Category: AI Economics

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

AI Economics: Annotation Economics — Crowdsourcing vs Expert Labeling

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18625150  64stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Data annotation represents one of the most underestimated cost centers in enterprise AI development. While organizations meticulously budget for infrastructure, talent, and model training, annotation costs frequently emerge as budget-breaking surprises that derail otherwise promising AI initiatives. In my fourteen years of software development and seven years of AI research, I have observed ann...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18625150 64stabilfr·wdophcgmx
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[s]Reviewed Sources45%○≥80% from editorially reviewed sources
[t]Trusted97%✓≥80% from verified, high-quality sources
[a]DOI72%○≥80% have a Digital Object Identifier
[b]CrossRef66%○≥80% indexed in CrossRef
[i]Indexed34%○≥80% have metadata indexed
[l]Academic52%○≥80% from journals/conferences/preprints
[f]Free Access34%○≥80% are freely accessible
[r]References29 refs✓Minimum 10 references required
[w]Words [REQ]3,522✓Minimum 2,000 words for a full research article. Current: 3,522
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18625150
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]5%✗≥80% 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]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Quality Economics — The True Cost of Bad Data in Enterprise AI

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18624306  60stabilfr·wdophcgmx
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[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed76%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
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[w]Words [REQ]4,438✓Minimum 2,000 words for a full research article. Current: 4,438
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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]17%✗≥80% of references from 2025–2026. Current: 17%
[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 (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Lead Engineer, a leading technology consultancy | PhD Researcher, Odessa Polytechnic National University

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18624306 60stabilfr·wdophcgmx
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[s]Reviewed Sources18%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI41%○≥80% have a Digital Object Identifier
[b]CrossRef29%○≥80% indexed in CrossRef
[i]Indexed76%○≥80% have metadata indexed
[l]Academic24%○≥80% from journals/conferences/preprints
[f]Free Access71%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
[w]Words [REQ]4,438✓Minimum 2,000 words for a full research article. Current: 4,438
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18624306
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]17%✗≥80% of references from 2025–2026. Current: 17%
[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 (66 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Acquisition Costs and Strategies — The First Economic Gatekeeper of Enterprise AI

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18623221  57stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,791✓Minimum 2,000 words for a full research article. Current: 2,791
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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]50%✗≥80% of references from 2025–2026. Current: 50%
[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 (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). AI Economics: Data Acquisition Costs and Strategies — The First Economic Gatekeeper of Enterprise AI. AI Economics Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18623221 Abstract Data acquisition represents the foundational economic challenge of enterprise AI implementation, often consuming 40-80% of total project budgets before a sin...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18623221 57stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI17%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed100%✓≥80% have metadata indexed
[l]Academic8%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]2,791✓Minimum 2,000 words for a full research article. Current: 2,791
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18623221
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]50%✗≥80% of references from 2025–2026. Current: 50%
[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 (61 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Open Source vs Commercial AI — The Strategic Economics of Build Freedom

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18622040  61stabilfr·wdophcgmx
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[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed69%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access88%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]3,813✓Minimum 2,000 words for a full research article. Current: 3,813
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18622040
[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]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Academic Citation: Ivchenko, O. (2026). AI Economics: Open Source vs Commercial AI — The Strategic Economics of Build Freedom. AI Economics Series. Odesa National Polytechnic University. DOI: 10.5281/zenodo.18622040 Abstract The choice between open source and commercial AI solutions represents one of the most consequential economic decisions enterprise leaders face today [1]. This paper provide...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18622040 61stabilfr·wdophcgmx
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[s]Reviewed Sources13%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI50%○≥80% have a Digital Object Identifier
[b]CrossRef13%○≥80% indexed in CrossRef
[i]Indexed69%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access88%✓≥80% are freely accessible
[r]References16 refs✓Minimum 10 references required
[w]Words [REQ]3,813✓Minimum 2,000 words for a full research article. Current: 3,813
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18622040
[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]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (67 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Vendor Lock-in Economics — The Hidden Cost of AI Platform Dependency

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18620726  57stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI31%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,969✓Minimum 2,000 words for a full research article. Current: 3,969
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18620726
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥80% of references from 2025–2026. Current: 33%
[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%)

Vendor lock-in represents one of the most underestimated economic risks in enterprise AI adoption, with switching costs typically ranging from 2.3x to 5.7x the original implementation investment.

