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  57stabilfr·wdophcgmx
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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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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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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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Data Mining Chapter 5: Supervised Learning Taxonomy — Classification and Regression

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Data Science by Iryna Ivchenko & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626630  72stabilfr·wdophcgmx
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This chapter presents a hierarchical taxonomy of supervised learning methods, organized along three primary dimensions: algorithmic architecture, learning mechanism, and model interpretability. We trace the evolutionary development from early statistical classifiers through decision tree families, neural architectures, kernel methods, and ensemble strategies. Special attention is given to the i...

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Data Science by Iryna Ivchenko & Oleh Ivchenko DOI: 10.5281/zenodo.18626630 72stabilfr·wdophcgmx
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Anticipatory Intelligence: Anticipatory vs Reactive Systems — A Comparative Framework

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko  ·  DOI: 10.5281/zenodo.18626628  49stabilfr·wdophcgmx
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By Dmytro Grybeniuk, AI Architect | Anticipatory Intelligence Specialist | Stabilarity Hub | February 12, 2026

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Academic Research by Dmytro Grybeniuk & Oleh Ivchenko DOI: 10.5281/zenodo.18626628 49stabilfr·wdophcgmx
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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  57stabilfr·wdophcgmx
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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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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  54stabilfr·wdophcgmx
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*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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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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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
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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
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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 60stabilfr·wdophcgmx
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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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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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Score = Ref Trust (71 × 60%) + Required (3/5 × 30%) + Optional (1/4 × 10%)
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