcgmx
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[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI47%○≥80% have a Digital Object Identifier
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[l]Academic47%○≥80% from journals/conferences/preprints
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[r]References30 refs✓Minimum 10 references required
[w]Words [REQ]3,161✓Minimum 2,000 words for a full research article. Current: 3,161
[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%✗≥60% 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 (62 × 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 l[REDACTED]g algorit...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18665630 58stabilfr·wdophcgmx
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[s]Reviewed Sources43%○≥80% from editorially reviewed sources
[t]Trusted80%✓≥80% from verified, high-quality sources
[a]DOI47%○≥80% have a Digital Object Identifier
[b]CrossRef43%○≥80% indexed in CrossRef
[i]Indexed50%○≥80% have metadata indexed
[l]Academic47%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References30 refs✓Minimum 10 references required
[w]Words [REQ]3,161✓Minimum 2,000 words for a full research article. Current: 3,161
[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%✗≥60% 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 (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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