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Cost-Effective AI: Build vs Buy vs Hybrid — Strategic Decision Framework for AI Capabilities

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626731  58stabilfr·wdophcgmx
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The build-versus-buy decision for AI capabilities requires strategic sophistication beyond traditional IT procurement—a portfolio approach combining internal development, commercial solutions, and hybrid configurations.

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18626731 58stabilfr·wdophcgmx
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[h]Freshness [REQ]13%✗≥60% of references from 2025–2026. Current: 13%
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Score = Ref Trust (62 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Data Poisoning — Economic Impact and Prevention

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626697  64stabilfr·wdophcgmx
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Data poisoning represents one of the most insidious and economically devastating threats to enterprise AI systems. Unlike traditional cybersecurity attacks that target infrastructure, data poisoning corrupts the fundamental l[REDACTED]g process of machine l[REDACTED]g models, leading to systematic failures that may remain undetected for months or years. In my experience at Enterprise AI Divisio...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18626697 64stabilfr·wdophcgmx
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Score = Ref Trust (72 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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The Enterprise AI Landscape — Understanding the Cost-Value Equation

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18625628  46stabilfr·wdophcgmx
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Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)

Enterprise AI spending reached $154 billion globally in 2025, yet 73% of organizations report difficulty extracting measurable business value from their AI investments [1]. This disconnect between investment and return represents the central challenge of our generation's most transformative technology. In my fourteen years building enterprise systems and seven years researching AI economics at ...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18625628 46stabilfr·wdophcgmx
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[t]Trusted53%○≥80% from verified, high-quality sources
[a]DOI34%○≥80% have a Digital Object Identifier
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[l]Academic37%○≥80% from journals/conferences/preprints
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18625628
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[h]Freshness [REQ]12%✗≥60% of references from 2025–2026. Current: 12%
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Score = Ref Trust (42 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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AI Economics: Annotation Economics — Crowdsourcing vs Expert Labeling

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18625150  63stabilfr·wdophcgmx
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (70 × 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 63stabilfr·wdophcgmx
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[t]Trusted90%✓≥80% from verified, high-quality sources
[a]DOI68%○≥80% have a Digital Object Identifier
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[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: Data Quality Economics — The True Cost of Bad Data in Enterprise AI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18624306  58stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 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 58stabilfr·wdophcgmx
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[t]Trusted89%✓≥80% from verified, high-quality sources
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[o]ORCID [REQ]✓✓Author ORCID verified for academic identity
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (63 × 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
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18623221  53stabilfr·wdophcgmx
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[f]Free Access93%✓≥80% are freely accessible
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[w]Words [REQ]2,823✓Minimum 2,000 words for a full research article. Current: 2,823
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[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (54 × 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 53stabilfr·wdophcgmx
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[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI14%○≥80% have a Digital Object Identifier
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[r]References14 refs✓Minimum 10 references required
[w]Words [REQ]2,823✓Minimum 2,000 words for a full research article. Current: 2,823
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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]20%✗≥60% of references from 2025–2026. Current: 20%
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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 (54 × 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  58stabilfr·wdophcgmx
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[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
[i]Indexed67%○≥80% have metadata indexed
[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]3,815✓Minimum 2,000 words for a full research article. Current: 3,815
[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]8%✗≥60% of references from 2025–2026. Current: 8%
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[m]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (62 × 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 58stabilfr·wdophcgmx
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[s]Reviewed Sources11%○≥80% from editorially reviewed sources
[t]Trusted83%✓≥80% from verified, high-quality sources
[a]DOI44%○≥80% have a Digital Object Identifier
[b]CrossRef11%○≥80% indexed in CrossRef
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[l]Academic44%○≥80% from journals/conferences/preprints
[f]Free Access89%✓≥80% are freely accessible
[r]References18 refs✓Minimum 10 references required
[w]Words [REQ]3,815✓Minimum 2,000 words for a full research article. Current: 3,815
[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]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]Diagrams9✓Mermaid architecture/flow diagrams. Current: 9
[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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AI Economics: Vendor Lock-in Economics — The Hidden Cost of AI Platform Dependency

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18620726  56stabilfr·wdophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI27%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,997✓Minimum 2,000 words for a full research article. Current: 3,997
[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]17%✗≥60% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 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 56stabilfr·wdophcgmx
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[s]Reviewed Sources7%○≥80% from editorially reviewed sources
[t]Trusted87%✓≥80% from verified, high-quality sources
[a]DOI27%○≥80% have a Digital Object Identifier
[b]CrossRef7%○≥80% indexed in CrossRef
[i]Indexed80%✓≥80% have metadata indexed
[l]Academic27%○≥80% from journals/conferences/preprints
[f]Free Access93%✓≥80% are freely accessible
[r]References15 refs✓Minimum 10 references required
[w]Words [REQ]3,997✓Minimum 2,000 words for a full research article. Current: 3,997
[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]17%✗≥60% 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]Diagrams5✓Mermaid architecture/flow diagrams. Current: 5
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (59 × 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
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18619213  53stabilfr·wdophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted86%✓≥80% from verified, high-quality sources
[a]DOI29%○≥80% have a Digital Object Identifier
[b]CrossRef21%○≥80% indexed in CrossRef
[i]Indexed57%○≥80% have metadata indexed
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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18619213 53stabilfr·wdophcgmx
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The Black Swan Problem: Why Traditional AI Fails at Prediction

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18749477  25stabilfr·wdophcgmx
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Traditional recurrent neural network architectures—including LSTMs and GRUs—exhibit systematic failure modes when confronted with Black Swan events: rare, high-impact occurrences that fall outside the training distribution. This technical analysis quantifies the economic impact of prediction failures, examines the mathematical foundations of why these architectures fail, and introduces the conc...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18749477 25stabilfr·wdophcgmx
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