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18620726 57stabilfr·wdophcgmx
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[s]Reviewed Sources8%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI31%○≥80% have a Digital Object Identifier
[b]CrossRef8%○≥80% indexed in CrossRef
[i]Indexed77%○≥80% have metadata indexed
[l]Academic15%○≥80% from journals/conferences/preprints
[f]Free Access92%✓≥80% are freely accessible
[r]References13 refs✓Minimum 10 references required
[w]Words [REQ]3,969✓Minimum 2,000 words for a full research article. Current: 3,969
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18620726
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]33%✗≥80% of references from 2025–2026. Current: 33%
[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: AI Talent Economics — Build vs Buy vs Partner

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18619213  54stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed58%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,342✓Minimum 2,000 words for a full research article. Current: 3,342
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18619213
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
[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 (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

*Scarcity Index: Composite score (1-10) based on demand/supply ratio, salary growth, and time-to-fill

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18619213 54stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted92%✓≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
[b]CrossRef25%○≥80% indexed in CrossRef
[i]Indexed58%○≥80% have metadata indexed
[l]Academic17%○≥80% from journals/conferences/preprints
[f]Free Access67%○≥80% are freely accessible
[r]References12 refs✓Minimum 10 references required
[w]Words [REQ]3,342✓Minimum 2,000 words for a full research article. Current: 3,342
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18619213
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]40%✗≥80% of references from 2025–2026. Current: 40%
[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 (56 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Hidden Costs of AI Implementation — The Expenses Organizations Discover Too Late

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AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18617979  67stabilfr·wdophcgmx
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[s]Reviewed Sources55%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI65%○≥80% have a Digital Object Identifier
[b]CrossRef55%○≥80% indexed in CrossRef
[i]Indexed60%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,488✓Minimum 2,000 words for a full research article. Current: 3,488
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18617979
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (77 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI implementations routinely exceed initial budgets by 40-75%, a pattern I have observed repeatedly across my 14 years in software engineering and 7 years specializing in AI systems at a leading technology consultancy. While organizations meticulously plan for obvious expenses such as infrastructure, licensing, and talent acquisition, they consistently underestimate or completely ove...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18617979 67stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources55%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI65%○≥80% have a Digital Object Identifier
[b]CrossRef55%○≥80% indexed in CrossRef
[i]Indexed60%○≥80% have metadata indexed
[l]Academic55%○≥80% from journals/conferences/preprints
[f]Free Access50%○≥80% are freely accessible
[r]References20 refs✓Minimum 10 references required
[w]Words [REQ]3,488✓Minimum 2,000 words for a full research article. Current: 3,488
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18617979
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
[p]Peer Reviewed [REQ]—✗Peer reviewed by an assigned reviewer
[h]Freshness [REQ]8%✗≥80% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (77 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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  60stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources20%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,675✓Minimum 2,000 words for a full research article. Current: 3,675
[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]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]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

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 60stabilfr·wdophcgmx
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[s]Reviewed Sources20%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI60%○≥80% have a Digital Object Identifier
[b]CrossRef27%○≥80% indexed in CrossRef
[i]Indexed47%○≥80% have metadata indexed
[l]Academic20%○≥80% from journals/conferences/preprints
[f]Free Access47%○≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,675✓Minimum 2,000 words for a full research article. Current: 3,675
[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]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]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (65 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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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  66stabilfr·wdophcgmx
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[s]Reviewed Sources41%○≥80% from editorially reviewed sources
[t]Trusted100%✓≥80% from verified, high-quality sources
[a]DOI65%○≥80% have a Digital Object Identifier
[b]CrossRef35%○≥80% indexed in CrossRef
[i]Indexed59%○≥80% have metadata indexed
[l]Academic59%○≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
[r]References17 refs✓Minimum 10 references required
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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 66stabilfr·wdophcgmx
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18616503
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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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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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
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[s]Reviewed Sources30%○≥80% from editorially reviewed sources
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18616115
[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[h]Freshness [REQ]5%✗≥80% of references from 2025–2026. Current: 5%
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[g]Code—○Source code available on GitHub
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