Cloud vs On-Premise Economics for AI: A Structured Cost Framework for Enterprise Decision-Making

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18678386  62stabilfr·wuedophcgmx
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The deployment of artificial intelligence workloads involves one of the most consequential infrastructure decisions in modern enterprise technology strategy: whether to run AI systems in the cloud, on-premise, or across a hybrid topology. This decision is rarely reducible to a simple cost comparison — it involves hidden cost structures, risk transfer, organizational capability requirements, and...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18678386 62stabilfr·wuedophcgmx
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The Cognitive Shift: A Creative Vision of How AI Will Change the Way We Think and Perceive

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Journal Commentary
Journal Commentary by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18685239  69stabilfr·wuedophcgmx
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Artificial intelligence is not primarily a threat to human labour — it is a repricing of human cognition. Drawing on Jürgen Schmidhuber's formal theory of intelligence as compression, Robert Sheckley's satirical science fiction, and Isaac Asimov's prescient design specifications for autonomous systems, this essay argues that AI is catalysing the most significant cognitive economy shift since th...

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Journal Commentary by Oleh Ivchenko DOI: 10.5281/zenodo.18685239 69stabilfr·wuedophcgmx
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The ROI Timeline — Realistic Expectations for Enterprise AI Projects

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Applied Research
Applied Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672405  42stabilfr·wuedophcgmx
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The single most damaging piece of misinformation in enterprise AI is the promise of rapid return. Vendor decks routinely project ROI within 6-12 months; the empirical reality is 18-36 months for most use cases, with a mandatory investment trough in between. Drawing on 52 enterprise AI deployments analyzed or directly managed between 2021 and 2025, alongside published data from McKinsey, Gartner...

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Applied Research by Oleh Ivchenko DOI: 10.5281/zenodo.18672405 42stabilfr·wuedophcgmx
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Chapter 9: Clustering and Segmentation — Grouping Strategies in Data Mining

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672455  80stabilfr·wuedophcgmx
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Clustering stands at the heart of unsupervised data mining — it is the art of asking a machine to look at raw, unlabeled data and answer a deceptively simple question: what belongs together? This chapter offers a comprehensive taxonomy of clustering and segmentation strategies, tracing the intellectual lineage of each major family of algorithms and exposing the often-overlooked gaps in how thes...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18672455 80stabilfr·wuedophcgmx
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AI Economics: MLOps Infrastructure Costs — The Hidden Price of Production AI

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672439  55stabilfr·wuedophcgmx
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Machine learning operations (MLOps) infrastructure has become the defining cost center for enterprise AI programs, yet it remains systematically underestimated in project planning and ROI calculations. This research presents a comprehensive economic analysis of MLOps infrastructure costs across the full production AI lifecycle — from continuous integration pipelines and feature stores through m...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18672439 55stabilfr·wuedophcgmx
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Gap Analysis: Real-Time Adaptation to Distribution Shift

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18672412  63stabilfr·wuedophcgmx
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Score = Ref Trust (50 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Distribution shift — the statistical divergence between the data a model trained on and the data it encounters in production — is the quiet destroyer of AI reliability. Unlike model bugs or data quality failures that manifest acutely, distribution shift degrades performance gradually, silently, until the system is making decisions optimized for a world that no longer exists. For anticipatory AI...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18672412 63stabilfr·wuedophcgmx
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The Spec-First Revolution: Why Enterprise AI Needs Formal Specifications

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Academic Research by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666032  80stabilfr·wuedophcgmx
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[s]Reviewed Sources77%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
[i]Indexed11%○≥80% have metadata indexed
[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References88 refs✓Minimum 10 references required
[w]Words [REQ]5,791✓Minimum 1,000 words of article body. Current: 5,791
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
[e]Evidence [REQ]91✓Cites at least 1 external source(s): links, DOIs or a reference list. Current: 91
[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18666032
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[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 (78 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

timeline title Evolution of Software Specification Practices 1950s-1960s : Ad-hoc specifications : Natural language : Manual testing 1970s-1980s : Formal methods : Hoare logic, VDM, Z notation : Mathematical proofs 1990s : Design-by-contract : Preconditions, postconditions : Eiffel, JML 2000s...

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Academic Research by Oleh Ivchenko DOI: 10.5281/zenodo.18666032 80stabilfr·wuedophcgmx
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[s]Reviewed Sources77%○≥80% from editorially reviewed sources
[t]Trusted94%✓≥80% from verified, high-quality sources
[a]DOI84%✓≥80% have a Digital Object Identifier
[b]CrossRef70%○≥80% indexed in CrossRef
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[l]Academic93%✓≥80% from journals/conferences/preprints
[f]Free Access18%○≥80% are freely accessible
[r]References88 refs✓Minimum 10 references required
[w]Words [REQ]5,791✓Minimum 1,000 words of article body. Current: 5,791
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18666032
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness1%○≥60% of references from 2025–2026. Current: 1%
[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 (78 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains

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Academic Research
Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662985  78stabilfr·wuedophcgmx
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[s]Reviewed Sources38%○≥80% from editorially reviewed sources
[t]Trusted91%✓≥80% from verified, high-quality sources
[a]DOI91%✓≥80% have a Digital Object Identifier
[b]CrossRef56%○≥80% indexed in CrossRef
[i]Indexed9%○≥80% have metadata indexed
[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]5,141✓Minimum 1,000 words of article body. Current: 5,141
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Score = Ref Trust (75 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Academic Citation: Dmytro Grybeniuk & Oleh Ivchenko. (2026). Gap Analysis: Explainability-Accuracy Tradeoff in High-Stakes Domains. Anticipatory Intelligence Series. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18662985 Abstract The explainability-accuracy tradeoff represents one of the most economically consequential yet technically intractable gaps in anticipatory AI syste...

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18662985 78stabilfr·wuedophcgmx
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[t]Trusted91%✓≥80% from verified, high-quality sources
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[l]Academic91%✓≥80% from journals/conferences/preprints
[f]Free Access56%○≥80% are freely accessible
[r]References32 refs✓Minimum 10 references required
[w]Words [REQ]5,141✓Minimum 1,000 words of article body. Current: 5,141
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[g]Code—○Source code available on GitHub
[m]Diagrams7✓Mermaid architecture/flow diagrams. Current: 7
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Score = Ref Trust (75 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Chapter 8: Sequential Pattern Mining — Temporal Discoveries

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Data Science
Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18666030  52stabilfr·wuedophcgmx
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[a]DOI33%○≥80% have a Digital Object Identifier
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Score = Ref Trust (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

Academic Citation: Iryna Ivchenko & Oleh Ivchenko. (2026). Chapter 8: Sequential Pattern Mining — Temporal Discoveries. Intellectual Data Analysis Series, Chapter 8. Odessa National Polytechnic University. DOI: 10.5281/zenodo.18666030 Abstract Sequential pattern mining represents one of the most intellectually compelling challenges in data mining: discovering meaningful patterns hidden with...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18666030 52stabilfr·wuedophcgmx
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[s]Reviewed Sources0%○≥80% from editorially reviewed sources
[t]Trusted33%○≥80% from verified, high-quality sources
[a]DOI33%○≥80% have a Digital Object Identifier
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[l]Academic33%○≥80% from journals/conferences/preprints
[f]Free Access100%✓≥80% are freely accessible
[r]References3 refs○Minimum 10 references required
[w]Words [REQ]5,093✓Minimum 1,000 words of article body. Current: 5,093
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[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
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[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 (32 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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Federated Learning Economics: Privacy vs Efficiency

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AI Economics
AI Economics by Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18662973  79stabilfr·wuedophcgmx
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[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI95%✓≥80% have a Digital Object Identifier
[b]CrossRef46%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
[r]References41 refs✓Minimum 10 references required
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18662973
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[m]Diagrams6✓Mermaid architecture/flow diagrams. Current: 6
[x]Cited by0○Referenced by 0 other hub article(s)
Score = Ref Trust (77 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)

After seven years of implementing AI systems across healthcare, finance, and enterprise domains, I've observed a fundamental tension in modern machine learning: organizations need data to build effective models, but privacy regulations, competitive concerns, and ethical considerations prevent centralized data collection. Federated learning promises to resolve this paradox by training models acr...

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AI Economics by Oleh Ivchenko DOI: 10.5281/zenodo.18662973 79stabilfr·wuedophcgmx
BadgeMetricValueStatusDescription
[s]Reviewed Sources44%○≥80% from editorially reviewed sources
[t]Trusted95%✓≥80% from verified, high-quality sources
[a]DOI95%✓≥80% have a Digital Object Identifier
[b]CrossRef46%○≥80% indexed in CrossRef
[i]Indexed5%○≥80% have metadata indexed
[l]Academic95%✓≥80% from journals/conferences/preprints
[f]Free Access59%○≥80% are freely accessible
[r]References41 refs✓Minimum 10 references required
[w]Words [REQ]4,744✓Minimum 1,000 words of article body. Current: 4,744
[u]Original [REQ]100%✓≥85% unique sentences (no padding or repeated blocks) and no leaked drafting notes. Current: 100% unique
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[d]DOI [REQ]✓✓Zenodo DOI registered for persistent citation. DOI: 10.5281/zenodo.18662973
[o]ORCID✓✓Author ORCID verified for academic identity
[p]Peer Reviewed—○Peer reviewed by an assigned reviewer
[h]Freshness2%○≥60% of references from 2025–2026. Current: 2%
[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 (77 × 60%) + Required (4/4 × 30%) + Optional (2/7 × 10%)
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